Version 1
: Received: 28 March 2020 / Approved: 29 March 2020 / Online: 29 March 2020 (11:25:23 CEST)
How to cite:
Nnolim, U. A Combination-Based Approach to COVID-19 Symptom Severity Management. Preprints2020, 2020030435. https://doi.org/10.20944/preprints202003.0435.v1
Nnolim, U. A Combination-Based Approach to COVID-19 Symptom Severity Management. Preprints 2020, 2020030435. https://doi.org/10.20944/preprints202003.0435.v1
Nnolim, U. A Combination-Based Approach to COVID-19 Symptom Severity Management. Preprints2020, 2020030435. https://doi.org/10.20944/preprints202003.0435.v1
APA Style
Nnolim, U. (2020). A Combination-Based Approach to COVID-19 Symptom Severity Management. Preprints. https://doi.org/10.20944/preprints202003.0435.v1
Chicago/Turabian Style
Nnolim, U. 2020 "A Combination-Based Approach to COVID-19 Symptom Severity Management" Preprints. https://doi.org/10.20944/preprints202003.0435.v1
Abstract
The recent outbreak and spread of the COVID-19 virus infection across the world has seen a massive global system-wide shutdown of human social and economic activity. Both developed and developing nations have been forced to contain and isolate their citizens as much as possible. However, the continuous rising cases in both categories of nations, especially those with poor or nonexistent testing facilities and healthcare systems pose a hidden danger. The seemingly lack of and access to a truly global concerted research effort in both temporary but effective symptom mitigation may lead to more deaths in infected cases. We propose that a fusion of both technological and home-grown solutions can be utilized effectively to manage symptoms. This would add to the preventive methods of social distancing, isolation, quarantine and frequent handwashing to halt the impact of the disease. We also hope to spur further research in such drug/non-drug combination therapy-based methods with emphasis on effectiveness based on quality of nutrient sources.
Medicine and Pharmacology, Epidemiology and Infectious Diseases
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
