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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Vydáno v:	mBio Ročník 15; číslo 7; s. e0065824
Hlavní autoři:	Chin, Taylor, Foxman, Ellen F., Watkins, Timothy A., Lipsitch, Marc
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States American Society for Microbiology 17.07.2024
Témata:	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
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23 The authors declare no conflict of interest.
ISSN:	2150-7511 2161-2129 2150-7511
DOI:	10.1128/mbio.00658-24