Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Jun 9, 2020
Date Accepted: Sep 22, 2020
Date Submitted to PubMed: Oct 14, 2020
Discrete SIR modelling using empirical infection data shows that SARS-CoV-2 infection provides short-term immunity
ABSTRACT
The novel coronavirus SARS-CoV-2, which causes the COVID-19 disease, is now a global pandemic. Since December 2019, it has infected millions of people, caused the deaths of hundreds of thousands, and resulted in incalculable social and economic damage. Understanding the infectivity and transmission dynamics of the virus is essential for understanding how best to reduce mortality whilst ensuring minimal social restrictions to the lives of the general population. Anecdotal evidence is available, but detailed studies have not yet revealed whether infection with the virus results in immunity. In this work, we have extended the generic SIR framework to use random processing based on empirical infection and fatality data from different regions, in order to investigate the reinfection frequency of the disease. Our model predicts that cases of reinfection should have been observed by now if primary SARS-CoV-2 infection did not protect from subsequent exposure in the short term, however, no such cases have been documented. This work therefore provides a useful insight for serological testing strategies, lockdown easing and vaccine design.
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