Dataset Information

Reinfection with SARS-CoV-2: Discrete SIR (Susceptible, Infected, Recovered) Modeling Using Empirical Infection Data.

ABSTRACT: 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 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.

SUBMITTER: McMahon A

PROVIDER: S-EPMC7674142 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Reinfection with SARS-CoV-2: Discrete SIR (Susceptible, Infected, Recovered) Modeling Using Empirical Infection Data.

McMahon Andrew A Robb Nicole C NC

JMIR public health and surveillance 20201116 4

<h4>Background</h4>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 ...[more]

PMID: 33052872

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Project description:The diversity of SARS-CoV-2 mutations raises the possibility of reinfection of individuals previously infected with earlier variants, and this risk is further increased by the emergence of the B.1.1.529 Omicron variant. In this study, we used an in vivo, hamster infection model to assess the potential for individuals previously infected with SARS-CoV-2 to be reinfected with Omicron variant and we also investigated the pathology associated with such infections. Initially, Syrian hamsters were inoculated with a lineage A, B.1.1.7, B.1.351, B.1.617.2 or a subvariant of Omicron, BA.1 strain and then reinfected with the BA.1 strain 5 weeks later. Subsequently, the impact of reinfection with Omicron subvariants (BA.1 and BA.2) in individuals previously infected with the BA.1 strain was examined. Although viral infection and replication were suppressed in both the upper and lower airways, following reinfection, virus-associated RNA was detected in the airways of most hamsters. Viral replication was more strongly suppressed in the lower respiratory tract than in the upper respiratory tract. Consistent amino acid substitutions were observed in the upper respiratory tract of infected hamsters after primary infection with variant BA.1, whereas diverse mutations appeared in hamsters reinfected with the same variant. Histopathology showed no acute pneumonia or disease enhancement in any of the reinfection groups and, in addition, the expression of inflammatory cytokines and chemokines in the airways of reinfected animals was only mildly elevated. These findings are important for understanding the risk of reinfection with new variants of SARS-CoV-2. IMPORTANCE The emergence of SARS-CoV-2 variants and the widespread use of COVID-19 vaccines has resulted in individual differences in immune status against SARS-CoV-2. A decay in immunity over time and the emergence of variants that partially evade the immune response can also lead to reinfection. In this study, we demonstrated that, in hamsters, immunity acquired following primary infection with previous SARS-CoV-2 variants was effective in preventing the onset of pneumonia after reinfection with the Omicron variant. However, viral infection and multiplication in the upper respiratory tract were still observed after reinfection. We also showed that more diverse nonsynonymous mutations appeared in the upper respiratory tract of reinfected hamsters that had acquired immunity from primary infection. This hamster model reveals the within-host evolution of SARS-CoV-2 and its pathology after reinfection, and provides important information for countermeasures against diversifying SARS-CoV-2 variants.

Project description:As public health guidelines throughout the world have relaxed in response to vaccination campaigns against SARS-CoV-2, it is likely that SARS-CoV-2 will remain endemic, fueled by the rise of more infectious SARS-CoV-2 variants. Moreover, in the setting of waning natural and vaccine immunity, reinfections have emerged across the globe, even among previously infected and vaccinated individuals. As such, the ability to detect reexposure to and reinfection by SARS-CoV-2 is a key component for global protection against this virus and, more importantly, against the potential emergence of vaccine escape mutations. Accordingly, there is a strong and continued need for the development and deployment of simple methods to detect emerging hot spots of reinfection to inform targeted pandemic response and containment, including targeted and specific deployment of vaccine booster campaigns. In this study, we identify simple, rapid immune biomarkers of reinfection in rhesus macaques, including IgG3 antibody levels against nucleocapsid and FcγR2A receptor binding activity of anti-RBD antibodies, that are recapitulated in human reinfection cases. As such, this cross-species analysis underscores the potential utility of simple antibody titers and function as price-effective and scalable markers of reinfection to provide increased resolution and resilience against new outbreaks. IMPORTANCE As public health and social distancing guidelines loosen in the setting of waning global natural and vaccine immunity, a deeper understanding of the immunological response to reexposure and reinfection to this highly contagious pathogen is necessary to maintain public health. Viral sequencing analysis provides a robust but unrealistic means to monitor reinfection globally. The identification of scalable pathogen-specific biomarkers of reexposure and reinfection, however, could significantly accelerate our capacity to monitor the spread of the virus through naive and experienced hosts, providing key insights into mechanisms of disease attenuation. Using a nonhuman primate model of controlled SARS-CoV-2 reexposure, we deeply probed the humoral immune response following rechallenge with various doses of viral inocula. We identified virus-specific humoral biomarkers of reinfection, with significant increases in antibody titer and function upon rechallenge across a range of humoral features, including IgG1 to the receptor binding domain of the spike protein of SARS-CoV-2 (RBD), IgG3 to the nucleocapsid protein (N), and FcγR2A receptor binding to anti-RBD antibodies. These features not only differentiated primary infection from reexposure and reinfection in monkeys but also were recapitulated in a sequencing-confirmed reinfection patient and in a cohort of putatively reinfected humans that evolved a PCR-positive test in spite of preexisting seropositivity. As such, this cross-species analysis using a controlled primate model and human cohorts reveals increases in antibody titers as promising cross-validated serological markers of reinfection and reexposure.

Dataset Information

Reinfection with SARS-CoV-2: Discrete SIR (Susceptible, Infected, Recovered) Modeling Using Empirical Infection Data.

Publications

Reinfection with SARS-CoV-2: Discrete SIR (Susceptible, Infected, Recovered) Modeling Using Empirical Infection Data.

Similar Datasets

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