Durability of immunity to reinfection with SARS-CoV-2

The most effective approaches currently available to control the spread and transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the virus responsible for coronavirus-2019 disease (COVID-19), include several effective COVID-19 vaccines that have already been distributed around the world. Despite their usefulness, there remain critical knowledge gaps about re-infections with SARS-CoV-2 and long-term immune responses after vaccination.

To study: The durability of immunity against reinfection with SARS-CoV-2: an evolutionary comparative study. Image Credit: Lightspring / Shutterstock.com

The interaction of SARS-CoV-2 with the mammalian immune system is unlikely to be extremely divergent from its coronavirus close relatives. A comparative evolutionary analysis was conducted among viruses of SARS-CoV, Middle East respiratory syndrome coronavirus (MERS-CoV), human coronavirus (HCoV) -229E, HCoV-OC43 and HCoV-NL63 to estimate the probability and the probable time of reinfection of SARS-CoV-2.

Thus, the objective of the present study was to use data on the durability of immunity in relatives of the SARS-CoV-2 coronavirus evolutionarily to estimate the times of reinfection of related viruses of the SARS-CoV-2. SARS-CoV-2.

Study design

The current study involved searching PubMed and Google Scholar for articles with information on antibody levels after recovery from infection by close relatives of the SARS-CoV-2 coronavirus and the corresponding reinfection periods. .

In order to reconstruct the maximum likelihood molecular phylogeny of coronaviruses infecting humans, phylogenic analyzes of the S, M, and ORF1b genes, which were all present as single copies in all viral genomes, were carried out. This allowed for comparative studies of the optical density data of the maximum normalized core protein, spike protein and whole virus lysate post-infection to the optical density data of immunoglobulin G (IgG) antibodies, as well as corresponding data on reinfection rates on coronaviruses infecting humans.

By analyzing ancestral and descendant states, decreases in antibody levels over time were estimated. Additional estimates included inferred parameters for linear logistic models that related the probabilities of reinfection to antibody levels and quantified the expected reinfection time after recovery under conditions of endemic transmission.

Study results

This study used antibody optical density data for six coronaviruses infecting humans, ranging from 128 days to 28 years after infection to estimate typical antibody decline profiles and the odds of reinfection.

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

This result was considered consistent with the low number of validated cases of reinfection. Reinfection is expected to become more common, as pandemic disease evolves into endemic disease. The estimated timing of the decline in immunity can help guide policy development.

It is important to note that this estimate strongly opposes the assertion of herd immunity resulting from a natural infection or that the morbidity and mortality of COVID-19 can be achieved without vaccination. In places with low vaccination rates, the results underscore the need to maintain safety practices such as social distancing, good indoor ventilation, and wearing a mask to prevent re-infections.

Limitations of the study

There are several limitations to this study.

First, in the absence of longitudinal data collected on the response of anti-spike IgG and anti-virus IgG antibodies to endemic coronavirus infection, the authors had to rely on inferences based on the high correlations between the antibodies. directed against certain targets. These targets included anti-nucleocapsid and anti-spike antibodies, as well as anti-virus and anti-spike antibodies.

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

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

As this study was based on predictive modeling of epidemiology, it depended on the consequences of natural infections and had to recognize lower times of decline of immunity as a function of symptoms.

Another limitation was that protective immunity consists of both humoral (antibody-based) and cell-mediated (B and T cells) immunity. Identification of B and T lymphocyte populations may be more directly causative of immunity or better indicators of the durability of immunity, rather than antibody levels alone.

Conclusion

During the COVID-19 pandemic, epidemiological modeling played a critical role in formulating public health policies, as well as managing infection. The long-term implications have been vague due to a lack of information estimating the decline in immunity for SARS-CoV-2.

The results highlight the need for further modeling. This study provides solid knowledge about the outlook for resistance to reinfection with SARS-CoV-2, which can guide many public health decisions until more definitive data becomes available. In the future, these results could also be used to predict the duration and spectrum of immunity conferred by specific vaccines compared to natural immunity post-infection by emerging variants.