The interactions of SARS-CoV-2 with co-circulating pathogens: Epidemiological implications and current knowledge gaps

TL;DR

This paper reviews current evidence and develops a framework to understand how SARS-CoV-2 interacts with other pathogens, highlighting experimental findings, epidemiological insights, and modeling approaches to address knowledge gaps and improve multi-epidemic forecasting.

Contribution

It introduces a comprehensive framework for analyzing SARS-CoV-2 interactions with other pathogens and reviews experimental, epidemiological, and modeling studies to inform future research.

Findings

Co-infection with influenza increases disease severity.

Vaccinations may reduce SARS-CoV-2 infection risk.

Models can incorporate interaction mechanisms to predict co-circulation dynamics.

Abstract

Despite the availability of effective vaccines, the persistence of SARS-CoV-2 suggests that co-circulation with other pathogens and resulting multi-epidemics may become increasingly frequent. To better forecast and control the risk of such multi-epidemics, it is essential to elucidate the potential interactions of SARS-CoV-2 with other pathogens; these interactions, however, remain poorly defined. Here, we aimed to review the current body of evidence about SARS-CoV-2 interactions. To study pathogen interactions in a systematic way, we first developed a general framework to capture their major components: sign, strength, symmetry, duration, and mechanism. We then reviewed the experimental evidence from animal models about SARS-CoV-2 interactions. Of the 14 studies identified, 11 focused on the outcomes of co-infection with non-attenuated influenza A viruses and generally demonstrated that co-infection increased disease severity compared with either mono-infection. By contrast, the effect of co-infection on the viral load of either virus was variable and inconsistent across studies. Next, we reviewed the epidemiological evidence about SARS-CoV-2 interactions in human populations. Although numerous studies were identified, only few were specifically designed to infer interaction and many were prone to multiple biases, including confounding. Nevertheless, their results suggested that influenza and pneumococcal conjugate vaccinations were associated with reduced riskof SARS-CoV-2 infection. Finally, we formulated simple transmission models of SARS-CoV-2 co-circulation with a viral or a bacterial pathogen, showing how they can naturally incorporate the proposed framework. More generally, we argue that such models, when designed with an integrative and multidisciplinary perspective, will be invaluable tools to resolve the substantial uncertainties that remain about SARS-CoV-2 interactions.