The pitfalls of inferring virus-virus interactions from co-detection prevalence data: application to influenza and SARS-CoV-2.
Proc Biol Sci
; 289(1966): 20212358, 2022 01 12.
Article
in English
| MEDLINE | ID: covidwho-1621738
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
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
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Influenza, Human
/
Coinfection
/
COVID-19
Type of study:
Diagnostic study
/
Observational study
Limits:
Humans
Language:
English
Journal:
Proc Biol Sci
Journal subject:
Biology
Year:
2022
Document Type:
Article
Affiliation country:
Rspb.2021.2358
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