An extended catalytic model to assess changes in risk for multiple reinfections with SARS-CoV-2

Abstract Background The SARS-CoV-2 pandemic has illustrated that monitoring trends in multiple infections can provide insight into the biological characteristics of new variants. Following several pandemic waves, many people have already been infected and reinfected by SARS-CoV-2 and therefore methods are needed to understand the risk of multiple reinfections. Objectives In this paper, we extended an existing catalytic model designed to detect increases in the risk of reinfection by SARS-CoV-2 to detect increases in the population-level risk of multiple reinfections. Methods The catalytic model assumes the risk of reinfection is proportional to observed infections and uses a Bayesian approach to fit model parameters to the number of n th infections among individuals whose ( n − 1) th infection was observed at least 90 days before. Using a posterior draw from the fitted model parameters, a 95% projection interval of daily n th infections is calculated under the assumption of a constant n th infection hazard coefficient. An additional model parameter was introduced to consider the increased risk of reinfection detected during the Omicron wave. Validation was performed to assess the model’s ability to detect increases in the risk of third infections. Key Findings The model parameters converged when applying the model’s fitting and projection procedure to the number of observed third SARS-COV-2 infections in South Africa. No additional increase in the risk of third infection was detected after the increase detected during the Omicron wave. The validation of the third infections method showed that the model can successfully detect increases in the risk of third infections under different scenarios. Limitations Even though the extended model is intended to detect the risk of n th infections, the method was only validated for detecting increases in the risk of third infections and not for four or more infections. The method is very sensitive to low numbers of n th infections, so it might not be usable in settings with small epidemics, low coverage of testing or early in an outbreak. Conclusions The catalytic model to detect increases in the risk of reinfections was successfully extended to detect increases in the risk of n th infections and could contribute to future detection of increases in the risk of n th infections by SARS-CoV-2 or other similar pathogens.