ReviewmBio2024
Considerations for viral co-infection studies in human populations.
Review in mBio, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Leveraging perturbations to infer the population dynamics of human rhinovirus and interaction of influenza A virus.PLoS computational biology · 2026Article
- SARS-CoV-2 3CLpro inhibits the replication of influenza viruses through the cleavage of NP and PA.Virologica Sinica · 2026Article
- Clinical Manifestations and Immunological Impacts in Co-Infections and Sequential Infections of SARS-CoV-2 and Arboviruses.Current microbiology · 2026Review
- Respiratory Viral Co-Infections in Pediatric Patients: Clinical Impact and Implications for Healthcare Practice-A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Leveraging perturbations to infer the population dynamics of human rhinovirus and interaction of influenza A virus.medRxiv : the preprint server for health sciences · 2026Article
- Immunoinformatics based designing of a broad-spectrum multi-epitope vaccine against co-infection of human metapneumovirus, respiratory syncytial virus, and influenza A virus.Scientific reports · 2026Article
- Hidden Markov models detect recombination and ancestry of SARS-CoV-2.bioRxiv : the preprint server for biology · 2026Article
- Integration of bioinformatic tools for the detection of SARS-CoV-2 co-infection cases.Microbial genomics · 2026Article
- Respiratory viral coinfections: interactions, mechanisms and clinical implications.Nature reviews. Microbiology · 2025Review
- Using COVID-19 pandemic perturbation to model RSV-hMPV interactions and potential implications under RSV interventions.Nature communications · 2025Article
- Population-level respiratory virus-virus interactions, Puerto Rico, 2013-2023.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2025Article
- General practice antibiotic prescriptions attributable to respiratory syncytial virus by age and antibiotic class: an ecological analysis of the English population.The Journal of antimicrobial chemotherapy · 2025Article
- Seasonality and Co-Detection of Respiratory Viral Infections Among Hospitalised Patients Admitted With Acute Respiratory Illness-Valencia Region, Spain, 2010-2021.Influenza and other respiratory viruses · 2024Article
- Current View on Major Natural Compounds Endowed with Antibacterial and Antiviral Effects.Antibiotics (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
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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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.