Considerations for viral co-infection studies in human populations

When respiratory viruses co-circulate in a population, individuals may be infected with multiple pathogens and experience possible virus-virus interactions, where concurrent or recent prior infection with one virus affects the infection process of another virus. While experimental studies have provi...

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Bibliographic Details
Published in:mBio Vol. 15; no. 7; p. e0065824
Main Authors: Chin, Taylor, Foxman, Ellen F., Watkins, Timothy A., Lipsitch, Marc
Format: Journal Article
Language:English
Published: United States American Society for Microbiology 17.07.2024
Subjects:
Adaptive immunity
Bias
co-detections
Coinfection - epidemiology
Coinfection - virology
Cross-Sectional Studies
Epidemiology
Humans
Minireview
Population
Population studies
Prevalence
Respiratory Tract Infections - epidemiology
Respiratory Tract Infections - virology
Selection Bias
Viral infections
Viral Interference
Virus Diseases - epidemiology
Virus Diseases - virology
Viruses
Viruses - classification
Viruses - genetics
Viruses - isolation & purification
virus–virus interactions
selection bias
co-detections
epidemiology
virus–virus interactions
ISSN:2150-7511, 2161-2129, 2150-7511
Online Access:Get full text
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Summary:When respiratory viruses co-circulate in a population, individuals may be infected with multiple pathogens and experience possible virus-virus interactions, where concurrent or recent prior infection with one virus affects the infection process of another virus. While experimental studies have provided convincing evidence for within-host mechanisms of virus-virus interactions, evaluating evidence for viral interference or potentiation using population-level data has proven more difficult. Recent studies have quantified the prevalence of co-detections using populations drawn from clinical settings. Here, we focus on selection bias issues associated with this study design. We provide a quantitative account of the conditions under which selection bias arises in these studies, review previous attempts to address this bias, and propose unbiased study designs with sample size estimates needed to ascertain viral interference. We show that selection bias is expected in cross-sectional co-detection prevalence studies conducted in clinical settings, except under a strict set of assumptions regarding the relative probabilities of being included in a study limited to individuals with clinical disease under different viral states. Population-wide studies that collect samples from participants irrespective of their clinical status would meanwhile require large sample sizes to be sufficiently powered to detect viral interference, suggesting that a study's timing, inclusion criteria, and the expected magnitude of interference are instrumental in determining feasibility.
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The authors declare no conflict of interest.
ISSN:2150-7511
2161-2129
2150-7511
DOI:10.1128/mbio.00658-24