Discrete SIR modelling using empirical infection data shows that SARS-CoV-2 infection provides short-term immunity

Background The novel coronavirus SARS-CoV-2, which causes the COVID-19 disease, has resulted in a global pandemic. Since its emergence in December 2019, the virus 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. Objective The objective of the study was to use mathematical modelling to investigate the reinfection frequency of COVID-19. Methods We have used the SIR (Susceptible, Infected, Recovered) framework and random processing based on empirical SARS-CoV-2 infection and fatality data from different regions to calculate the number of reinfections that would be expected to occur if no immunity to the disease occurred. Results 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. Conclusions This work concludes that infection with the SARS-CoV-2 virus provides short-term immunity to reinfection and therefore provides a useful insight for serological testing strategies, lockdown easing and vaccine design.