tetano
Editor, Senior Moderator
Proc Biol Sci
. 2022 Jan 12;289(1966):20212358.
doi: 10.1098/rspb.2021.2358. Epub 2022 Jan 12.
The pitfalls of inferring virus-virus interactions from co-detection prevalence data: application to influenza and SARS-CoV-2
Matthieu Domenech de Cellès[SUP] 1 [/SUP], Elizabeth Goult[SUP] 1 [/SUP], Jean-Sebastien Casalegno[SUP] 2 3 [/SUP], Sarah C Kramer[SUP] 1 [/SUP]
Affiliations
Abstract
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 emerging or seasonal respiratory viruses. By focusing on the pair influenza-SARS-CoV-2, we first demonstrate that the prevalence ratio systematically underestimates 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 ecological or epidemiological studies based on co-detection prevalence data provide a poor guide to assess interactions among respiratory viruses.
Keywords: COVID-19; SARS-CoV-2; influenza; mathematical modelling; virus–virus interaction.
. 2022 Jan 12;289(1966):20212358.
doi: 10.1098/rspb.2021.2358. Epub 2022 Jan 12.
The pitfalls of inferring virus-virus interactions from co-detection prevalence data: application to influenza and SARS-CoV-2
Matthieu Domenech de Cellès[SUP] 1 [/SUP], Elizabeth Goult[SUP] 1 [/SUP], Jean-Sebastien Casalegno[SUP] 2 3 [/SUP], Sarah C Kramer[SUP] 1 [/SUP]
Affiliations
- PMID: 35016540
- DOI: 10.1098/rspb.2021.2358
Abstract
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 emerging or seasonal respiratory viruses. By focusing on the pair influenza-SARS-CoV-2, we first demonstrate that the prevalence ratio systematically underestimates 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 ecological or epidemiological studies based on co-detection prevalence data provide a poor guide to assess interactions among respiratory viruses.
Keywords: COVID-19; SARS-CoV-2; influenza; mathematical modelling; virus–virus interaction.