tetano
Editor, Senior Moderator
PLoS One
. 2025 Jan 2;20(1):e0315476.
doi: 10.1371/journal.pone.0315476. eCollection 2025. A generalised catalytic model to assess changes in risk for multiple reinfections with SARS-CoV-2
Belinda Lombard[SUP] 1 [/SUP], Cheryl Cohen[SUP] 2 3 [/SUP], Anne von Gottberg[SUP] 2 4 [/SUP], Jonathan Dushoff[SUP] 1 5 [/SUP], Juliet R C Pulliam[SUP] 1 [/SUP], Cari van Schalkwyk[SUP] 1 [/SUP]
Affiliations
Background: Monitoring trends in multiple infections with SARS-CoV-2, following several pandemic waves, provides insight into the biological characteristics of new variants, but also necessitates methods to understand the risk of multiple reinfections.
Objectives: We generalised a catalytic model designed to detect increases in the risk of SARS-CoV-2 reinfection, to assess 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 nth infections among individuals that occur at least 90 days after a previous infection. Using a posterior draw from the fitted model parameters, a 95% projection interval of daily nth infections is calculated under the assumption of a constant nth infection hazard coefficient. An additional model parameter was incorporated for the increased reinfection risk detected during the Omicron wave. The generalised model's performance was then assessed using simulation-based validation.
Key findings: No additional increase in the risk of third infection was detected after the increase detected during the Omicron wave. Using simulation-based validation, we show that the model can successfully detect increases in the risk of third infections under different scenarios.
Limitations: Even though the generalised model is intended to detect the risk of nth infections, it is validated specifically for third infections, with its applicability for four or more infections being unconfirmed. Furthermore, the method's sensitivity to low counts of nth infections, limits application in settings with small epidemics, limited testing coverage or early in an outbreak.
Conclusions: The catalytic model was successfully adapted to detect increases in the risk of nth infections, enhancing our capacity to identify future changes in the risk of nth infections by SARS-CoV-2 or other similar pathogens.
. 2025 Jan 2;20(1):e0315476.
doi: 10.1371/journal.pone.0315476. eCollection 2025. A generalised catalytic model to assess changes in risk for multiple reinfections with SARS-CoV-2
Belinda Lombard[SUP] 1 [/SUP], Cheryl Cohen[SUP] 2 3 [/SUP], Anne von Gottberg[SUP] 2 4 [/SUP], Jonathan Dushoff[SUP] 1 5 [/SUP], Juliet R C Pulliam[SUP] 1 [/SUP], Cari van Schalkwyk[SUP] 1 [/SUP]
Affiliations
- PMID: 39746096
- DOI: 10.1371/journal.pone.0315476
Background: Monitoring trends in multiple infections with SARS-CoV-2, following several pandemic waves, provides insight into the biological characteristics of new variants, but also necessitates methods to understand the risk of multiple reinfections.
Objectives: We generalised a catalytic model designed to detect increases in the risk of SARS-CoV-2 reinfection, to assess 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 nth infections among individuals that occur at least 90 days after a previous infection. Using a posterior draw from the fitted model parameters, a 95% projection interval of daily nth infections is calculated under the assumption of a constant nth infection hazard coefficient. An additional model parameter was incorporated for the increased reinfection risk detected during the Omicron wave. The generalised model's performance was then assessed using simulation-based validation.
Key findings: No additional increase in the risk of third infection was detected after the increase detected during the Omicron wave. Using simulation-based validation, we show that the model can successfully detect increases in the risk of third infections under different scenarios.
Limitations: Even though the generalised model is intended to detect the risk of nth infections, it is validated specifically for third infections, with its applicability for four or more infections being unconfirmed. Furthermore, the method's sensitivity to low counts of nth infections, limits application in settings with small epidemics, limited testing coverage or early in an outbreak.
Conclusions: The catalytic model was successfully adapted to detect increases in the risk of nth infections, enhancing our capacity to identify future changes in the risk of nth infections by SARS-CoV-2 or other similar pathogens.