Abstract

The outbreak of the novel coronavirus has resulted in significant morbidity and mortality in the affected 210 countries with about 2.4 million people infected and over 163 thousand deaths. The SARS-CoV-2 spike protein is effective at binding to human cells, but this SARS-CoV-2 backbone differed substantially from those of already known coronaviruses and mostly resembled related viruses found in bats and pangolins. To help predict the possible dynamics of COVID-19 as well as ways to contain it, this paper develops a mathematical model for the disease, which includes two different infectious routes. The model’s predictions are fitted to data from the outbreaks in New York State from March 1 2020 first report to April 19 2020. However, the containment time and the severity of the outbreaks depend crucially on the contact coefficients and the isolation rate constant. When randomness is added to the model coefficients, the simulations show that the model is sensitive to the scaled contact rate among people and to the isolation rate. The model is analyzed using stability theory for ordinary differential equations and indicates that when using only isolation for control and advising self recovery,the endemic steady state is locally stable and attractive. After the April 14 2020 highest peak of COVID-19 new infections by the SARS-CoV-2 virus will slow down from the beginning of May at New York State if people will keep the isolation. Numerical simulations with parameters estimated from New York State illustrate the analytical results and the model behavior, which may have important implications for the disease containment in other cities. Indeed, the model highlights the importance of isolation of infected individuals and advising self recovery may be used to assess other control measures. The model is general and may be used to analyze outbreaks in other states of the United States and other countries.

Keywords: CoVID-19; SARS-CoV-2; NY Population; SEIQRD Model; Peak Prediction; Dynamical System; Reproduction Number

References

1. AB Gumel., et al. “Modelling strategies for controlling SARS outbreaks”. Proceedings of the Royal Society 1554 (2004): 2223-2232.
2. G Chowell., et al. “Synthesizing data and models for the spread of MERS-CoV, 2013: key role of index cases and hospital transmission”. Epidemics 9 (2014): 40-51.
3. ZQ Xia., et al. “Modeling the transmission of Middle East Respirator Syndrome Corona Virus in the Republic of Korea”. PLoS ONE12 (2015): 1-13.
4. Durgesh Sinha and Ankur Sinha. “Mathematical Model of Zoonotic NIPAH virus in South-East Asia Region". Acta Scientific Microbiology9 (2019): 82-89.
5. https://www.indexmundi.com/g/g.aspx?c=us&v=25
6. https://www.indexmundi.com/g/g.aspx?v=26&c=us&l=en
7. https://www.worldometers.info/world-population/us-population/
8. health.ny.gov
9. https://coronavirus.health.ny.gov/past-coronavirus-briefings
10. https://en.wikipedia.org/wiki/2020_coronavirus_pandemic_in_New_York_(state)
11. Sinha Durgesh and Tan Peiwen. Mathematical Model and Simulations of COVID-19 2020 Outbreak in New York: Predictions and Implications for Control Measures (2020).

Citation

Citation: Durgesh Nandini Sinha and Peiwen Tan. “Mathematical Model and Simulations of COVID-19 2020 Outbreak in New York: Predictions and Implications for Control Measures". Acta Scientific Microbiology 3.8 (2020): 07-14.

Copyright

Copyright: © 2020 Durgesh Nandini Sinha and Peiwen Tan. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.