Version 1
: Received: 10 May 2020 / Approved: 10 May 2020 / Online: 10 May 2020 (17:10:15 CEST)
How to cite:
Wagh, C.S.; Mahalle, P.N.; Wagh, S.J. Epidemic Peak for COVID-19 in India, 2020. Preprints2020, 2020050176. https://doi.org/10.20944/preprints202005.0176.v1
Wagh, C.S.; Mahalle, P.N.; Wagh, S.J. Epidemic Peak for COVID-19 in India, 2020. Preprints 2020, 2020050176. https://doi.org/10.20944/preprints202005.0176.v1
Wagh, C.S.; Mahalle, P.N.; Wagh, S.J. Epidemic Peak for COVID-19 in India, 2020. Preprints2020, 2020050176. https://doi.org/10.20944/preprints202005.0176.v1
APA Style
Wagh, C.S., Mahalle, P.N., & Wagh, S.J. (2020). Epidemic Peak for COVID-19 in India, 2020. Preprints. https://doi.org/10.20944/preprints202005.0176.v1
Chicago/Turabian Style
Wagh, C.S., Parikshit N. Mahalle and Sanjeev J. Wagh. 2020 "Epidemic Peak for COVID-19 in India, 2020" Preprints. https://doi.org/10.20944/preprints202005.0176.v1
Abstract
In India the first case of coronavirus disease 2019 (COVID-19) reported on 30 January 2020, and thereafter cases were increasing daily after the last week of Feb. 2020. COVID-19 identified as family member of coronaviridae where previously Middle East Respiratory Syndrome MERS and Severe Acute Respiratory Syndrome SARS belongs to same family. The COVID-19 attacks on respiratory system signing fever, cough and breath shortness, in severe cases may cause pneumonia, SARS or some time death. The aim of this study work is to develop model which predicts the epidemic peak for COVID-19 in India by using the real-time data from 30 Jan to 10 May 2020. There are uncertainties while identifying the population information due to the incomplete and inaccurate data, we initiate the most popular model for epidemic prediction i.e Susceptible, Exposed, Infectious, & Recovered SEIR initially the compartmental model for the prediction. Based on the solution of the state estimation problem for polynomial system with Poisson noise, we estimate that the epidemic peak may reach the early-middle July 2020, initializing recovered R0 to 0 and Infected I0 to 1. The outcomes of the model will help epidemiologist to isolate the source of the disease geospatially and analyze the death. Also government authorities will be able to target their interventions for rapidly checking the spread of the epidemic.
Computer Science and Mathematics, Computer Science
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.
