Reinfection with SARS-CoV-2: Discrete SIR (Susceptible, Infected, Recovered) Modeling Using Empirical Infection Data
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Abstract
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 to determine how best to reduce mortality while ensuring minimal social restrictions on 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 this study was to use mathematical modeling 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 individuals from subsequent exposure in the short term; however, no such cases have been documented.
Conclusions
This work concludes that infection with SARS-CoV-2 provides short-term immunity to reinfection and therefore offers useful insight for serological testing strategies, lockdown easing, and vaccine development.
Article activity feed
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SciScore for 10.1101/2020.06.03.20120113: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Unless specified, the national data on infections and deaths from SARS-CoV-2 was acquired from the ‘Our World in Data’ database compiled by the Oxford Martin School at the University of Oxford [9]; the hospitalisation cases in Switzerland were obtained from the Federal Office of Public Health in Switzerland [10]; the data for the city of New York was obtained from the New York City Health website [11]; the population of New York City was obtained from the 2019 New York census [12] and the recovery data for Germany was sourced from Trading Economics [13] which gets data from WHO (World … SciScore for 10.1101/2020.06.03.20120113: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Unless specified, the national data on infections and deaths from SARS-CoV-2 was acquired from the ‘Our World in Data’ database compiled by the Oxford Martin School at the University of Oxford [9]; the hospitalisation cases in Switzerland were obtained from the Federal Office of Public Health in Switzerland [10]; the data for the city of New York was obtained from the New York City Health website [11]; the population of New York City was obtained from the 2019 New York census [12] and the recovery data for Germany was sourced from Trading Economics [13] which gets data from WHO (World Health Organisation). Data’suggested: NoneResults from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our model has a number of limitations, including the lack of modelling of any social structure, the fact that individuals who have been infected may change their shielding behaviours, differing recovery times from person to person, and missing information regarding immigration into and out of regions of interest. In spite of this, the results documented here provide strong evidence, based on real data, to suggest that that there is at least short-term immunity conferred by an initial infection of SARS-CoV-2. This has implications for serological testing strategies, lockdown easing timescales and vaccine development. Our modelling strategy can also be extended to understand the reinfection dynamics of future pandemics.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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