Durability of immunity to SARS-CoV-2 reinfection

By Nidhi Saha, BDSReviewed by Benedette Cuffari, M.Sc.Oct 6 2021

The most effective approaches that are currently available to control the spread and transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the virus responsible for the coronavirus disease-2019 (COVID-19), include several highly effective COVID-19 vaccines that have already been distributed worldwide. Despite their utility, there remain critical knowledge gaps on SARS-CoV-2 reinfections and long-term immune responses following vaccination.  

Study: The durability of immunity against reinfection by SARS-CoV-2: a comparative evolutionary study. Image Credit: Lightspring / Shutterstock.com

It is not likely that the interaction of SARS-CoV-2 with the mammalian immune system is extremely divergent when compared to its close coronavirus relatives. A comparative evolutionary analysis was conducted among SARS-CoV, the Middle East respiratory syndrome coronavirus (MERS-CoV), human coronavirus (HCoV)-229E, HCoV-OC43, and HCoV-NL63 viruses to estimate the probability and likely time of reinfection of SARS-CoV-2.

Thus, the goal of the current study was to use data on the durability of immunity among evolutionarily close coronavirus relatives of SARS-CoV-2 to estimate times to reinfection of related viruses of SARS-CoV-2.

Study design

The current study entailed searches from PubMed and Google Scholar for articles containing information on antibody levels after recovery from infection by the close coronavirus relatives to SARS-CoV-2 and corresponding times of reinfection.

For the purpose of reconstructing maximum-likelihood molecular phylogeny of human-infecting coronaviruses, phylogenic analyses of the S, M, and ORF1b genes, all of which were present as single copies in all viral genomes, were conducted. This enabled comparative studies of peak normalized nucleocapsid protein, spike protein, and whole-virus lysate post-infection immunoglobulin G (IgG) antibody optical density data, along with corresponding data pertaining to reinfection rates on human-infecting coronaviruses.

By analyzing ancestral and descendent states, the declines in antibody levels with time were estimated. Additional estimates included inferred parameters for linear logistic models that linked probabilities of reinfection with antibody levels and quantified the anticipated reinfection time after recovery under conditions of endemic transmission.

Study findings

This study utilized data on the optical density of antibodies for six human-infecting coronaviruses, extending from 128 days to 28 years after infection to estimate typical antibody decline profiles and probabilities of reinfection.

Under endemic conditions, reinfection by SARS-CoV-2 is likely to occur between 3 and 63 months after peak antibody response, with a median of 16 months. This estimate is less than half the duration of those for the endemic coronaviruses infecting humans.

This finding was thought to be consistent with the low numbers of validated cases of reinfection. It is anticipated that reinfection will become more common, as the pandemic disease transitions into endemic disease. The estimated timing of the immunity waning can help guide policy-making.

Importantly, this estimate argues strongly against the claim of herd immunity arising from natural infection or that the morbidity and mortality of COVID-19 can be achieved without immunization. In places where vaccination rates are low, the results emphasize the need for continued safety practices such as social distancing, proper ventilation indoors, and mask-wearing to avoid reinfections.

Limitations of the study

There are several limitations to this study.

Firstly, with no longitudinal data gathered on anti-spike IgG and anti-virus IgG antibody response to endemic coronavirus infection, the authors had to rely on inferences based on the high correlations among antibodies to some targets. These targets included anti-nucleocapsid and anti-spike, s well as anti-virus and anti-spike antibodies.

Additionally, long-term studies that were used to calculate antibody declines and infection probabilities of SARS-CoV, MERS-CoV, HCoV-229E, HCoV-OC43, and HCoV-NL63 accounted for a small number of infected individuals. Therefore, this data would be affected by individuals having shorter or longer durations of immunity.

Some of the different factors that can affect an individual’s risk of reinfection include their immune status, the severity of the infection, cross-immunity, age, immunological factors such as T-cell and B-cell memory, or the lack of antibody neutralizing capacity. The current study did not capture all these aspects and their interactions, nor did it consider other aspects of SARS-CoV-2 infection that warrant special attention.

As this study was based on predictive modeling of epidemiology, it was dependent on the consequences of natural infections and needed to recognize lower waning times of immunity depending on the symptoms.

Another limitation was that protective immunity consists of both humoral (antibody-based) immunity and cell-mediated (B- and T-cell) immunity. The identification of B-cell and T-cell populations could be more directly causal of immunity or better indicators of the durability of immunity, rather than antibody levels alone.

Conclusion

During the COVID-19 pandemic, epidemiological modeling has played a vital role in formulating public health policies, as well as in the management of the infection. Long-term implications have been vague due to a lack of information estimating the waning of immunity for SARS-CoV-2.

The results underscore the necessity of further modeling. This study provides robust knowledge of the prospects of resistance to SARS-CoV-2 reinfection, which can guide numerous public health decisions until more definitive data is available. In the future, these findings may also be utilized in predicting the duration and spectrum of immunity conferred by specific vaccines as compared to natural immunity post-infection by emerging variants.