Population events such as natural disasters, pandemics, extreme weather, and wars might cause jumps that have an immediate impact on mortality rates. The recent COVID-19 pandemic has demonstrated that these events should not be treated as nonrepetitive exogenous interventions. Therefore, mortality models incorporating jump effects are particularly important to capture the adverse mortality shocks. The mortality models with jumps, proposed by Cox. et al. , Chen and Cox , and Özen and Şahin , which we consider in this study, differ in terms of the duration of the jumps - transitory or permanent, frequency of the jumps, and size of the jumps. To illustrate the effect of jumps, we also consider benchmark mortality models without jump effects, such as the Lee-Carter , Renshaw and Haberman and the Cairns-Blake-Dowd models. We discuss the performance of all the models by analysing their ability to capture the mortality deterioration caused by COVID-19. We use data from different countries to simulate the mortality rates for the pandemic years and examine their accuracy in forecasting the mortality jumps due to the pandemic. Moreover, we also analyse the impact of mortality jump models on catastrophe bond pricing.

Distributed digital manufacturing offers a solution to medical supply and technology shortages during pandemics. To prepare for the next pandemic, this study reviews the state-of-the-art for open hardware designs needed in a COVID-19-like pandemic. It evaluates the readiness of the top twenty technologies requested by the Government of India. The results show that the majority of the actual medical products have had some open source development, however, only 15% of the supporting technologies that make the open source device possible are freely available. The results show there is still considerable work needed to provide open source paths for the development of all the medical hardware needed during pandemics. Five core areas of future work are discussed that include: i) technical development of a wide-range of open source solutions for all medical supplies and devices, ii) policies that protect the productivity of laboratories, makerspaces and fabrication facilities during a pandemic, as well as iii) streamlining the regulatory process, iv) developing Good-Samaritan laws to protect makers and designers of open medical hardware, as well as to compel those with knowledge that will save lives to share it, and v) requiring all citizen-funded research to be released with free and open source licenses.

COVID-19 has been a major issue in most countries throughout the world with 213 countries being affected till date due to the disease. The pandemic has raised concerns over the healthcare facilities available in various countries and question the government decisions made during this period of outbreak. Despite having the best healthcare facilities several countries across Europe and America have found it difficult to contain the disease outbreak questioning the available solutions to contain an area. This paper focuses on presenting information on solutions available to control outbreaks in order to prevent another pandemic occurring in the future. The paper also highlights the strategies and plans implemented by various governments who have been successful in combatting the disease with minimum damage. By using available resources such as technology, scientific innovation and digitalized healthcare this paper focuses on providing solutions which are already available to be utilized in the right manner.

Two conundrums have puzzled COVID-19 investigators: 1) morbidity and mortality is rare among Infants and young children and 2) rates of morbidity and mortality exhibit very large variances across nations, locals and even within cities. These differences correlate with rates of Haemophilus influenzae type B (Hib) and pneumococcal vaccination, which are almost universal among infants and vary widely by geography among adults and the elderly. The higher the rate of vaccination, the lower the COVID-19 morbidity and mortality. Vaccination rates with other vaccines, including BCG and poliovirus, do not correlate with COVID-19 risks. Notably, both Hib and pneumoccoci are common co-infections with influenza and coronaviruses and are associated with more severe disease and risk of death. Whether the vaccines simply protect against COVID-19 complications, directly protect against COVID-19 infection by inducing cross-reactive immunity, or are markers for some other types of protection such as availability of better healthcare, is not yet known. What is known is that improving coverage rates of Hib and pneumococcal vaccination has significantly lowered severe morbidity and mortality in influenza epidemics and might have similar efficacy for mitigating coronavirus outbreaks. If infants and children are valid indicators, the beneficial effects might be very significant. The possibility that anti-viral proteins in milk (e.g., lactoferrin) protect against COVID-19 is also explored.

During the period of health emergency linked to the current COVID-19 pandemic, the management of children's oral health presents specific problems related to the infectious spread of the disease. These problems must be faced on the one hand by acting on the oral health prevention methods, and on the other by implementing specific protocols relating both to the conditions of oral pathologies that normally do not represent an emergency, and to those clinical situations that fall into the category of pediatric dental emergencies. In this perspective, in addition to defining rigorous and highly effective infection control protocols in the dental settings, it is of fundamental importance to work on remote communication and education aimed at maintaining the oral health of the children. This article, after an analysis of the risk factors from COVID-19 associated with pediatric dental treatment, presents a series of considerations on potential oral prevention strategies and on the management of emergency and non-emergency dental procedures in a context of disease transmission control, proposing new approaches and models of treatment based also on remote interaction techniques which will then retain their usefulness even at the end of the current emergency period.

Geoscientists developed geoethics, an intra-disciplinary field of applied philosophical studies, during the last decade. Reaching beyond the sphere of professional geosciences, it led to professional, cultural, and philosophical approaches to handle the social-ecological structures of our planet ‘wherever human activities interact with the Earth system’. Against the backdrop of the COVID-19 and considering geoscientists’ experiences dealing with disasters (related to hazards like tsunamis, floods, climate changes.), this essay (1) explores the geoethical approach, (2) re-casts geoethics within western philosophical systems, such as the Kantian imperatives, Kohlberg scale of moral adequacy, Jonas’ imperative of responsibility, and (3) advances a ‘geoethical thesis’. The latter takes the form of a hypothesis of a much broader scope of geoethics than initially envisioned. That hypothesis appears by suspecting a relationship between the relative successes in the COVID-19 battle with the positioning of agents (individual, collective, institutional) into ethical frameworks. The turmoil caused by the COVID-19 pandemic, calls for the transfer of experiences between different disciplinary domains to further sustainable governance, hence generalising the geoethical approach. It is emphasized that only when behaving as responsible and knowledgeable citizens, then people of any trade (including [geo-]scientists) can transgress the boundaries of ordinary governance practices with legitimacy.

Background: The COVID 19 pandemic and associated public health measures have disrupted the lives of people around the world. It is already evident that the direct and indirect psychological and social effects of the COVID 19 pandemic are insidious and affect the mental health of young children and adolescents now and will in the future. The aim and objectives of this knowledge-synthesis study were to identify the impact of the pandemic on children’s and adolescent’s mental health and to evaluate the effectiveness of different interventions employed during previous and the current pandemic to promote children’s and adolescent’s mental health. Methodology: We conducted the systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and included experimental randomized, nonrandomized controlled trials; observational studies; and qualitative studies. Results: Of the 5,828 articles that we retrieved, 18 articles met the inclusion criteria. We thematically analyzed and put up the major findings under the thematic areas of impact of pandemic on children and adolescent’s mental health. These studies reported that pandemics cause stress, worry, helplessness, and social and risky behavioral problems among children and adolescents (e.g., substance abuse, suicide, relationship problems, academic issues, absenteeism from work). Interventions such as art-based programs, support services, and clinician-led mental health and psychosocial services effectively decrease mental health issues among children and adolescents. Conclusion: Children and adolescents are more likely to experience high rates of depression and anxiety during and after a pandemic. It is critical that future researchers explore effective mental health strategies that are tailored to the needs of children and adolescents. Explorations of effective channels regarding the development and delivery of evidence-based, age-appropriate services are vital to lessen the effects and improve long-term capacities for mental health services for children and adolescents.

Background: Corona virus disease was first reported in Wuhan City, Hubei province, China. Soon the corona virus disease has spread to many countries. World health organization has described the situation as Pandemic. By 12 March 2020, corona virus disease has affected 125 countries around the world. Corona virus was first confirmed in Iran on 19 February 2020. Objective: The aim of writing this small article is to determine the mortality rate of corona virus disease in different cities of Iran. This article will give readers an idea of different ways to control spread of corona virus disease in Iran. Methods: Author has collected the data of daily confirmed cases from different diagnostic centers and hospitals across the country from 19 February 2020 to 1 April 2020. Results: Mortality rate of corona virus disease is roughly 4%. Conclusions: Mortality rate could rise if COVID 19 infection is not controlled.

Background. The Weekly Mortality Cycle among CoViD-19 patients has been studied.Methods. Mortality data obtained from the 'Worldometer' website were analyzed with a comparison of absolute values, percentages, and p-value. Results. For patients suffering from CoViD-19, the most favorable or the safest days of the week were Sundays and Mondays. Conclusion. The weekly cycle with decreased mortality on Sundays and Mondays is a unique phenomenon observed among victims of CoViD-19. Presumably the decreased mortality on certain days of the week related to the optimized therapeutic protocols used on the "safest days".

With the COVID-19 pandemic now ongoing for close to a year, people all over the world are still waiting for a vaccine to become available. The initial focus of accelerated global research and development efforts to bring a vaccine to market as soon as possible was on novel platform technologies that promised speed but had limited history in the clinic. In contrast, recombinant protein vaccines, with numerous examples in the clinic for many years, missed out on the early wave of investments from government and industry. Emerging data are now surfacing suggesting that recombinant protein vaccines indeed might offer an advantage or complement to the nucleic acid or viral vector vaccines that will likely reach the clinic faster. Here, we summarize the current public information on the nature and on the development status of recombinant subunit antigens and adjuvants targeting SARS-CoV-2 infections.

In the context of the pandemic caused by Covid-19, an important aspect that was affected and changed was the way people communicate. Communication efficiency can be improved by using modern technologies, for example: mobile devices, digital audio and video platforms, text platforms, social media and more. For further economic success, digital skills are needed not only for executives, but also for managers, in order to be able to lead teams and avoid situations of stress and tension in the virtual environment. This scientific paper includes a primary research of how the pandemic affected the communication at the workplace. For this a qualitative analysis was first performed, in which the interview was used as a working tool. Next, on the basis of a questionnaire, a hypothesis was formulated and validated afterwards through multiple linear regression. This research highlighted the particularly important role of communication and, in this context, the role of the remote team leader. In order to keep the team motivated and interested in the virtual environment, the manager must use modern tools to motivate, support and express his appreciation when the team, respectively the members of his team, achieve their goals successfully.

One of the most important problems in the presence of epidemics and pandemics is an accurate prediction and preparedness. Scholars and experts argue that future pandemics and/or epidemics are almost inevitable events and is not whether next pandemics will happen, but when a new heath emergency will emerge. Epidemiologic models for prediction of Coronavirus Disease 2019 (COVID-19) have shown many limitations because of unpredictable dynamics of the new viral agent SARS-CoV-2 in environment and society. The main goals of this study are twofold: first, the analysis of anthropogenic activities and factors that may trigger pandemic threats; second, the planning of new directions for strategies to reduce risks that a pandemic threat emerges and/or in the initial phase to reduce vast diffusion and negative impact of new viral agents that can generate hazards and problems in public health, environment and socioeconomic systems. In particular, the investigation and understanding of sources and driving factors concerning the emergence and diffusion of new pandemics have critical aspects for strategic actions of forecast, prevention and preparation of effective policy responses to cope with next pandemic crises and health emergencies. Insights here endeavor, whenever possible, to clarify these problems to increase the knowledge of the sources and factor determining the emergence of new viral agents in order to design optimal response policies to face next pandemic diseases similar to COVID-19. .

The teaching and learning environment have changed significantly over the last decade with the integration of an increasing amount of information and communication technologies, especially in distance education due to global pandemics, total lockdowns, isolation, and quarantine periods. The number of numerous virtual and non-virtual tools involved in educational processes is growing day by day. In terms of technological innovations used in learning environments, many researchers and educators are trying their best to bring more creative ideas into the technology- based learning environments. Although higher educational institutions have greatly benefited from the growing number of hardware and software resources in the educational process, they have also faced some setbacks that have harmed the learning processes. These technologies have in turn complicated some of the processes in the learning atmosphere. Therefore, we take Spanish teaching course as an example to present the challenging smarter teaching and learning systems that higher educational institutions face today.

COVID-19 is the first known coronavirus pandemic. Nevertheless, the seasonal circulation of the four milder coronaviruses of humans – OC43, NL63, 229E and HKU1 – raises the possibility that these viruses are the descendants of more ancient coronavirus pandemics. This proposal arises by analogy to the observed descent of seasonal influenza subtypes H2N2 (now extinct), H3N2 and H1H1 from the pandemic strains of 1957, 1968 and 2009, respectively. Recent historical revisionist speculation has focussed on the influenza pandemic of 1889-1892, based on molecular phylogenetic reconstructions that show the emergence of human coronavirus OC43 around that time, probably by zoonosis from cattle. If the “Russian influenza”, as The Times named it in early 1890, was not influenza but caused by a coronavirus, the origins of the other three milder human coronaviruses may also have left a residue of clinical evidence in the 19th century medical literature and popular press. In this paper, we search digitised 19th century British newspapers for evidence of previously unsuspected coronavirus pandemics. We conclude that there is little or no corpus linguistic signal in the UK national press for large-scale outbreaks of unidentified respiratory disease for the period 1785 to 1890.

The harrowing second wave of COVID-19 in India has led to much discussion over the quality and timeliness of reporting of deaths attributed to the pandemic. In this brief report, we aim to present the existing evidence, as well as the broader complexities surrounding the mortality burden of COVID-19 in India. This article sheds light on the following epidemiological issues: (1) general and India-specific challenges to COVID-19 death reporting, (2) latest COVID-19 mortality estimates in India as of May 16, 2021, (3) the apparent scale of uncaptured COVID-19 deaths, and (4) the role of disaggregated historic mortality trends in quantification of excess deaths attributed to COVID-19. We conclude with a set of high-level policy recommendations for improving the vital surveillance system and tracking of causes of death in India. We encourage direct efforts to integrate health data and indirect strategies for cross-validation of registered deaths. Such system-wide advances would drastically aid epidemiological research efforts and strengthen India’s position to overcome future public health crises.

Background: COVID-19 is currently one of the most important medical challenges as it affects the entire population with children being infected as easily as adults. Objective: The study evaluated the mortality of under 17-year-olds and compare the frequency with that of adults in a cohort of 8986 COVID-19 positive patients hospitalized in a single care center. Design: retrospective clinical trial, all hospitalizations of COVID-19 positive patients as diagnosed by RT-qPCR were analyzed by revisiting their medical records. Setting: 8986 COVID-19 positive patients hospitalized in a Hospital de Base and the Infant and Maternal Hospital of São Jose do Rio Preto, SP, Brazil (HB/HCM) being these totals of 383 COVID-19 positive under 17-patients. Patients: patients COVID-19 positive under 17-year-olds were grouped by age and evaluated by analyzing their medical records. They were categorized as Group I, children, and adolescents under 19 years old and Group II, adults over 19 years. Results: Overall mortality in under 19-year-olds was 12 deaths, that is, 3.13% of the patients in this age group and 0.55% of the overall mortality from COVID-19. Mortality in the group of adults was 2197 equivalent to 24.45% of the hospitalized adults and 99.45% of overall deaths. Mortality was significantly higher for adults than for under 19-year olds (p-value < 0.0001). Regarding the different ages of children and adolescents, the number of deaths of under 1-year olds was 2/123 (1.62%), of 1-4 years it was 4/95 (2.11%), of 5-9 years it was 1/47 (2.13%), of 10-14 years it was 1/40 (2.5%) and of 15-19 years it was 4/78 (5.13%). There were no statistical differences between the different age groups ( p-value = 0.3). Conclusion: Mortality from COVID-19 in children and adolescents was lower than in adults and generally associated with other comorbidities with the main association being with heart disease.

The COVID-19 pandemics has had an unprecedented global impact, and the COVID-19 mass vaccination campaign has been commonly regarded as crucial to overcome the pandemics. Since all-cause mortality is the best way to measure the consequences of a health intervention, the present study was devised to analyze the all-cause mortality data of the United Kingdom (UK), which are made publicly available broken down by vaccination status. Data from January to May 2022 were retrospectively collected and analyzed according to age groups and vaccination status and the relative risk (RR) for all-cause mortality was calculated in comparison to the corresponding unvaccinated groups. All-cause mortality RR was also calculated from January to May 2021 for vaccinated people. Results show that the all-cause mortality RR was higher in people who received one or two doses of COVID-19 vaccines throughout the whole period and in any of the age groups considered. People vaccinated with three doses more than 21 days earlier had RRs lower than unvaccinated people, which however linearly increased over time. RR in vaccinated people of all ages in comparison to unvaccinated people were lower in January-May 2021, however they steadily grew over time. The finding that all-cause mortality RR in vaccinated in comparison to unvaccinated people increases over time requires careful examination to understand the underlying factors. Meanwhile, all the other major countries should undertake a systematic collection of all-causes mortality broken down by vaccination status, and mass vaccination campaigns should be suspended.

The COVID-19 pandemic has caused ~ 2 million fatalities. Significant progress has been made in advancing our understanding of the disease process, one of the unanswered questions, however, is the anomaly in the case/mortality ratio with Mexico as a clear example. Herein, this anomaly is explored by spatial analysis and whether mortality varies locally according to local factors. To address this, hexagonal cartogram maps (hexbin) used to spatially map COVID-19 mortality and visualise association with patient-level data on demographics and pre-existing health conditions. This was further interrogated at local Mexico City level by choropleth mapping. Our data show that the use of hexagonal cartograms is a better approach for spatial mapping of COVID-19 data in Mexico as it addresses bias in area size and population. We report sex/age-related spatial relationship with mortality amongst the Mexican states and a trend between health conditions and mortality at the state level. Within Mexico City, there is a clear south, north divide with higher mortality in the northern municipalities. Deceased patients in these northern municipalities have the highest pre-existing health conditions. Taken together, this study provides an improved presentation of COVID-19 mapping in Mexico and demonstrates spatial divergence of the mortality in Mexico.

This pandemic of COVID-19 is tedious to control. The only lockdown is the way to stop the spread of this infection. Conventional health care is facing a real challenge to operate. Primarily the challenge is to provide health care support for COVID-19 patients with limited resources and continue the health care services like earlier.Perhaps, this challenge is the same but magnitude is different from different geographical locations around the globe. In this article, we presented a Bayesian algorithm with the Code to predict cancer death due to COVID-19. This code is possible to run at different time points and different geographical locations around the world. This code will help us to get the best strategy and shift the treatment option for cancer treatment. The model would provide physicians with an objective tool for counseling and decision making at different hotspots and small areas to implement.

SARS-CoV-2 infections underlie the current Coronavirus disease (COVID-19) pandemic and are causative for a high death toll particularly among elderly subjects and those with comorbidities. Selenium (Se) is an essential trace element of high importance for human health and particularly for a well-balanced immune response. Mortality risk from severe disease like sepsis or polytrauma is inversely related to Se status. We hypothesized that this relation also applies to COVID-19. Serum samples (n=166) from COVID-19 patients (n=33) were collected consecutively and analysed for total Se by X-ray fluorescence and selenoprotein P (SELENOP) by a validated ELISA. Both biomarkers showed the expected strong correlation (r=0.7758, p<0.001), pointing to an insufficient Se status for optimal selenoprotein expression. In comparison to reference data from a European cross sectional analysis (EPIC, n=1915), the patients showed a pronounced deficit in total serum Se (mean±SD, 50.8±15.7 vs. 84.4±23.4 µg/L) and SELENOP (3.0±1.4 vs. 4.3±1.0 mg/L). A Se status below the 2.5th percentile of the reference population, i.e., [Se] < 45.7 µg/L and [SELENOP] < 2.56 mg/L was present in 43.4% and 39.2% of COVID samples, respectively. The Se status was significantly higher in samples from surviving COVID patients as compared to non-survivors (Se; 53.3±16.2 vs. 40.8±8.1 µg/L, SELENOP; 3.3±1.3 vs. 2.1±0.9 mg/L). We conclude that Se status analysis in COVID patients provides diagnostic information. However, causality remains unknown due to the observational nature of this study. Nevertheless, the findings strengthen the notion on a relevant role of Se for COVID convalescence, and support the discussion on adjuvant Se supplementation in severely diseased and Se-deficient patients.

Introduction: Established predictors for COVID-19 related mortalities are diverse, with cytokine release syndrome (CRS), a key intermediator to the case fatalities being dominant and multi-faceted. The impact of these several risk factors on coronavirus mortality have been previously reported in several meta‐analyses limited by small sample sizes and premature data, and CRS not fully being accounted for. The objective of this systematic review and meta-analysis was to evaluate the evidence on the risk of COVID-19 related CRS and mortality with HIV serostatus using published data, and a meta-regression to account for possible covariates. Method: Electronic databases including Google Scholar, Cochrane Library, Web of Sciences (WOS), EMBASE, Medline/PubMed, COVID-19 Research Database, and Scopus, were systematically searched till 30th February, 2022. All human studies were included irrespective of publication date or region. Twenty-two studies with a total of 19,783,097 patients detailing COVID-related mortality and eleven with a total of 2,005,274 were included. To pool the estimate, a random-effects model with risk ration as the effect measure was used. Moreover, publication bias and sensitivity analysis were evaluated followed by meta-regression. The trial was registered (CRD42021264761) on the PROSPERO register. Results: The findings were consistent in stating the contribution of HIV infection for COVID-19 related CRS and mortality. The cumulative COVID-19 related mortality and CRS was 110270 (0.6%) and 48863 (2.4%) with total events of 2010 (3.6%), 108260 (0.5%) and 837(4.6%), 48026 (2.4%) among HIV-positive and negative persons respectively. HIV infection showed an increased risk of COVID-19 related CRS and mortality [RR= 1.48, 95% CI (1.16, 1.88) (P=0.002)] and [RR =1.19, 95% CI (1.02 -1.39) (P=0.00001)] respectively, both with substantial heterogeneity (I² > 80%). The true effects size in 95% of all the comparable populations fell between 0.64 to 2.22 and 0.67 to 3.29 for mortality and CRS respectively. MC studies and COVID-19 mortality with HIV infection showed a significant association [RR = 1.305, 95% CI (1.092 -1.559) (P = 0.003)], similar to studies conducted in America (RR = 1.422, 95% CI 1.233–1.639) and South Africa (RR = 1.123, 95% CI 1.052–1.198). HIV infection showed a risk for ICU admission [(P=0.00001) (I² = 0%)] and mechanical ventilation [(P=0.04) (I² = 0%)] as parameters of CRS. Furthermore, risk of COVID-19 related CRS is influenced by the year a study was conducted (R² = 0.55) and the region (R² = 0.11) same for mortality (R² = 0.60). The variance proportion explained by covariates was significant for CRS (I² = 86.5%, Q = 73.99, df = 10, P = 0.0000) (R² = 0.78) and mortality (I² = 87.5%, Q = 168.02, df = 21, p = 0.0000) (R² = 0.67). Conclusion: Our updated meta-analysis indicated that HIV infection was significantly associated with an increased risk for both COVID-19 – CRS and mortality, which might be modulated by regions, study setting and year. Risk for ICU admission and mechanical ventilation are the key indicators of CRS. We believe the updated data further anchoring CRS will contribute to more substantiation of the findings reported by similar earlier studies (Dong et al., 2021; K. W. Lee et al., 2021; Massarvva, 2021; Mellor et al., 2021; Ssentongo et al., 2021)

The age-related mortality and morbidity risk of COVID-19 has been considered speculative without enough scientific evidence. This study aimed to collect more evidence on the association between patient age and risk of severe disease state and/or mortality from SARS-CoV-2 infection. Genomic dataset along with metadata (3608 samples) retrieved from GISAID from different geographical regions were grouped into 10 age groups (0-10, 11-20, 21-30, 31-40, 41-50, 51-60, 61-70, 71-80, 81-90, 91-100 years) as well as high-risk or low-risk according to patient clinical status. Genomic sequences were aligned and analyzed using MAFFT and FASTTREE to build a phylogenetic tree in order to identify age-risk associations based on phylogenetic clustering. Case fatality rates (CFR), as well as the Odds ratio (OR) for high-risk outcomes, were calculated for different age groups. Results revealed that individuals aged between 25-50 years have the best immune response to the infection. On the other hand, disease fatality was higher in patients aging above 50 years. We created an application to calculate the OR of being at high risk given a certain age threshold from GISAID datasets. OR values increased between ages 1-10 years (1.271) and 11-20 years (1.313) but reduced at age range 21-30 years (1.290) and increased again for 61-70 years (2.465). CFR calculated for each of the age groups had peak values at 90-100 years (26.8%) and the lowest at 0-10 years (0%). The CFR for ages above 50 years was about twice greater (11.6%-26.8%) than that for ages below (0-6.6%). The phylogenetic analysis revealed that the majority of samples obtained from India showed low-risk among different age groups and were defined as clade GH. Another cluster from Singapore visualization showed unfavorable patient outcome across several age groups and were classified under clade O. To conclude, this study analyses showed a variety of age-risk associations. As scientists from different countries upload more genomes to globally shared databases, more evidence will reinforce mortality risk associations in COVID-19 patients.

Background. The main impetus behind the worldwide Covid-19 vaccination campaign in 2021 was to reduce the mortality attributed to SARS-CoV-2 infection in the preceding year. Nevertheless, rigorous analyses of the mortality benefits conferred by this massive vaccination effort have been lacking. Methods Statistics offers us an essential methodological approach for measuring the impacts of Covid-19 vaccination on public health. The mathematical relation between vaccinated-alive groups can be repeated between vaccinated-dead groups with relatively high statistical reliability because of the large population numbers involved. This method also confers greater statistical usefulness because it eliminates the Simpson effect. Results Calculations were performed for each of the following five(5) four-week intervals: weeks 35-38 (2021), weeks 39-42 (2021), weeks 43-46 (2021), weeks 47-50 (2021), and weeks 50(2021)-1(2022). The results obtained confirm that the mortality of the vaccinated coronavirus-infected groups was 14.5% higher on average than the mortality of non-vaccinated coronavirus-infected groups. Conclusions Vaccinated infected groups appear to have higher average mortality than their non-vaccinated infected counterparts. The findings suggest the legitimacy of extending the statistics between vaccinated living and vaccinated dead individuals for different age groups. Calculating the impact of Covid-19 vaccination on the mortality rate is a necessary step towards satisfying the first principle of medicine: “Primum non nocere”, “Do no harm”.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known globally as COVID-19, originated in December 2019 in Wuhan, Hubei province in China and has rapidly spread across the globe ever since. The first recorded case in sub-Saharan Africa was in Nigeria, on the 25th February, 2020. The virus continues to spread, and new variants of the disease have emerged, the number of deaths and new infections in the countries of sub-Saharan Africa has been relatively low compared to predictive models. This could be due to several factors, such as slower transmission dynamics of the virus, a lower-case fatality rate, or a lack of testing or reliable data. Whilst this may also, in part, be due to the robustness of the nations' public health responses, there is scarce reporting on the specifics of this. However, emerging research has demonstrated that various environmental factors could influence virus transmission. The study adopted collected meteorological data that was critically analysed and discussed. The impact of three factors in the context of sub-Saharan African nations: temperature, ultraviolet (UV) exposure and pre-existing infection with Plasmodium (malaria) were discussed. These factors were discussed critically in light of the reduced rates of transmission and mortality observed.

Humanity’s social and economic development has been challenged by a range of adversities over the millennia that have caused widespread and unimaginable suffering. At the same time, these challenges have forced humans to evolve more wisely, overcoming adversity through creativity and leading to advancements in science and technology, medicine, ethics and legal systems, and socio-political systems. The dynamics of risks and opportunities caused by COVID-19, in the built, cyber, social and economic environments, present opportunities for deepening our understanding of resilient and sustainable development and infrastructure. This article reflects on five lessons that COVID-19 is teaching us about what it means to develop sustainably through the lens of transportation: (1) sustainable development planning and analytical frameworks must be comprehensive, for long-term sustainability; (2) multi-modal transportation is a superior vision for sustainable development than any one particular mode; (3) tele-activities are part of an effective infrastructure sustainability strategy; (4) economic capital is critically important to sustainable development even when it is not a critical existential threat, and, (5) effective social capital is essential in global disaster resistance and recovery, and can and must be leveraged between fast-moving and slow-moving disasters. Resilient and sustainable infrastructure will continue to be critical to addressing evolving natural and man-made hazards in the 21^st Century.

With covid infection having a periodic occurrence and each wave affecting millions of people, there is an urgent need for healthcare providers to develop readily available biological markers to predict the severity and mortality of COVID-19 infection at an early stage of the disease to predict the most optimal management. The Neutrophil – lymphocyte Ratio (NLR) and Platelet-lymphocyte ratio (PLR), are novel inflammatory markers suggested to predict the severity of illness in COVID -19 patients. We evaluated a retrospective cohort of 224 patients with covid 19 illnesses from August 2020 to October 2020. We categorized the patients into 'mild' and 'moderate to severe as per the severity of their illnesses. The optimal cut-off values of the continuous NLR, PLR, and Age were calculated by applying the receiver operating characteristic (ROC) curve analysis. Sensitivity, specificity, and likelihood ratios were calculated from ROC analysis. The results showed that Age, Hb, and NLR are significant predictors of the severity of illness. So we quantified the predictive contribution of those variables by its Z- score and showed that NLR is the strongest predictor followed by Age.ROC curve analysis revealed NLR to be an excellent predictor of disease severity (sensitivity 88.30% and specificity 82.60%) at a cut-off of > 4.24. A cut-off of 165.63 was obtained for PLR, which showed good predictive value for the severity of illness. A NLR > 6 was used as a predictor for mortality due to covid illness with good sensitivity and specificity. Conclusion: NLR and PLR can be used as prognostic markers to identify the severity of covid illness and the probability of mortality in the future in low-resource settings. Given its low cost, easily available, accessible, and reproducible data, it can be used as a useful marker to assist the physician in early referral and treatment of covid 19 infected patients.

Covid-19 has given a halt to all the activities in the world. Europe was most affected, followed by the United States of America. It has taken more than 350000 lives until now. In this study, we have assessed the severity of Covid-19 by analyzing the mortality rate of Covid-19 and other chronic diseases. The Covid-19 data and “death rate” data caused by other diseases were downloaded from the world health organization (WHO) website. A normalized method was used to see the mortality rate of Covid-19 in comparison to other diseases. The deaths caused by Covid-19 in April 2020 have overtaken the average number of deaths caused by Cancer, Cardiovascular diseases, and other diseases in Belgium, the United Kingdom (UK), Spain, France, and Ireland. Covid-19 was found to be strongly correlated with non-communicable respiratory diseases and Cancer with correlation coefficients 0.73 and 0.67 respectively. The severity of Covid-19 in the United States of America (USA) was moderate. The severity of Covid-19 in Asian countries was found to be low. Europe showed the highest diversity in the mortality rate of Covid-19. On average, except for a few European countries, Cardiovascular diseases, cancer, and non-communicable respiratory diseases were still more lethal and caused more deaths than Covid-19.

Artificial Intelligence (AI) and Machine Learning (ML) approaches that could learn from large data sources have been identified as useful tools to support clinicians in their decisional process; AI and ML implementations have had a rapid acceleration during the recent COVID-19 pandemic. However, many ML classifiers are “black box” to the final user, since their underlying reasoning process is often obscure. Additionally, the performance of such models suffer from poor generalization ability in presence of dataset shift. Here, we present a comparison between an explainable-by-design (“white box”) model (Bayesian Network (BN)) versus a black-box model (Random Forest), both studied with the aim to support clinicians of Policlinico San Matteo University Hospital in Pavia (Italy) during the triage of COVID-19 patients. Our aim is to evaluate whether the BN predictive performances are comparable with those of a widely used but less explainable ML model such as Random Forest and to test the generalization ability of the ML models across different waves of the pandemic.

To safeguard against meat supply shortages during pandemics or other catastrophes, this study analyzed the potential to provide the average household’s entire protein consumption using either soybean production or distributed meat production at the household level in the U.S. with: 1) pasture-fed rabbits, 2) pellet and hay-fed rabbits, or 3) pellet-fed chickens. Only using the average backyard resources, soybean cultivation can provide 80%-160% of household protein and 0%-50% of a household’s protein needs can be provided by pasture-fed rabbits using only the yard grass as feed. If external supplementation of feed is available, raising 52 chickens while also harvesting the concomitant eggs or alternately 107 grain-fed rabbits can meet 100% of an average household’s protein requirements. These results show that resilience to future pandemics and challenges associated with growing meat demands can be incrementally addressed through backyard distributed protein production. Backyard production of chicken meat, eggs, and rabbit meat reduces environmental costs of protein due to savings in production, transportation, and refrigeration of meat products and even more so with soybeans. Generally, distributed production of protein was found to be economically competitive with centralized production of meat if distributed labor costs were ignored.

Using publicly available data for 28 EU/EES countries from Eurostat and Our World in Data, we investigate how the current rate of Covid vaccination in a country compares to its average rate of excess mortality (EM) in the pandemic to date. We find that, in the linear regression, the correlation between average EM and vaccination rate is strongly negative, a priori evidence to support the claim that the Covid vaccines have saved many lives. However, a closer analysis of the timeline suggests otherwise. The correlation was already strongly negative before the vaccines were rolled out and is only weakly negative thereafter. In theory, survivor bias could still explain this shift, especially since waves of EM closely align with Covid waves. However, we find in addition that about half of our 28 countries experienced higher EM in 2022 than in 2021, and all that did so have higher than average vaccination rates. This is something which survivor bias cannot explain and raises the real possibility that the vaccines have not just failed to save many lives, but may have already caused net harm. Moreover, any such harm may be ongoing since we find that EM and vaccination rates have been consistently positively correlated since April 2022. We show that all these findings are robust to several different ways of measuring EM and/or vaccination rates. Finally, using public data from Worldometers, we show that the correlation over time of official Covid mortality rates with current vaccination rates closely tracks that of EM rates, even as Covid mortality has waxed and waned and even in the post-omicron period.

Abstract: This study aims to evaluate the effectiveness of plant-based diets and supplement interventions in mitigating the severity and mortality of COVID-19 patients. Observational research suggests that a diet high in plant-based foods may decrease the likelihood and severity of SARS-CoV-2 infection. However, several studies have faced criticism regarding the accuracy of obtaining dietary questionnaires and the insufficient intake of essential vitamins and minerals in such diets. To address these issues, this study employed a prospective interventional design that strictly controlled the quality, quantity, and processing of plant-based foods provided. Furthermore, the study incorporated supplementation, which enhanced the anti-viral, anti-inflammatory, antioxidant, antithrombotic, and immunomodulatory properties of a plant-based diet against the SARS-CoV-2 virus. Of the 3,470 COVID-19 elderly patients, half already followed a plant-based diet, and the other half new patients, both with various comorbidities, were intervened with a plant-based diet and supplementation. The results showed that the participants in the non-plant-based diet group were twice as likely to have experienced moderate disease (with 95% confidence limit: 1.42-3.12) and 2.4 times more likely to have experienced severe disease (with 95% confidence limit: 1.2-5.0) with a significant p-value of less than 0.0005 and 0.016 respectively, compared to those in the PBD group. During the interventions, none of the participants experienced disease progression or hospitalization, and there were no reported deaths by the end of the study. The outcome was highly favorable and aligned with the researchers' expectations, as COVID-19 morbidity and mortality rates in Indonesia were among the highest in Asia. This research may provide significant insights into the prevention and management of COVID-19 and aid in better preparation for future pandemics.

The COVID-19 pandemic halted progress in global vaccine coverage and disrupted routine childhood vaccination practices worldwide. While there is ample evidence of vaccination declines experienced during the pandemic, it is less clear how low-income countries were affected. We executed a systematic review to synthesize the current literature on impacts to routine childhood vaccinations in low-income countries from January 1, 2020 to February 8, 2023. We collected data using an extraction form on Covidence and assessed the quality of studies included in the review using the Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I) tool. Effect estimates for changes in vaccination during the pandemic were reported and summarized. Factors that influenced changes were grouped into descriptive themes. Thirteen studies, encompassing 18 low-income countries and evaluating 15 vaccines at varying doses, were included in the final review. We found that routine childhood vaccinations during the COVID-19 pandemic varied considerably by vaccine type, location, and phase of the pandemic. Nine different themes were identified as factors that influenced changes in vaccination. Documenting past experiences and lessons learned is crucial for informing preparedness efforts in anticipation of future public health emergencies. Failure to effectively address these things in the next public health emergency could result in a recurrence of declining routine childhood vaccinations.

During the COVID-19 pandemic, the high prevalence of comorbidities in Mexico, as well as the disparities between public and private health subsystems, substantially contributed to the severe impact it had in the country. The objective of this study was to evaluate and compare risk factors present at admission for mortality of hospitalized patients with COVID-19. A 2-year retrospective cohort study of hospitalized adult patients with COVID-19 was conducted at a private tertiary care center. The study population consisted of 1,258 patients with a median age of 56 ± 16.5 years, of whom 1,093 recovered (86.8%) and 165 died (13.1%). In the univariate analysis, older age (p &lt;0.001), comorbidities such as hypertension (p &lt;0.001) and diabetes (p &lt;0.001), signs and symptoms of respiratory distress, and markers of acute inflammatory response were significantly more frequent in non-survivors. The multivariate analysis showed that older age (p &lt;0.001), the presence of cyanosis (p 0.005) and previous myocardial infarction (p 0.032) were independent predictors for mortality. In the studied cohort, risk factors present at admission associated with an increased risk of death were older age, cyanosis and a previous myocardial infarction, which can be used as valuable predictors for patients’ outcomes. To our knowledge, this is the first study analyzing predictors of mortality in COVID-19 patients attended on a private tertiary hospital in Mexico.

The prevalence of depression and anxiety among healthcare workers (HCWs) during the Covid-19 pandemics is high. The aim of the study is to identify the importance of self-esteem and occupational factors in association with the incidence of depression and anxiety among HCWs through a longitudinal cohort study during the Covid-19 pandemics in Latvia. Participants were interviewed repeatedly seven times during the Covid-19 pandemics. 322 participants were included in the data analysis for depression and 352 for anxiety. Low self-esteem and working in the general practitioner's office is associated with the incidence of depression and anxiety. Undergraduate education and direct contact with Covid-19 patients are associated with the incidence of depression. The risk of anxiety decreases by 1% each year among HCWs. Our study shows the importance of personality factors of HCWs during the Covid-19 pandemics.

This study updates previous research showing that 22 all-cause mortality in 31 European countries increased over time the higher the 21 COVID-19 full vaccination uptake. The update illuminates that a one percentage point increase in 21 full vaccination uptake initially decreased all-cause mortality from Jan to Mar 22 by –0.423 percent (95% CI –0.577, –0.270), but the following 14 months, a one percentage point increase in 21 booster vaccination uptake oppositely increased mortality by 0.366 percent (95% CI 0.250, 0.482). The findings indicate that full vaccination initially prevented mortality, but subsequently, booster vaccination, in particular, detrimentally and consistently induced higher mortality. The effects remained robust when controlling for alternative explanations. Studies have argued that heat waves caused mortality in the 22 summer and energy prices caused mortality in the 22-23 winter. However, the update shows that booster vaccination consistently induced higher mortality when neither heat waves nor energy prices were likely explanations.

Introduction: The viral respiratory disorder known as severe acute respiratory syndrome (SARS) is caused by the SARS-associated coronavirus. End of May 2021, there have been over 170 million reported cases and more than 3.53 million deaths worldwide. In the field of cardiovascular performance, the shock index (SI) is a simple and widely used formula that measures the physiological response prior to systemic hypotension. It is obtained by dividing the heart rate by the systolic blood pressure. Another parameter, the modified shock index (MSI), is defined as the ratio of heart rate to mean arterial pressure (MAP). Methods: In this retrospective study, we examined the data of COVID-19 patients who were admitted to the ICU between June 1st, 2020, and January 31st, 2021. The patient details were collected from the Medical Records Department (MRD) in a tertiary care center. A case-recorded proforma was used to record the patient information, which was then analyzed using SPSS software. Results: The mean age of the patients was 67 years, consisting of 64 males (68.9%) and 29 females (31.1%). The standard deviation was calculated as 13.557. Among the patients, the minimum age recorded was 28, while the maximum age was 97. Patients with known CAD (Coronary Artery Disease) had a higher mortality rate among comorbidities. The p-value for Shock Index and Mortality Status was 0.205 (with an alpha value greater than 0.05), indicating no significant association. However, the modified shock index (MSI) showed a significant association with mortality status. The Pearson chi-square value for MSI and Mortality Status was 4.19, with a p-value of 0.041 (with an alpha value less than 0.05). These results suggest that there is a higher risk of mortality for patients with a Modified Shock Index of less than 0.7 or greater than 1.3. The MSI Odds ratio was found to be 4.500, indicating that such patients have a 4.5 times higher risk of mortality compared to others. Conclusion: In conclusion, our study found that there is no significant association between the Shock Index (SI) and mortality, as the p-value was greater than 0.05. However, the Modified Shock Index (MSI) showed a significant association with mortality status, with a p-value less than 0.05. This suggests that patients with a Modified Shock Index less than 0.7 or greater than 1.3 are at a higher risk of mortality. It is important to consider these findings when assessing the prognosis and potential interventions for COVID-19 patients.

Laboratory analyses reveal significant differences between vaccinated and unvaccinated patients in parameters like C-reactive protein, ferritin, and white blood cell counts. Univariate and multivariate Cox proportional hazards regression analyses identify several factors influencing mortality, including comorbidities, pneumonia development, and various inflammatory markers.

COVID-19 outbreak started in the Chinese city of Wuhan and spread around the globe in months due to its high contagious level. Hence, this disease posed a great challenge to researchers and mathematicians. Numerous mathematical models have been suggested to visualize the spreading speed and trends of this pandemic. In this paper, a comparison of a widely established and accepted method, the Susceptible-Exposed-Infected-Quarantined-Recovered-Death-Insusceptible (SEIQRDP) model with a newly-proposed fractional-order SEIQRDP is studied. Densely populated Countries of Asia (Pakistan, India, and Bangladesh) have been chosen as data sets and both algorithms have been applied to their data. The same comparison technique has also been used on the data of two polar countries, New Zealand and Russia, that validated our findings. Error comparison of both algorithms has been recorded in a tabulated form which shows multi-fold erroneous trends over WHO data of selected countries.

The question of how many deaths were averted by interventions during the COVID-19 pandemic carries important implications for policies going forward. Given that the interventions of lockdowns and mass vaccination carry acknowledged downsides, it is important to balance these against potential upsides, which were the putative reasons for health officials to implement these policies. Several attempts have been made to quantify the impact of mass vaccination on averted mortality both globally, and in specific countries/regions, using different methodologies. Here, we examine the assumptions of these models and look for areas of improvement, while understanding that simplifications are inherent in model building. We find that several assumptions greatly overstate the degree of averted mortality due to vaccination and perform an empirical analysis of country level data in Europe to test if vaccination was associated with lower excess mortality. We show a positive and statistically significant correlation between number of vaccine doses given and 2022 excess mortality, calling into question estimates of positive averted mortality due to mass vaccination. Our results show several assumptions which work to systematically overstate the level of averted mortality from Covid-19 vaccines. This work aims to improve epidemiological modeling on the impact of vaccination, and to ground the public health response to infectious diseases in robust and rigorous analysis.

A population dynamics epidemic model is developed that incorporates age of infection and age of vaccination. The model analyzes pre-symptomatic and symptomatic periods of an infected individual in terms of infection age. The model analyzes the efficacy of vaccination in terms of vaccination age. The model is applied to the 2003 SARS epidemic in Taiwan and the current COVID-19 epidemic in New York State.

Objectives: To analyze cumulative incidence and mortality by COVID-19 in the different types of nursing homes, in the first and second wave.Methods: Data source: Transparency portal of the Generalitat de Catalunya, official COVID-19 register of all Catalan nursing homes (from March to December 10, 2020). Variables: Affectation of disease and mortality by COVID-19 in geriatric centers in Catalonia, stratified by size of center and type of management. Univariate, bivariate and multivariate linear regression analysis, IBM^TMSPSS^® v.20.Results:N = 1,057 residences (63,682 beds, average = 60.48). 52% have <50 beds, 42.8% between 50-150 beds, and 5%> 150 beds. COVID-19: Cumulative incidence: 21,370 (confirmed + suspicious); mortality: 5,010 (confirmed + suspicious).233 residences did not register COVID-19 patients (22%) and 636 did not register COVID-19 deaths (60.2%).According to the type of supplier, the lowest incidence of COVID-19 was in non-accredited (36.2% without cases), then concerted / collaborators (19.3%) and own of the Generalitat de Catalunya (7.9 %, p = 0.000). The centers with the fewest deaths were the non-accredited (78% without deaths), followed by contracted / collaborators (56.8%) and those of the Generalitat de Catalunya (41.3%, p = 0.000).Depending on the size, no COVID-19 patients were registered: 168 centers <50 beds (30.5%); 64 of the 50-150 beds (14.2%); and 1 of the> 150 beds (1.9%) (p = 0.000). 395 of the <50 beds did not register deaths due to COVID-19 (71.8%), 232 of the 50-150 beds (51.3%), and 9 of the> 150 beds (17%) (p = 0.000).Discussion:Of the 1,057 centers analyzed, 22% did not register cases and in 60% there were no deaths from COVID-19.Residences without concert / collaboration with the Generalitat and with fewer beds showed a lower cumulative incidence and mortality due to COVID-19 during the first wave.

Rationale: The possible effect of Particulate Matter (PM10 and PM2.5 of diameter 10 and 2.5 µm respectively) levels on Covid-19 mortality is now well established. However, time-evolution of Covid-19 mortality according to PM2.5 levels has been scarcely investigated. Aim: To understand this relationship at the European level for the period 2020 (beginning) - 2022 (end). Methods: 16 representative locations in Europe (81 million people) with heterogeneous levels of PM2.5 (µg.m-3), from low to high. PM2.5 levels were assessed by various methods, and Covid-19 mortality was reported by Johns Hopkins University. Results: The trend of Covid-19 mortality vs. PM2.5 levels varied among locations. Overall, the estimated mean value was of a 40±20% mortality increase per 1 µg.m-3 PM2.5 increase. The stronger the positive gradient of the PM peak, the stronger the positive gradient of the Covid-19 mortality. Exposure to several PM peaks during about a 2-month period was the main contributor to Covid-19 mortality increases. Conclusion: Our data confirm a temporal relation between PM2.5 exposure and Covid-19 mortality, considering a 2-month integration-time for pollution events. Number-concentrations of PM should be used in the future rather than the PM2.5 mass-concentrations (µg.m-3) with the consideration of PM composition to better explain this finding.

Cardiovascular diseases (CVD´s) continue as the primary cause of death worldwide. During the past couple of years, and with the surge of the COVID-19 pandemic, deaths linked to CVD´s were -slightly overshadowed by those deaths related to COVID-19, albeit during the highest peaks of the pandemic. The present study derived form understanding the correlation between both diseases, from the standpoint of patients already diagnosed with CVD´s (n = 41883) and what comorbidities had the highest influence on overall patient death (n = 3637). Obesity, hypertension, and diabetes are all linked to worsening the outcome of COVID-19 positive patients, hence they were considered when looking at the overview of all CVD positive patients. Our finding showed that 1697 deaths were related to diabetes (p < 0.001), 987 deaths were related to obesity (p < 0.001), and lastly 2499 deaths were attributed to hypertension (p < 0.001) individually. Using logistic regression modeling, we found diabetes (OR: 1.744, p < 0.001) and hypertension (OR: 2.179, p < 0.001) had a high impact on patient deaths. Hence, having a CVD diagnosis, with hypertension and/or diabetes seems to increase the likely-hood of complication leading to death in COVID-19 positive patients.

The pharmacology of azithromycin, and the actions of certain bee products, suggest the possibility of overlap with the pathophysiology of COVID-19 at several points in the disease process. First, intercellular epithelial tight junctions of the respiratory tract serve as a critical barrier to invaders. Pathophysiological factors capable of disrupting this epithelial barrier include viral virulence factors such as those observed for other coronaviruses; virulence factors derived from potentially synergistic pathogens such as Candida albicans and Porphyramonas gingivalis; and imbalances in the host inflammatory response. Azithromycin, and to a lesser extent, certain bee products, appear to have actions that oppose such processes. Second, the matrikine PGP or its derivatives may contribute to risk in individuals at high risk for serious COVID-19 infection, especially during reactivation; but azithromycin is capable of modulating PGP in some contexts. Third, the most serious COVID-19 infections are associated with massive upregulation of inflammatory cytokines such as IL-6, TNF alpha, and other inflammatory cytokines. The anti inflammatory actions of azithromycin and bee derived products such as melittin are potentially capable of modulating these processes, as well. Azithromycin is already in current use as a treatment for COVID-19; however, it's utility as a protector of epithelial barrier function would be most likely to be realized in prophylactic context rather than in a treatment context. Similarly, since the anti inflammatory effects of bee products take time, their effectiveness of melittin and other bee products would be expected to be maximized in a prophylactic context. In the context of the current pandemic, prophylaxis with azithromycin, bee products, or both, might be warranted for individuals at high risk for serious COVID-19 infection.

Background: Endotheliopathy is common pathologic findings in patients with acute and long COVID-19. It may be associated with disease severity and predispose to long-term complications. Plasma levels of a proteoglycan syndecan-1 are found to be significantly elevated in patients with COVID-19, but its roles in assessing the disease severity and predicting long-term outcome are not fully understood. Methods: 124 consecutive hospitalized patients with SARS-CoV2 infection were prospectively enrolled and blood samples were collected on admission (T1), 3-4 days following treatment (T2), and 1-2 days prior to discharge or death (T3). Plasma levels of syndecan-1 were determined using an immunosorbent assay; various statistical analyses were performed to determine the association between plasma syndecan-1 levels and disease severity or the 60-day mortality rate. Results: Compared with those in the healthy controls, plasma levels of syndecan-1 in patients with critical COVID-19 were significantly higher (p<0.0001). However, there was no statistically significant difference among patients with different disease severity (p>0.05), resulting from large individual variability. Longitudinal analysis demonstrated that while the levels fluctuated during hospitalization in all patients, plasma syndecan-1 levels were persistently elevated from baseline in critical COVID19 patients. Cox proportional hazard regression analyses revealed that elevated plasma levels of syndecan-1 (>260 ng/mL at T1, >1018 ng/mL at T2, and >461 ng/mL at T3) were significantly associated with the 60-day mortality rate. Conclusions: Endotheliopathy, marked by glycocalyx degradation and elevated plasma syndecan-1, occurs in nearly all hospitalized patients with SARS-CoV2 infection; the elevated plasma syndecan-1 is associated with increased mortality in COVID-19 patients.

The increase in pandemics caused by RNA viruses of zoonotic origin highlights the urgent need for broad-spectrum antivirals against novel and re-emerging RNA viruses. Broad-spectrum antivirals could be deployed as first-line interventions during an outbreak while virus-specific drugs and vaccines are developed and rolled out. Viruses depend on the host’s protein synthesis machinery for replication. Several natural compounds that target the cellular DEAD-box RNA helicase eIF4A, a key component of the eukaryotic translation initiation complex eIF4F, have emerged as potential broad-spectrum antivirals. Rocaglates, a group of flavaglines of plant origin that clamp mRNAs with highly structured 5’UTRs onto the surface of eIF4A through specific stacking interactions, exhibit the largest selectivity and potential therapeutic indices among all known eIF4A inhibitors. Their unique mechanism of action limits the inhibitory effect of rocaglates to the translation of eIF4A-dependent viral mRNAs and a minor fraction of host mRNAs exhibiting stable RNA secondary structures and/or polypurine sequence stretches in their 5´UTRs, resulting in minimal potential toxic side effects. Maintaining a favorable safety profile while inducing efficient inhibition of a broad-spectrum of RNA viruses makes rocaglates into primary candidates for further development as pan-antiviral therapeutics.

The potential advantages of plant-based interventions in decreasing the incidence and lessening the morbidity and mortality of COVID-19 are yet to be widely acknowledged. Nevertheless, recent publications propose that these interventions could be effective. Our cardiology center conducted an interventional study in which we incorporated plant-based foods and provided supplementation to 3470 elderly COVID-19 patients with multiple comorbidities. We pay close attention to the precise selection of food items, the application of appropriate processing methods, and the provision of nourishing meals to our patients. Our outcome was highly successful as we achieved a zero mortality rate, and none of our patients experienced worsening conditions or required hospitalization. In this review article we present the mechanisms how plant-based and supplement interventions can mitigate COVID-19 disease severity and mortality. And experts have validated our hypotheses very recently. The measures and methods used to address the ongoing SARS-CoV-2 pandemic through plant-based interventions and supplements are effective and may help prevent or manage future pandemics.

Conclusions: The mortality rate of people with disabilities from all causes did not significantly increase compared to the pre-COVID-19 era. However, COVID-related deaths among people with disabilities increased significantly.

There is much discussion among clinicians, epidemiologists, and public health experts about why case fatality rate from COVID-19 in Italy (at 13.3% as of April 20, 2020, versus a global case fatality rate of 6.9%) is considerably higher than estimates from other countries (especially China, South Korea, and Germany). In this article, we propose several potential explanations for these differences. We suggest that Italy’s overall and relative case fatality rate, as reported by public health authorities, is likely to be inflated by such factors as heterogeneous reporting of coronavirus-related fatalities across countries and the iceberg effect of under-testing, yielding a distorted view of the global severity of the COVID-19 pandemic. We also acknowledge that deaths from COVID-19 in Italy are still likely to be higher than in other equally affected nations due to its unique demographic and socio-economic profile. Lastly, we discuss the important role of the stress imparted by the epidemic on the Italian healthcare system, which weakened its capacity to adequately respond to the sudden influx of COVID-19 patients in the most affected areas of the country, especially in the Lombardy region.

(1) Background: Until December 2021, French Guiana (FG), located in South America, faced four consecutive COVID-19 epidemic waves. This study sought to analyze the mortality trend of severe COVID-19 patients admitted to the referral ICU of FG. (2) Methods: We conducted a prospective, observational, and non-interventional study in ICU at Cayenne Hospital. We included 383 patients older than 18 admitted with SARS-CoV-2-related pneumonia hospitalized from May 2020 to December 2021. The study covers three periods. Period 1 (Waves 1 and 2, original variant), period 2 (Wave 3, gamma variant), and period 3 (Wave 4, delta variant). (3) Results: The median age was 63 years (52-70). Frailty was diagnosed in 36 patients over 70 (32.4%). Only 4.8% of patients were vaccinated. The median ICU LOS was 10 days (6-19). Hospital mortality was 37.3%. It was 30.9% in period 1, 36.6% in period 2 (p=0.329 vs. period 1), and 47.1% in period 3 (0.015 vs. period 1). In multivariate analysis, independent factors associated with hospital mortality were age more than 40 years (]40-60 years] OR=5.2, 95%CI: 1.4-19.5; (]60-70 years] OR=8.5 95%CI: 2.2-32; (]70+ years] OR=17.9, 95%CI: 4.5-70.9), frailty (OR=5.6, 95%CI: 2.2-17.2), immunosuppression (OR=2.6, 95%CI: 1.05-6.7), and MV use (OR=11, 95%CI: 6.1-19.9). This model had an overall sensitivity of 72%, a specificity of 80.4%, a positive predictive value of 68.7%, and a negative predictive value of 82.8%. (4) Conclusions: The mortality of severe COVID-19 patients in French Amazonia was higher during the delta variant wave. This over-death is explained by the virulence of the responsible SARS-CoV-2 variant and the under-vaccination coverage of the studied population.

COVID-19 was first identified in Wuhan, China in December 2019 and has caused huge death and has spread to almost all the parts of the world. There are speculation that most of the world economy and financial markets would be affected due to lockdown and social distancing. The first case of COVID-19 was first identified in Nigeria on 27th February 2020 and this study examines the effect of COVID-19 outbreak on the performance of the Nigeria stock exchange using historical data covering 2nd March 2015 to 16th April, 2020 sourced from a secondary source. This study considered the COVID-19 period from 2nd January 2020 to 16th April 2020, the results revealed a loss in stock returns and high volatility in stock returns under the COVID-19 period in Nigeria as against the normal period under study. In addition, Quadratic GARCH (QGARCH) and Exponential GARCH (EGARCH) models with dummy variable were applied to the stock returns shows that the COVID-19 has had negative effect on the stock returns in Nigeria. The study recommended that political and economic policy such as stable political environment, incentive to indigenous companies, diversification of the economy, flexible exchange rate regime be implemented so as to improve the financial market and to attract more and new investors to the Nigerian Stock Exchange.

The emergence of the COVID-19 outbreak has caused a pandemic situation in over 210 countries. Controlling the spread of this disease has proven difficult despite several resources employed. Millions of hospitalization and deaths have been observed, and thousands of cases daily with many measures in place. Due to the complex nature of COVID-19, we proposed a system of time-fractional equations to understand the transmission of the disease better. Nonlocality involved in the model has made fractional differential equations appropriate for modeling the behavior. However, solving these types of models is computationally demanding. Our proposed generalized compartmental COVID-19 model incorporates effective contact rate, transition rate (from exposed quarantine and recovered to susceptible and infected quarantined individuals), quarantine rate, disease-induced death rate, natural death rate, natural recovery rate, recovery rate of quarantine infected for a holistic study of the coronavirus disease. A detailed analysis of the proposed model is carried out, including the existence and uniqueness of solutions, local and global stability analysis of the disease-free equilibrium analysis, and sensitivity analysis. Furthermore, numerical solutions of the proposed model are obtained with the generalized Adam-Bashforth-Moulton method developed for the fractional order model. Our analysis and solutions profile show that each of these incorporated parameters is very important in controlling the spread of COVID-19, especially quarantining exposed and infected individuals and the effective contact rate. Based on the results with different fractional order, we observe that there seems to be a third or even fourth wave of the spike in cases of COVID-19, which is what is happening right now in many countries.

Since the outbreak of the novel coronavirus disease (COVID-19) at the end of 2019, the clinical presentation of the disease showed a great heterogeneity with a diverse impact between different subpopulations. Emerging evidence from different parts of the world showed significantly poor outcome among males compared to female patients. A better understanding of the molecular mechanisms behind this difference might be a fundamental step for a more effective and targeted response to the outbreak. For that reason, here we try to investigate the molecular basis of the gender variations in mortality rates related to COVID-19 infection. To achieve this, we used our in-house pipeline to process publicly available lung transcriptomic data from 141 females compared to 286 males. After excluding Y specific genes, our results showed a shortlist of 73 genes that are differentially expressed between the two groups. Our results showed downregulation of a group of genes that are involved in the regulation of hydrolase activity including (AGTR1, CHM, DDX3X, FGFR3, SFRP2, and NLRP2), which is also believed to be essential for lung immune response and antimicrobial activity in the lung tissues in males compared to females. In contrast, our results showed an upregulation of angiotensin II receptor type 1 (AGTR1), a member of the renin-angiotensin system (RAS) that plays a role in angiotensin-converting enzyme 2 (ACE2) activity modulation. Interestingly, recent reports and experimental animal models highlight an important role of this receptor in SARS-Coronavirus lung damage as well as pulmonary edema, suggesting a possible role of its blockers like losartan and olmesartan as potential therapeutic options for COVID-19 infection. Finally, our results also showed a differential expression of different genes that are involved in the immune response including the NLRP2 and PTGDR2, further supporting the notion of the sex-based immunological differences. Taken together, our results provide an initial evidence of the molecular mechanisms that might be involved in the differential outcomes observed between both genders during the COVID-19 outbreak. This might be essential for the discovery of new targets and more precise therapeutic options to treat COVID-19 patients from different clinical and epidemiological characteristics with the aim of improving their outcome.

Treatment with calcitriol, the hormonal form of vitamin D, has shown beneficial effects in ex-perimental models of acute lung injury. In this study we aimed to analyze the associations be-tween calcitriol supplementation and the risk of SARS-CoV2 infection or COVID-19 mortality. Individuals ≥18 years old living in Catalonia and supplemented with calcitriol from April 2019 to February 2020 were compared with propensity score matched controls. Outcome variables were SARS-CoV2 infection, severe COVID-19 and COVID-19 mortality. Associations between calcitriol supplementation and outcome variables were analyzed using multivariable Cox proportional regression. A total of 8076 patients were identified as being on calcitriol treatment. Advanced chronic kidney disease and hypoparathyroidism were the most frequent reasons for calcitriol supplementation in our population. Calcitriol use was associated with reduced risk of SARS-CoV2 infection (HR 0.78 [CI 95% 0.64-0.94], p=0.010), reduced risk of severe COVID-19 and reduced COVID-19 mortality (HR 0.57 (CI 95% 0.41-0.80), p=0.001) in patients with advanced chronic kidney disease. In addition, an inverse association between mean daily calcitriol dose and COVID-19 severity or mortality was observed in treated patients, independently of renal function. Our findings point out that patients with advanced chronic kidney disease could benefit from calcitriol supplementation during the COVID-19 pandemic.

The repurposing of licenced drugs for use against COVID-19 is one of the most rapid ways to develop new and alternative therapeutic options to manage the ongoing pandemic. Given the approximately 8,000 licenced compounds available from Compounds Australia that can be screened, this paper demonstrates the utility of commercially-available ex vivo/3D airway and alveolar tissue models. These models are a closer representation of in vivo studies compared to in vitro models, but retain the benefits of rapid in vitro screening for drug efficacy. We demonstrate that several existing drugs appear to show anti-SARS-CoV-2 activity against both Delta and Omicron Variants of Concern in the airway model. In particular, fluvoxamine, as well as aprepitant, everolimus, and sirolimus have virus reduction efficacy comparable to the current standard of care (remdesivir, molnupiravir, nirmatrelvir). Whilst these results are encouraging, further testing and efficacy studies are required before clinical use can be considered.

The worldwide pandemic of coronavirus disease 2019 (COVID-19) has become an unprecedented challenge to global public health. With the intensification of the COVID-19 epidemic, the development of vaccines and therapeutic drugs against the etiological agent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are also widespread. To prove the effectiveness and safety of these preventive vaccines and therapeutic drugs, available animal models that faithfully recapitulate clinical hallmarks of COVID-19 are urgently needed. Currently, animal models including mice, golden hamsters, ferrets, nonhuman primates and other susceptible animals have been involved in the study of COVID-19. 92 vaccine candidates have entered clinical trials after the primary evaluation in animal models, of which inactivated vaccines, subunit vaccines, virus-vectored vaccines and mRNA vaccines are promising vaccine candidates. In this review, we summarize the landscape of animal models and advanced vaccines with efficacy range from about 50% to more than 95%. In addition, we point out future directions for animal models and vaccine development, aiming at providing valuable information and accelerating the breakthroughs confronting SARS-CoV-2.

The models used to estimate disease transmission, susceptibility and severity determine what epidemiology can (and cannot tell) us about COVID-19. These include: ‘model organisms’ chosen for their phylogenetic/aetiological similarities; multivariable statistical models to estimate the strength/direction of (potentially causal) relationships between variables (through ‘causal inference’), and the (past/future) value of unmeasured variables (through ‘classification/prediction’); and a range of modelling techniques to predict beyond the available data (through ‘extrapolation’), compare different hypothetical scenarios (through ‘simulation’), and estimate key features of dynamic processes (through ‘projection’). Each of these models: address different questions using different techniques; involve assumptions that require careful assessment; and are vulnerable to generic and specific biases that can undermine the validity and interpretation of their findings. It is therefore necessary that the models used: can actually address the questions posed; and have been competently applied. In this regard, it is important to stress that extrapolation, simulation and projection cannot offer accurate predictions of future events when the underlying mechanisms (and the contexts involved) are poorly understood and subject to change. Given the importance of understanding such mechanisms/contexts, and the limited opportunity for experimentation during outbreaks of novel diseases, the use of multivariable statistical models to estimate the strength/direction of potentially causal relationships between two variables (and the biases incurred through their misapplication/misinterpretation) warrant particular attention. Such models must be carefully designed to address: ‘selection-collider bias’, ‘unadjusted confounding bias’ and ‘inferential mediator adjustment bias’ – all of which can introduce effects capable of enhancing, masking or reversing the estimated (true) causal relationship between the two variables examined. Selection-collider bias occurs when these two variables independently cause a third (the ‘collider’), and when this collider determines/reflects the basis for selection in the analysis. It is likely to affect all incompletely representative samples, although its effects will be most pronounced wherever selection is constrained (e.g. analyses focusing on infected/hospitalised individuals). Unadjusted confounding bias disrupts the estimated (true) causal relationship between two variables when: these share one (or more) common cause(s); and when the effects of these causes have not been adjusted for in the analyses (e.g. whenever confounders are unknown/unmeasured). Inferentially similar biases can occur when: one (or more) variable(s) (or ‘mediators’) fall on the causal path between the two variables examined (i.e. when such mediators are caused by one of the variables and are causes of the other); and when these mediators are adjusted for in the analysis. Such adjustment is commonplace when: mediators are mistaken for confounders; prediction models are mistakenly repurposed for causal inference; or mediator adjustment is used to estimate direct and indirect causal relationships (in a mistaken attempt at ‘mediation analysis’). These three biases are central to ongoing and unresolved epistemological tensions within epidemiology. All have substantive implications for our understanding of COVID-19, and the future application of artificial intelligence to ‘data-driven’ modelling of similar phenomena. Nonetheless, competently applied and carefully interpreted, multivariable statistical models may yet provide sufficient insight into mechanisms and contexts to permit more accurate projections of future disease outbreaks.

Background: The monocyte-phagocyte system (MPS), including monocytes/macrophages and dendritic cells (DCs), plays a key role in anti-viral immunity. We aimed to analyze the prognostic value of the MPS components on in-hospital mortality in a cohort of 58 patients (M/F ; mean ageSD years) with COVID-19 pneumonia and 22 age- and sex-matched healthy controls. Methods: We measured frequencies and absolute numbers of peripheral blood CD169+ monocytes, conventional CD1c+ and CD141+ (namely cDC2 and cDC1), and plasmacytoid CD303+ DCs by means of multi-parametric flow cytometry. A gene profile analysis of 770 immune-inflammatory related human genes and 20 SARS-CoV-2 genes was also performed. Results: Median frequencies and absolute counts of CD169-expressing monocytes were significantly higher in COVID-19 patients than in controls (p 0.04 and p 0.01, respectively). Conversely, percentages and absolute numbers of all DC subsets were markedly depleted in patients (p

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of CoV disease 2019 (COVID-19) is a highly pathogenic and transmissible CoV that is presently plaguing the global human population and economy. No proven effective anti-viral therapy or vaccine currently exist, and supportive care remains to be the cornerstone treatment. Through previous lessons learned from SARS-CoV-1 and MERS-CoV studies, scientific groups worldwide have rapidly expanded the knowledge pertaining to SARS-CoV-2 virology that included in vitro and in vivo models for testing of anti-viral therapies, and randomized clinical trials. In the present narrative, we review SARS-CoV-2 virology, clinical features, pathophysiology, and animal models with a specific focus on anti-viral and adjunctive therapies currently being tested or require testing in animal models and randomized clinical trials.

Background and Objectives: Since the beginning of COVID-19 pandemic, the infection primarily affected patients with following chronic conditions: cardiovascular disease, hypertension, chronic obstructive pulmonary disease, diabetes mellitus, obesity and cancer. The aim of this study was to explore clinical and epidemiological characteristics associated with COVID-19 outcomes in patients at the primary health care centre from March 2020 to September 2022. Materials and Methods: The study included 40,692 citizens of Banja Luka County, Bosnia and Herzegovina, who were confirmed and registered as RT-PCR positive on COVID-19. Differences regarding the distributions of patients between groups were analysed using Pearson chi square test and Mantel-Haenszel chi square test for trends, while differences in mean values were compared using independent samples t test. Relationship between mortality and independent variables were examined using logistic regression. Results: Out of 40,692 COVID-19 positive patients, 7.76% were hospitalized. The average age of hospitalized patients was significantly higher than the age of non-hospitalized patients (64.2±16.1 vs. 45.4±18.7; p&lt;0.001). The average age of patients with lethal outcome was nearly twice higher compared to patients with non-lethal outcome (74.6±11.5 vs. 45.7±18.6; p&lt;0.001). Male patients had higher hospitalization and mortality rate, compared to females (9.8% vs. 5.9%, p&lt;0.001; 4.8% vs. 3%, p&lt;0.001, respectively). The highest hospitalization rate was in patients with chronic renal failure, diabetes and cardiovascular diseases, while the death rate was the highest among patients with CRF and hearth comorbidities. Fever, cough, fatigue, nausea and vomiting, chest pain, shortness of breath and appetite loss favoured hospitalization. Patients with fatigue and appetite loss had higher percentage of lethal outcome. Vaccinated patients had significantly lower rate of lethal outcome. Conclusions: Clinical symptoms, signs and outcomes, are posing as predictive parameters for further management of COVID-19. Vaccination has an important role in clinical outcomes of COVID-19.

This qualitative study explores the implementation and conformation process of digital devices in teaching and learning before, during, and after the COVID-19 pandemic in Nepal. Using Rogers' Diffusion of Innovation Theory as a framework, the study examines the adoption and adaptation of digital devices by in-service secondary mathematics teachers (n=62) and their perceptions and preferences towards instructional modality. The findings suggest that despite the increased reliance on digital devices during the pandemic, there is a less likelihood of using them in face-to-face classrooms in developing countries like Nepal. The adoption of online learning has not yet reached the confirmation stage, even after the onset of the COVID-19 pandemic. Prior to the pandemic, online learning was not widely implemented or confirmed by teachers in developing countries societies. The study also provides important insights into the challenges and opportunities of using digital devices in post-COVID-19 classrooms, and its implications for policymakers and educators in Nepal.

COVID-19 is fast spreading around the globe in a highly contagious manner. The results from our study showed that after intervention with successive Lockdowns, there was marked decrease in the rate of COVID-19 cases, though there was sporadic volatility in number of COVID-19 cases due to some extrinsic factors. Concomitant with reduction in rate of COVID-19 there was gradual increase in doubling time of COVID-19, steady increase in number of discharged/recovered patients from COVID-19 reaching to ≥ 58% taking the entire Indian population into consideration. Another important aspect was consistent low mortality rate was accompanied by gradual increase in recovery rate of COVID-19 in the population. The possible implication of these results in the development of protective immunity in the population has been discussed.

A literature review was conducted to summarize the frequency of the D-allele of the angiotensin-converting enzyme-1 in all countries with available data. Using an ecological study design limited to high income countries, we found that the country-level frequency of the D-allele was associated with increased COVID-19 incidence and mortality.

Molnupiravir, a prodrug known for its broad antiviral activity, demonstrated efficacy in animal models of Covid-19, prompting clinical trials where initial results indicated a significant effect against the disease. However, subsequent clinical studies did not confirm these findings, leading to the rejection of molnupiravir for permanent market authorization in many countries. This report critically assessed 19 studies published in 17 reports that investigated the efficacy of molnupiravir in animal models of Covid-19 with the purpose of determining how well the design of these models informed human studies. We found that the administered doses of molnupiravir in most studies involving animal Covid-19 models were disproportionately higher than the dose rec-ommended for human use. Specifically, when adjusted for body surface area, half of the doses of molnupiravir used in the animal studies were more than twice as high as the human dose. Addi-tionally, the drug was frequently given prophylactically or shortly after SARS-CoV-2 inoculation in these models, in contrast to clinical trials where such timing is not consistently achieved. Furthermore, the recommended five-day treatment duration for humans was exceeded in several animal studies. Collectively, we suggest that these design elements in the reported animal studies contributed to a bias favoring molnupiravir, and thus inflated expectations for its efficacy against Covid-19. Addressing these elements may offer avenues to enhance the clinical efficacy of mol-nupiravir for treatment of Covid-19 that include dose increment, early treatment, and admin-istration by inhalation along with use of molnupiravir in antiviral combination therapy.

We review and assess the classic SIR and SEIR epidemiological models regarding possible applications to the COVID-19 pandemic. In spite of numerous more complicated models, we show how the qualitative features of the solution to the SIR and SEIR models continue to provide valuable public health insights in some scenarios. Using estimated COVID-19 data as of this date, the SEIR model shows that if it were possible to reduce R₀ from 2.5 to 1.25 through social distancing and other measures, the maximum fraction of the population that would become infected at any particular time would drop from 17% to 4%, provided that all of the model assumptions are satisfied. Finally, we compare the classic SIR model with a recent stochastic model with favorable results. Since this comparison underscores the importance of underlying connectivity assumptions, we conclude with Monte-Carlo simulations with specific connectivity that reproduce the classical SIR model with standard incidence.

Background: It is unclear why certain countries have been more severely affected by COVID-19 than other countries. Methods: In this ecological study we compared COVID-19 mortality and incidence/100,000 as well as 4 putative explanatory factors by WHO world region. Linear regression was then used to assess the country-level predictors of COVID-19 mortality/100,000 and incidence/100,000 in 204 countries with available data. Results: COVID-19 incidence and mortality/capita were greater in Europe than other regions. This was despite a higher testing rate in Europe than other regions. Europe had an older population than all other regions and a higher prevalence of obesity than Africa, South East Asia and the Eastern Mediterranean. Country level multiple linear regression revealed positive associations between mortality/capita and testing rate, percent of the population 65 years or older, and Europe compared to Western Pacific and South East Asia (all P<0.005). Results for the analyses with cases/100,000 as outcome variable were similar. Conclusion: Our results suggest that older populations as well as other undefined regional and national factors, possibly related to efficacy of control efforts, are responsible for differences in national severity COVID-19 epidemics.

Despite widespread vaccination, early treatments, and improved understanding of the disease, the effects of SARS-CoV-2 infection remain significant worldwide. Many patients still suffer from severe COVID-19, necessitating admission to intensive care units. Remdesivir is a primary treatment option among viral RNA polymerase inhibitors for hospitalized SARS-CoV-2 patients. However, there is a lack of studies examining factors influencing its effectiveness in this context. We conducted a retrospective study throughout 2022, analyzing clinical, laboratory, and sociodemographic data from 252 hospitalized COVID-19 patients treated with remdesivir. Six machine learning algorithms were compared and validated to predict factors associated with the loss of clinical benefit from remdesivir in terms of mortality and hospital stay. Data were extracted from electronic health records. The eXtreme Gradient Boost (XGB) method achieved the highest balanced accuracy for both mortality (95.45%) and hospital stay (94.24%). Factors associated with worse outcomes of remdesivir use in terms of mortality included limitation of life support treatment, need for ventilatory support (especially invasive mechanical ventilation) on day 14 after the first dose of remdesivir, lymphopenia, low levels of albumin and hemoglobin, presence of flu and/or coinfection, and cough. Factors associated with worse outcomes of remdesivir use in terms of hospital stay included the number of doses of the COVID-19 vaccine, patchy lung density, bilateral pulmonary radiological status, number of comorbidities, oxygen therapy, troponin and lactate dehydrogenase levels, and asthenia. These findings highlight the effectiveness of XGB as a strong candidate for accurately categorizing COVID-19 patients undergoing remdesivir treatment.

We primarily study a possible link between 2021 COVID-19 vaccination uptake in Europe and monthly 2022 excess all-cause mortality, i.e., mortality higher than before the pandemic. Analyses of 31 countries weighted by population size show that all-cause mortality during the first nine months of 2022 increased more the higher the 2021 vaccination uptake; a one percentage point increase in 2021 vaccination uptake was associated with a monthly mortality increase in 2022 by 0.105 percent (95% CI, 0.075-0.134). When controlling for alternative explanations, the association remained robust, and we discuss the result emphasizing causality as well as potential ecological fallacy. Also, the study shows that 2021 all-cause mortality was lower the higher the vaccination uptake, but this association became non-significant when controlling for alternative explanations.

This article describes a simple Susceptible Infected Recovered (SIR) model fitting with COVID-19 data for the month of march 2020 in New York (NY) state. The model is a classical SIR, but is non-autonomous; the rate of susceptible people becoming infected is adjusted over time in order to fit the available data. The death rate is also secondarily adjusted. Our fitting is made under the assumption that due to limiting number of tests, a large part of the infected population has not been tested positive. In the last part, we extend the model to take into account the daily fluxes between New Jersey (NJ) and NY states and fit the data for both states. Our simple model fits the available data, and illustrates typical dynamics of the disease: exponential increase, apex and decrease. The model highlights a decrease in the transmission rate over the period which gives a quantitative illustration about how lockdown policies reduce the spread of the pandemic. The coupled model with NY and NJ states shows a wave in NJ following the NY wave, illustrating the mechanism of spread from one attractive hot spot to its neighbor.

Watching the international COVID-19 epidemics unfold during February and early March 2020, we began to highlight how outbreaks in care homes were inevitable, given the vulnerable patients in close proximity, and pressures on social care to help relieve pressure on the NHS. We suggested public health measures would significantly impact on care homes. Specifically we felt all homes would have multiple individuals isolated within days of introduction of guidance requiring isolation of new cough (or fever), and that a clear definition of an outbreak would be needed that differentiated COVID19 from influenza. We share the experiences of a GP practice looking after 900 nursing or dual registration care homes in the London Borough of Ealing in the early stages of the COVID19 Pandemic 2020. We believe that the altered presentation of cases of COVID19 in care homes contributed to the size of outbreaks, and that keeping COVID19 out of homes is the only way to manage this disease, with early isolation and complete segregation of positive and negative cases. We have seen over 300 suspected cases resulting in four fold (n=175) average monthly death rates, three fold usual issue of anticipatory medications, and approximately 32% mortality rate (up to 43% in over 90’s). Discharge pathways from hospital and admissions to care homes must be clear and robust. COVID19 naïve patients should not be admitted to units with outbreaks and COVID19 positive patients should not be admitted to anything other than a designated “hot” home until risk of contagion is passed. Some patients are testing positive at over 30 days since initial mild symptoms.

During a sanitary crisis, excess mortality measures the number of all-cause deaths, beyond what we would have expected if that crisis had not occurred. The high number of COVID-19 deaths started a debate in Italy with two opposite positions: those convinced that COVID-19 deaths were not, by default, excess deaths, because many COVID-19 deaths were not correctly registered, with the most part attributable to other causes and to the overall crisis conditions, and those who presented the opposite hypothesis. We analyzed the curve of the all-cause excess mortality, during the period January 5, 2020 – August 28, 2022, compared to the curve of the daily confirmed COVID-19 deaths, investigating the association between excess mortality and the recurrence of COVID-19 waves in Italy. We compared the two curves looking for the corresponding highest peaks and we found that 5 out of the 6 highest peaks (83.3%) of the excess mortality curve have occurred, on average, just a week before the concomitant COVID-19 waves hit their highest peaks of daily deaths (Mean 6.4 days; SD 2.4 days). This temporal correspondence between the moments when the excess mortality peaked and the highest peaks of the COVID-19 deaths provides further evidence that the all-cause excess mortality wave has been mostly driven by COVID-19 deaths

This study assesses the impact of incorporating an adaptive learning mechanism into an agent-based model (ABMS) simulating behavior on a university campus during the course of a pandemic outbreak, with a particular case on the COVID-19 pandemic. The aim is to reduce overcrowding and infections on campus through the use of Reinforcement Learning (RL). Our findings indicate that RL is a viable approach for effectively representing agents’ behavior within this context. The results reveal specific temporal patterns of overcrowding violations. While our study successfully mitigated campus crowding, it had limited influence on altering the course of the epidemic. This highlights the necessity for comprehensive epidemic control strategies that consider the role of individual decision-making influenced by adaptive learning, along with the implementation of targeted interventions. This research significantly contributes to our understanding of adaptive learning within complex systems and offers valuable insights for shaping future public health policies in similar community settings. Future research directions encompass exploring various parameter settings and updating representations of the disease’s natural history.

Coronavirus disease 2019 (COVID-19) from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has resulted in tremendous morbidity and mortality worldwide. A major underlying cause of COVID-19 mortality is a hyperinflammatory cytokine storm in severe/critically ill patients. Although many clinical trials are testing the efficacy of targeting inflammatory cytokines/chemokines in COVID-19 patients, the critical inflammatory mediator initiating COVID-19 patient death is undefined. Here we suggest that the immunopathological pathway leading to COVID-19 mortality can be divided into three stages with distinct clinical features that can be used to guide therapeutic strategies. Our interpretation of the recently published clinical trials from COVID-19 patients suggests that the clinical efficacy in preventing COVID-19 mortality using IL-1 blockade is subjected to notable caveats, while that for IL-6 blockade is suboptimal. We discuss critical factors in determining appropriate inflammatory cytokine/chemokine targets, timing, and combination of treatments to prevent COVID-19 mortality.

The eruption of COVID-19 patients in 215 countries worldwide has urged for robust predictive methods that can detect as early as possible the size and duration of the contagious disease and also providing precision predictions. In much recent literature reported on COVID-19, one or more essential parts of such investigation were missed. One of the crucial elements for any predictive method is that such methods should fit simultaneously as much data as possible; these data could be total infected cases, daily hospitalized cases, cumulative recovered cases, and deceased cases, and so on. Other crucial elements include sensitivity and precision of such predictive methods on the amount of data as the contagious disease evolved day by day. To show the importance of these aspects, we have evaluated the standard SIRD model and a newly introduced Gaussian-SIRD model on the development of COVID-19 in Kuwait. It is observed that the SIRD model quickly picks up the main trends of COVID-19 development, but the Gaussian-SIRD model provides precise prediction a longer period of time.

We performed a global analysis with data from 149 countries to test whether temperature can explain the spatial variability of the spread rate and mortality of COVID-19 at the global scale. We performed partial correlation analysis and linear mixed effect modelling to evaluate the association of the spread rate and motility of COVID-19 with maximum, minimum, average temperatures and temperature extreme (difference between maximum and minimum temperature) and other environmental and socioeconomic parameters. After controlling the effect of the duration after the first positive case, partial correlation analysis revealed that temperature was not related with the spatial variability of the spread rate of COVID-19. Mortality was negatively related with temperature in the countries with high-income economies. In contrast, temperature extreme was significantly and positively correlated with mortality in the low-and middle-income countries. Taking the country heterogeneity into account, mixed effect modelling revealed that inclusion of temperature as a fixed effect in the model significantly improved model skill predicting mortality in the low-and middle-income countries. Our analysis suggest that warm climate may reduce the mortality rate in high-income economies but in low and middle-income countries temperature extreme may increase the mortality risk.

Background: The use of animal models for biomedical research provides us with a convenient and feasible route to establish causal relationships by recapitulating the temporal sequence of events in a controlled environment with a potential to manipulate the variables at multiple levels including genetic, protein, physiological or environmental. Objectives: The current review was conducted to gain insights into various animal models for the SARS-CoV-2 virus. Material and Methods: A literature review (PUBMED, PUBMED Central, PMC, Google Scholar, Google search engine) following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines performed in early April 2020 revealed 9 articles of interest. Search terms included covid 19, covid-19, novel corona virus, SARS-CoV-2, animal models, experimental models, laboratory models & covid 19 animal models. Two independent reviewers extracted the data; the third reviewer was involved in case of discrepancy. Results: SARS-CoV-2 shares an identical receptor binding domain with the SARS-CoV virus and has a superior binding affinity to the host ACE2. Based on this, the role of rhesus macaques, golden Syrian hamsters, transgenic hACE2 mice and ferrets as animal models have been studied. All four animals are susceptible to infection with SARS-CoV-2 with variable clinical presentation but universal recovery. The respiratory tract is primarily involved in all four models. Involvement of intestines was also seen in at least one study in each animal. Transfer to naïve animals in close contact has been documented in case of hamsters and ferrets. Seroconversion was documented in all although the role of convalescent sera was tested in hamsters only, with positive results though. Air-borne transmission was documented in ferrets and the possibility of feco-oral transmission was suggested for hamsters. The possibilities of recurrence and re-infection were ruled out by experiments upon the rhesus macaques. The fulfilment of Koch’s postulates has been highlighted. Discussion: The various studies available on animal models have been able to establish models of infection and transmission that recapitulate different aspects of disease in humans. However, the response between different animals and the same animal in different experiments is not completely coherent. Some of them do not manifest the disease clinically while others behave differently at molecular and immunological levels. Moreover, the physiology of these animals is not identical to human beings and the findings may not be extrapolated to human beings in an ‘as-is’ manner. Conclusions: The review acknowledges the achievements made by these experiments in a short span of time and highlighted the urgent need for a deeper dive in search of a quintessential animal model which can be studied for efficacy and safety of newer drugs and vaccines before a make-shift from the petri-dish to the human body can be contemplated.

The widely spread CoronaVirus Disease (COVID)- 19 is one of the worst infectious disease outbreaks in history and has become an emergency of primary international concern. As the pandemic evolves, academic communities have been actively involved in various capacities, including accurate epidemic estimation, fast clinical diagnosis, policy effectiveness evaluation and development of contract tracing technologies. There are more than 23,000 academic papers on the COVID-19 outbreak, and this number is doubling every 20 days while the pandemic is still on-going [1]. The literature, however, at its early stage, lacks a comprehensive survey from a data analytics perspective. In this paper, we review the latest models for analyzing COVID19 related data, conduct post-publication model evaluations and cross-model comparisons, and collect data sources from different projects.

In this paper we provide an ``expository overview" of classic epidemic projection models. Starting with the simple case of an epidemic that grows exponentially we then investigate ``compartmental" models. These assume that the growth of an infected population is limited endogenously by the size of the underlying pool of susceptibles. We then describe a new family of so-called "Exo-r" statistical models, which hinge on an exogenously driven growth rate of the infected population. This family, which can be used to model both infections and deaths, captures parsimoniously both the depletion of susceptibles and the effect of interventions such as lockdowns and ``social distancing". The model is used to fit numbers of Covid-19 infections in China. It is also used to model and project deaths in the United States. Results are used to inform a discussion on i) the challenges at hand and ii) the extent to which epidemic projection models may be useful despite being wrong.

COVID-19 is the most severe health crisis of the 21st century. COVID-19 presents a threat to almost all countries world-wide. The restriction of human mobility is one of the strategies used to control the transmission of COVID-19. However, it has yet to be determined how effective this restriction is in controlling the rise in COVID-19 cases, particularly in major capital cities such as Jakarta, Indonesia. Using Facebook's mobility data, our study explores the impact of restricting human mobility on COVID-19 case control in Jakarta. Our main contribution is showing how the restriction of human mobility data can give important information about how COVID-19 spreads in different places. We proposed modifying a global regression model into a local regression model by accounting for the spatial and temporal interdependence of COVID-19 transmission across space and time. We applied Bayesian hierarchical Poisson spatiotemporal models with spatially varying regression coefficients. We estimated the regression parameters using an Integrated Nested Laplace Approximation. We found that the local regression model with spatially varying regression coefficients outperforms the global regression model based on DIC, WAIC, MPL, and R2 criteria for model selection. In Jakarta's 44 districts, the impact of human mobility varies significantly. The impacts of human mobility on the log relative risk of COVID-19 range from –4.445 to 2.353. The prevention strategy involving the restriction of human mobility may be beneficial in some districts but ineffective in others. Therefore, a cost-effective strategy had to be adopted.

In Wuhan city of China, an episode of novel coronavirus (COVID-19) happened. during late December and it has quickly spread to all places in the world. Until May 29, 2020, cases were high in the USA with 1.7 Million, Russia with approximately 387 thousand, the UK with 271 thousand confirmed cases. Everybody on the planet is anxious to know when the coronavirus pandemic will end. In this scourge, most nations force extreme medication measures to contain the spread of COVID-19. Modeling has been utilized broadly by every national government and the World Health Organization in choosing the best procedures to seek after in relieving the impacts of COVID-19. Many epidemiological models are studied to understand the spread of the illness and its prediction to find maximum capacity for human-to-human transmission so that control techniques can be adopted. Also, arrangements for the medical facilities required such as hospital beds and medical supplies can be made in advance. Many models are used to anticipate the results keeping in view the present scenario. There is an urgent need to study the various models and their impacts. In this study, we present a systematic literature review on epidemiological models for the outbreak of novel coronavirus in India. The epidemiological dynamics of COVID-19 is also studied. Here, In addition, an attempt to take out the results from the exploration and comparing it with the real data. The study helps to choose the models that are progressive and dependable to predict and give legitimate methods for various strategies.

This paper reviews the management of the COVID-19 crisis and the difficulty of cost estimation model, comparing centralized management or bureaucratic government coaction and the agile market alternative or spontaneous social coordination. This is a study of Political Economy and Health Economics from the perspective of Austrian Economics. We describe and compare the al-ternative models, which are adapted to the current crisis. The analysis is based on the theorem of the impossibility of the economic calculation under coactive systems, and other principles of economy. In this context we pay also attention to collateral problems of the centralized and coac-tive management. Finally we propose a solution based on dynamic efficiency and the constitutions of wellbeing economics

Abstract: The objective of this meta-analysis was to evaluate the factors associated with mortality of elderly Italians diagnosed with the new coronavirus who resided in institutions or who were hospitalized as a result of the disease. Methods: A systematic review following the recommenda-tions of The Joanna Briggs Institute (JBI), where the PEO strategy was utilized - Population, Exposure and Outcome. P, being the elderly over 65 years old. E, the SARS-CoV_2 pandemic. O, mortality. The NCBI / PubMed, LILACS, EMBASE and CINAHL databases were used until July 31, 2020.; Results: Five Italian studies were included in the meta-analysis, with the number of elderly people varying between 18 and 1591 patients. The main morbidities presented by the elderly in the studies were: dementia, diabetes, chronic kidney disease and hypertension. Conclusions: The factors as-sociated with the mortality of elderly Italian people diagnosed with SARS-CoV-2 who lived in in-stitutions or who were hospitalized because of the disease were evaluated. It was found that de-mentia, diabetes, chronic kidney disease and hypertension are the main the main diagnosed dis-eases for mortality in elderly people with Covid-19.

Human serum albumin (HSA) is the ultimate, homeostatic determinant of fluid volume (FV) in all compartments of the body. The ability to change the pressure and flow of nutrient medium to cellular structures selectively has clinical significance in almost all areas of clinical medicine. We have shown that present fluid therapy (FT) using either colloid or saline dilutes nutrients. HSA binds and delivers nutrients, a lack of HSA binding causes Albumin Binding Disease (ABD) changing both colloidal pressure and nutrients leading to symptoms of sepsis. A reduction in HSA binding can be through lack of HSA or external ligands taking up binding sites on HSA. Many diseases cause ABD when immunoglobulins, infectious particles and by products bind to HSA including COVID-19 and other bacterial diseases like streptococcus as well as metabolic and cardiac disease due to incorrect pressure and nutrients. To raise HSA binding potential and remove vulnerabilities to these diseases we propose raising HSA. We show how present methods of fluid therapy (FT) are inefficient in that control of both pressure and nutrients forms initial equilibrium in the liver. HSA production and binding to nutrients is maintained at a level by the pressure in hepatocytes with the pancreatic and adrenal hormones moderating. Glucose and Ke-tone production are critical in the management of HSA binding and control of blood, colloidal pressure, and health of cells. Ketones provide a distinct mechanism of liver to lungs-heart metabolism during hypoglycaemia and activity. This known chain of nutrition becomes critical during disease like acute respiratory distress syndrome (ARDS) when ketones may be overproduced. HSA must therefore be infused into the liver to raise HSA levels safely. We investigated the control of HSA as a product of the actions of pressure in hepatocytes and changes in pancreatic and adrenal hormones. We found that in addition to pressure during glucose abundancy HSA is re-leased with ketones on administration of insulin and consider that this mechanism retains HSA moderation. HSA can therefore be increased by increasing insulin in the presence of glucose in the correct amounts. We suggest a protocol to increase FV, blood, lymph volume by infusion of HSA, insulin, and glucose direct to the liver so that the level of HSA stabilises. We suggest this will raise all other nutrients in the new FV, blood, lymph culminating in the creation of new blood cells. We propose that in a clinical situation maintaining this protocol will alleviate the symptoms of ABD and decrease the likelihood of serious illness and death.

All vaccines exhibit both specific and non-specific effects. The specific effects are measured by the efficacy against the target pathogen, while the non-specific effects can be detected by the change in all-cause mortality . All-cause mortality data (gender, age band, vaccination history, month of death) between January 2021 and May 2022 was compiled by the Office for National Statistics. COVID–19 vaccination gave good protection on many occasions but less so for younger ages. Each gender and age group shows its own unique vaccination benefit/disbenefit time profile. Individuals are free to make vaccination decisions. For example, women aged 18-39 show a cohort who do not progress beyond the first or second dose. The all-cause mortality outcomes for the Omicron variant showed a very poor response to vaccination with 70% of sex/age/vaccination stage/month combinations increasing all-cause mortality, probably due to unfavorable antigenic distance between the first-generation vaccines and this variant, and additional non-specific effects. The all-cause mortality outcomes of COVID–19 vaccination is far more nuanced than have been widely appreciated, and virus vector appear better than the mRNA vaccines in this specific respect. The latter are seemingly more likely to increase all-cause mortality especially in younger age groups. An extensive discussion/literature review is included to provide potential explanations for the observed unexpected vaccine effects.

In the existing group decision-making fuzzy analytic hierarchy process (FAHP) methods, the consensus among experts has rarely been fully reached. To fill this gap, in this study, a pre-aggregation fuzzy collaborative intelligence (FCI)-based FAHP approach is proposed. In the proposed pre-aggregation FCI-based FAHP approach, fuzzy intersection is applied to aggregate experts’ pairwise comparison results if these pairwise comparison results overlap. The aggregation result is a matrix of polygonal fuzzy numbers. Subsequently, alpha-cut operations are applied to derive the fuzzy priorities of criteria from the aggregation result. The pre-aggregation FCI-based FAHP approach has been applied to select suitable alternative suppliers for a wafer foundry in Taiwan amid the COVID-19 pandemic. The experimental results revealed that the pre-aggregation FCI-based FAHP approach significantly reduced the uncertainty inherent in the decision-making process by deriving fuzzy priorities with very narrow ranges.

Abstract: A primary action of vitamin D is regulation of gene transcription. Many cell types possess genes that make antimicrobial peptides (AMPS) (endogenous antibiotics), recently discovered to be regulated by vitamin D. Two examples are cathelicidin and beta defensins, both bioactive against many different bacteria, fungi, mycobacteria, parasites and viruses. The signal transduction pathway is triggered by sensing microorganisms via cell surface receptors, causing intracellular production of calcitriol (1,25(OH)2D) and vitamin D receptors, leading to upregulation of AMP production. Serum 25(OH)D concentrations required to sustain adequate AMP production to eradicate infections are unknown. Vitamin D3 is photosynthesized in skin in amounts ranging from 10,000 (250 mcg) to 25,000 (625 mcg) International Units (IU) from 7-dehydrocholesterol after whole-body exposure to one minimal erythemal dose (MED) of ultraviolet B (UVB) radiation, and is impacted by many factors including geographic localities, seasonal changes and skin pigmentation. We and others have reported extended daily oral dosing with these amounts of vitamin D3 safe. We routinely observe serum 25(OH)D concentrations below 20ng/ml on new admissions, which have been reported insufficient to sustain AMP production. In contrast serum 25(OH)D concentrations above 100ng/ml have been reported after serial UVB treatments for psoriasis. Little vitamin D naturally occurs in food, and insufficient sun exposure may be causing worldwide deficiency. We review evidence suggesting that higher daily intakes of vitamin D3 than the currently recommended 600 (15 mcg) IU/day may be necessary to sustain AMP production in the face of an overwhelming infection, particularly in non-Hispanic blacks, a high risk population suffering the worst outcomes from COVID-19. We propose that increased vitamin D supplementation could provide a safe and cost-effective way to protect all populations from infections, in particular those from pandemic COVID-19.

Evidence was brought forward in England and the USA that Black, Asian, Latino and Minority Ethnic people exhibit higher mortality risk from COVID-19 than White people. While socioeconomic factors were suggested to contribute to this trend, they arguably do not explain the range of the differences observed, allowing for possible genetic implications. Almost concurrently, the analysis of a cohort in Chinese COVID-19 patients proposed an association between the severity of the disease and the presence of the minor allele of rs12252 of the Interferon-induced transmembrane protein 3 (IFITM3) gene. This SNP, together with rs34481144, are the two most studied polymorphisms of IFITM3 and have been associated in the past with increased severity in Influenza, Dengue, Ebola, and HIV viruses. IFITM3 is an immune effector protein that is pivotal for the restriction of viral replication, but also for the regulation of cytokine production. Following up to these two developments in the ongoing SARS-CoV-2 pandemic, the present study investigates a possible association between the differences in mortality of ethnic groups in England and the combined haplotypes of rs12252 and rs34481144. The respective allele frequencies were collected for 26 populations from 1000 Genomes Project and subgroups were pooled wherever possible to create correspondences with ethnic groups in England. A significant correlation (r=0.9687, p= 0.0003) was observed between the reported Standardized Mortality Ratios and the frequency of the combined haplotype of both reference alleles, suggesting that the combination of reference alleles of the specific SNPs may be implicated in more severe outcomes of COVID-19. This study calls for further focus on the role of IFITM3 variants in the mechanism of cellular invasion of SARS-CoV-2, their impact in COVID-19 severity and their possible implications in vaccination efficacy.

The COVID-19 pandemic has presented an unprecedented challenge to the healthcare system. Identifying the genomics and clinical biomarkers for effective patient stratification and management is critical to controlling the spread of the disease. Omics datasets provide a wealth of information that can aid in understanding the underlying molecular mechanisms of COVID-19 and identifying potential biomarkers for patient stratification. Artificial intelligence (AI) and machine learning (ML) algorithms have been increasingly used to analyze large-scale omics and clinical datasets for patient stratification. In this manuscript, we demonstrate the recent advances and predictive accuracies in AI and ML-based patient stratification modeling linking omics and clinical biomarker datasets, focusing on COVID-19 patients. Our ML model not only demonstrates that clinical features are enough an indicator of COVID-19 severity and survival but also infers what clinical features are more impactful, which makes our approach a useful guide for clinicians for prioritization of best-fit therapeutics for a given cohort of patients. Moreover, with weighted gene network analysis, we are able to provide insights into gene networks that have a significant association with COVID-19 severity and clinical features. Finally, we have demonstrated the importance of clinical biomarkers in identifying high-risk patients and predicting disease progression.

COVID-19 transmission models have conferred great value in informing public health understanding, planning, and response. However, the pandemic also demonstrated the infeasibility of basing public health decision-making on transmission models with pre-set assumptions. No matter how favourably evidenced when built, a model with fixed assumptions is challenged by numerous factors that are difficult to predict. Ongoing planning associated with rolling back and re-instituting measures, initiating surge planning, and issuing public health advisories can benefit from approaches that allow state estimates for transmission models to be continuously updated in light of unfolding time series. A model being continuously regrounded by empirical data in this way can provide a consistent, integrated depiction of the evolving underlying epidemiology and acute care demand, offer the ability to project forward such a depiction in a fashion suitable for triggering the deployment of acute care surge capacity or public health measures, support quantative evaluation of tradeoffs associated with prospective interventions in light of the latest estimates of the underlying epidemiology. We describe here the design, implementation and multi-year daily use for public health and clinical support decision-making of a particle filtered COVID-19 compartmental model, which served Canadian federal and provincial governments via regular reporting starting in June 2020. The use of the Bayesian Sequential Monte Carlo algorithm of Particle Filtering allows the model to be re-grounded daily and adapt to new trends within daily incoming data – including test volumes and positivity rates, endogenous and travel-related cases, hospital census and admissions flows, daily counts dose-specific vaccinations administered, measured concentration of SARS-CoV-2 in wastewater, and mortality. Important model outputs include estimates (via sampling) of the count of undiagnosed infectives, the count of individuals at different stages of the natural history of frankly and pauci-symptomatic infection, the current force of infection, effective reproductive number, and current and cumulative infection prevalence. Following a brief description of model design, we describe how the machine learning algorithm of particle filtering is used to continually reground estimates of dynamic model state, support probabilistic model projection of epidemiology and health system capacity utilization and service demand and probabilistically evaluate trade-offs between potential intervention scenarios. We further note aspects of model use in practice as an effective reporting tool in a manner that is parameterized by jurisdiction, including support of a scripting pipeline that permits a fully automated reporting pipeline other than security-restricted new data retrieval, including automated model deployment, data validity checks, and automatic post-scenario scripting and reporting. As demonstrated by this multi-year deployment of Bayesian machine learning algorithm of particle filtering to provide industrial-strength reporting to inform public health decision making across Canada, such methods offer strong support for evidence-based public health decision making informed by ever-current articulated transmission models whose probabilistic state and parameter estimates are continually regrounded by diverse data streams.

Systemic capillary leak syndrome (SCLS) is an uncommon, potentially life-threatening disorder defined as recurrent attacks of pseudo-shock. This syndrome occurs due to the disruption of endothelial cells, which leads to increased vascular permeability, causing intravascular fluid to leak into the extravascular space and albumin to be retained in the interstitial space. SCLS can lead to hypovolemia, peripheral hypoperfusion, and acute renal insufficiency. The syndrome is presented with fever, generalized edema, pleural effusions, dyspnea, hypovolemia, hemoconcentration, prerenal azotemia, shock, and syncope. After ruling out other causes of hypovolemic shock, the diagnosis of SCLS can be considered on the presence of the classical triad of hypotension, hemoconcentration, and hypoalbuminemia. Eliminating the precipitating factors is the cornerstone of SCLS management. It is advisable to be very cautious and weigh the risks and benefits of vaccination of people with a history of this condition. This review will discuss and compare different aspects of SLCS after SARS-CoV-2 infection and COVID-19 vaccination.

The severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) Omicron variant of concern has been the dominant cause of worldwide COVID-19 cases since 2022. All the Omicron sublineage viruses have demonstrated high transmissibility and an ability to escape vaccine-induced immunity. While first-generation vaccines, including monovalent vaccines, continue to provide protection against severe disease, hospitalization and mortality, their efficacy against Omicron sub variants remains sparse. These vaccines have also been associated with rapidly waning protection against primary COVID-19 and COVID-19 reinfections conferred by evolving Omicron sublineages.. This led to the development and deployment of updated vaccines and the introduction of the bivalent booster. Through this review, we highlight the brief journey of the variants of concern leading to the dominance of Omicron and the effectiveness of the key vaccines against these variants, including the updated (bivalent) boosters.

Objective: We aim to assess the reporting discrepancy and the difference between confirmed and unreported COVID-19-like death counts.Study Design: The study is based on time-series data.Methods: We used publicly available data to explore the differences between confirmed death counts and deaths with Codiv-19 symptoms between March 8, 2020, and July 11, 2020, in Bangladesh.Results: During the week ending May 9, 2020, the unreported COVID-19-like death count was higher than the confirmed COVID-19 death count; however, it was lower in the following weeks. On average, unreported COVID-19-like death counts were similar to the confirmed COVID-19 death counts during the same period. However, the reporting authority neither considers these deaths nor adjusts for potential seasonal influenza or other related deaths, which might produce incomplete COVID-19 data and respective mortality rates. Conclusions: Documenting unreported deaths with COVID-19 symptoms needs to be included in provisional death counts because it is essential to estimate a robust COVID-19 mortality rate and to offer data-driven pandemic response strategies. An urgent initiative is needed to prepare an acceptable guideline for COVID-19 death reporting.

Objectives To develop and validate a prediction model for 28-day in-hospital mortality among adult patients critically ill with COVID-19 in the UK. Design Observational cohort study. Setting 287 adult critical care units in England, Wales and Northern Ireland, of which 260 admitted at least one eligible patient. Participants 10,933 patients with confirmed COVID-19 of whom 10,401 were eligible (excluding 532 patients with a duration of critical care less than 24 hours and 1 patient with unknown 28-day outcome): 8,666 development (March-April 2020) and 1,735 temporal validation (May-August 2020). Main outcome measures 28-day in-hospital mortality from start of critical care. Results Two models were developed using 14 patient level predictors selected from 30 candidate predictors, with and without adjustment for calendar time. In the temporal validation data, the model discrimination was maintained (c index 0.78) but calibration was poor, particularly for the model not adjusted for calendar time (ratio of observed to predicted mortality 0.74 versus 0.88 for the model adjusted for calendar time). Conclusions We developed and validated a prediction model for 28-day in-hospital mortality for patients critically ill with COVID-19. Although absolute predictions were inaccurate due to changing outcomes, the models will support risk-adjustment in analyses and monitoring changes in risk-adjusted outcome over time.

Background: This study aimed to examine the course of Depression and anxiety in COVID-19 survivors with a psychiatric history compared with those without a psychiatric history. Methods: A web-based cross-sectional survey for COVID-19 survivors was conducted from July to September 2021. 6016 COVID-19 survivors, the accuracy of whose responses was determined to be assured, were included in analyses. Exposures included psychiatric history and time since COVID-19 infection, and the main outcomes and measures included severity of depression and anxiety, as assessed using the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7), respectively. Results: Mean severity of PHQ-9 and GAD-7 were significantly higher in participants with a psychiatric history than in those without a psychiatric history. Two-way analysis of covariance for PHQ-9 showed a significant main effect of the presence of psychiatric history and a significant interaction effect of psychiatric history × time since infection. Two-way analysis of covariance for the GAD-7 score revealed a significant main effect of the presence of psychiatric history and time since COVID-19 infection and the interaction effect of these factors. Conclusions: The course of depression and anxiety was more severe in COVID-19 survivors with a psychiatric history than in those without a psychiatric history.

Introduction: On March 11, 2020, the World Health Organization (WHO) declared Coronavirus disease 2019 (COVID-19) as a pandemic. The spread of the Delta variant of coronavirus started in June 2021 and accounted for the fifth peak of COVID-19 in Iran in July 2021. According to reports from other countries, vaccination protects against severe diseases caused by COVID-19, including the Delta variant. Studies have also shown that vaccination provides strong protection against SARS-CoV-2 infection, COVID-19-related hospitalization, and mortality. This retrospective cohort study was designed based on the medical care monitoring center (MCMC) database of Sayyad Shirazi Hospital.Methods: The following patient data were extracted for this study: demographic characteristics, signs and symptoms, ICU admission, need for aggressive oxygen therapy, including intubation, mortality, and vaccination status. Results: Being vaccinated was associated with a 4.14-fold increase in survival (adjusted OR=4.14; 95% CI: 2.22 to 7.69; P<0.01), and individuals in a younger age group demonstrated a 5.58-fold higher likelihood of surviving (adjusted OR=5.58; 95% CI: 4.25 to 8.14; P<0.01). The risk of severe COVID-19 was significantly lower in vaccinated individuals, showing a 3.12-fold decrease in risk (adjusted OR=3.12; 95% CI: 2.06 to 4.72; P<0.01), and in younger age groups, the risk exhibited a 3.28-fold decrease (adjusted OR=3.28; 95% CI: 2.66 to 4.04; P<0.01).Conclusion: The present results suggest that receiving at least one dose of COVID-19 vaccine had a significant relationship with decreased COVID-19 severity and mortality in vaccinated patients compared to unvaccinated patients.

The COVID-19 pandemic has exerted a significant impact on mental health globally. The uncertainty, fear, and stress associated with this crisis have contributed to a heightened prevalence of anxiety, depression, and various other mental health disorders. In this scenario, the present study aimed at investigating the relationship between locomotion regulatory mode, resilience, and COVID-19 anxiety. It is worth noting that previous extensive research has established a significant correlation between high levels of locomotion and diverse positive psychological conditions, such as optimism, reduced hopelessness, and positive affect. A total of 243 participants completed measures of locomotion regulatory mode, resilience, and COVID-19 anxiety. In line with our hypotheses, individuals' locomotion regulatory mode was negatively, although non significatively, associated with COVID-19 anxiety. Furthermore, resilience was found to mediate the relationship between locomotion regulatory mode and COVID-19 anxiety, indicating that individuals displaying high locomotion may be better equipped to cope with the stress and uncertainty of the COVID-19 pandemic due to their greater levels of resilience. Taken together, these findings highlight the importance of considering both locomotion and resilience in managing anxiety related to COVID-19 and suggest that interventions aimed at enhancing resilience may be beneficial particularly for individuals with low locomotion regulatory mode.

ACE2 impact on the severity of COVID-19 is widely discussed but still controversial. To estimate its role in aspects of the main risk factors and comorbidities, we involved post-COVID-19 patients in Ternopil region (Ukraine). Recruitment period was July 2020 to December 2021. Medical records, treatment modalities and outcomes were recorded and analyzed. Serum human ACE2 protein was measured with Cusabio ELISA kits (Houston, TX, USA). Statistical analysis was performed with SPSS21.0 software (SPSS Inc., Chicago, IL, USA). The level of ACE2 serum protein was significantly higher (p < 0.001) in patients with mild symptoms compared to more severe course of disease, and inversely had changed from 1 to 90 days after recovery. In patients with mild COVID-19, ACE2 level significantly decreased over time, while among critical patients, it increased by 34.1percent. Such results could be explained by ACE2 shedding from tissues into circulation. Loss of the membrane-bound form of the enzyme decreases the virus entry into cells. Our studies did not identify any sex-related ACE2 serum levels correlation. The most common comorbidities were hypertension, cardiovascular diseases, respiratory diseases and diabetes mellitus. All comorbidities except respiratory diseases contribute to the severity of disease and correlate with ACE2 blood serum level.

Background: Early-stage interventions in a potential pandemic are important tounderstand as they can make the difference between runaway exponential growththat is hard to turn back and stopping the spread before it gets that far. COVID-19 is an interesting case study because there have been very different outcomesin different localities. These variations are best studied after the fact if precisionis the goal; while a pandemic is still unfolding less precise analysis is of value inattempting to guide localities in the early stages to learn lessons of those that pre-ceded them. Methods: I examine three factors that could differentiate strategy: asymptomaticspread, differences in use of the Bacillus Calmette-Guerin (BCG) tuberculosis vac-cine and cloth face masks.Results:Differences in disease progression as well as the possibility of alternativestrategies to prevent COVID-19 from entering the runaway phase or damping itdown later can be elucidated by a study of asymptomatic infection. A study todemonstrate not only what fraction are asymptomatic but how contagious they arewill also inform policy on universal mask wearing. Conclusions: While a COVID-19 outbreak is at a level that makes accurate trace-and test possible, investigation of asymptomatic transmission is viable and shouldbe attempted to enhance understanding of spread and variability in the disease aswell as policy options for slowing the spread.

Background Recently, a surge of COVID 19 was observed globally, regionally and nationally. With increasing numbers of cases, the frequency of long COVID is on the rise. Management and control of long COVID depend on changes in respect of human behaviors and requires an understanding of knowledge, attitudes, and practices (KAP) regarding health threats. MethodsA descriptive cross sectional study using online survey to gather data on the socio-economic background, knowledge, attitudes and practices on long-term complications of COVID. Results: Out of 201 respondents, 89.2% participants have heard about long-term complications of COVID 19. Only 35.9% have demonstrated adequate knowledge in the questions relating to co-morbidities and risk factors of COVID-19. A total of 92.2% believe that they should adhere to preventive measures following vaccination. Less than 60 % were following the advice on avoiding unnecessary travel and crowded places. Further, less than 50% were following COVID preventive measures. ConclusionAlthough the majority of participants have heard about long-term complications and common symptoms, the knowledge regarding co-morbidities that can lead to severe disease and long COVID was not satisfactory. The attitudes of the participants indicated increasing concern about long COVID. Practices indicate lack of adherence to key measures such as avoiding crowded places. These findings highlight the need for further increasing of awareness.

Introduction: Despite more than one year passed since the first cases of SARS-CoV-2 were reported, there is still no consensus on the definition and clinical management of post-acute-COVID-19. The condition has heterogeneously been named as Chronic COVID syndrome, Post COVID-19 Syndrome, post-acute sequela of SARS-CoV-2 (PASC), and the more familiar long COVID. Method: In order to capture all relevant published studies, we undertook a multi-step search with no language restriction. The following four-step search strategy was utilized: First, a preliminary (limited) search was conducted on January 20, 2021, in Google Scholar and PubMed to identify the appropriate keywords. Then, on January 30, 2021, we adopted a search strategy of electronic databases from Cochrane Library, PsycINFO, PubMed, Embase, Scopus, and Web of sciences, using those keywords. Then, after duplicate removal, we screened all titles, abstracts, and full texts. This resulted in 66 eligible studies. Subsequently, after a forward and backward search of their references and citations an additional 54 publications were found, resulting in a total of 120 publications that formed the basis of the present analysis. The titles, abstracts, and full-texts of non-English articles were translated using Google Translate for further evaluation. We conducted our scoping review based on the PRISMA-ScR Checklist.Results: We found only one randomized clinical trial in our search. Of the 67 original studies, 22 were cohort and 28 were cross-sectional studies totaling 74.6% of the original studies. Of the total of 120 publications, 59 (49.1%) focused on signs and symptoms, 28 (23.3%) were focused on management, and 13 (10.8%) focused on pathophysiology. Ten (9%) publications focused on imaging studies. Ninety-one percent of the original investigations came from high and upper-middle-income countries, highlighting the scarcity of reports originating from low-income and lower-middle-income countries.Conclusion: The predominant symptoms among those with the so-called “Long COVID” were: fatigue, breathlessness, arthralgia, sleep difficulties, and chest pain. Recent reports also point to the risk of long-term sequela with cutaneous, respiratory, cardiovascular, musculoskeletal, mental health, neurologic, and renal involvement in those who survive the acute phase of the illness. The ambiguity and controversies in its definition have impaired proper recognition and management of those requiring additional support following the resolution of the acute phase of this infection. This has resulted in long-standing distress for the patients and their families. Our findings highlight the need for a multidisciplinary approach, support, and rehabilitation for these patients in terms of long-term mental and physical health.