Reinfection and low cross-immunity as drivers of epidemic resurgence under high seroprevalence: a model-based approach with application to Amazonas, Brazil

TL;DR

This study presents a multi-strain epidemic model to explain COVID-19 resurgence in Amazonas, Brazil, highlighting reinfection and low cross-immunity as key factors behind persistent infections and new waves despite high seroprevalence.

Contribution

It introduces a novel multi-strain model incorporating reinfection and cross-immunity, applied to real data from Amazonas to explain epidemic resurgence.

Findings

Reinfection contributes to persistent infection levels.

Low cross-immunity facilitates emergence of new variants.

Model explains multiple epidemic waves despite high seroprevalence.

Abstract

This paper introduces a new multi-strain epidemic model with reinfection and cross-immunity to provide insights into the resurgence of the COVID-19 epidemic in an area with reportedly high seroprevalence due to a largely unmitigated outbreak: the state of Amazonas, Brazil. Although high seroprevalence could have been expected to trigger herd immunity and prevent further waves in the state, we have observed persistent levels of infection after the first wave and eventually the emergence of a second viral strain just before an augmented second wave. Our experiments suggest that the persistent levels of infection after the first wave may be due to reinfection, whereas the higher peak at the second wave can be explained by the emergence of the second variant and a low level of cross-immunity between the original and the second variant. Finally, the proposed model provides insights into the effect of reinfection and cross-immunity on the long-term spread of an unmitigated epidemic.