There is growing experimental evidence that many respiratory viruses--including influenza and SARS-CoV-2--can interact, such that their epidemiological dynamics may not be independent. To assess these interactions, standard statistical tests of independence suggest that the prevalence ratio--defined as the ratio of co-infection prevalence to the product of single-infection prevalences--should equal unity for non-interacting pathogens. As a result, earlier epidemiological studies aimed to estimate the prevalence ratio from co-detection prevalence data, under the assumption that deviations from unity implied interaction. To examine the validity of this assumption, we designed a simulation study that built on a broadly applicable epidemiological model of co-circulation of two respiratory viruses causing seasonal epidemics. By focusing on the pair influenza-SARS-CoV-2, we first demonstrate that the prevalence ratio systematically under-estimates the strength of interaction, and can even misclassify antagonistic or synergistic interactions that persist after clearance of infection. In a global sensitivity analysis, we further identify properties of viral infection--such as a high reproduction number or a short infectious period--that blur the interaction inferred from the prevalence ratio. Altogether, our results suggest that epidemiological studies based on co-detection prevalence data provide a poor guide to assess interactions among respiratory viruses.
The pitfalls of inferring virus-virus interactions from co-detection prevalence data: Application to influenza and SARS-CoV-2