ArticlePLoS pathogens2014
The contribution of social behaviour to the transmission of influenza A in a human population.
Article in PLoS pathogens, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
- Social contact patterns in the United Kingdom following the COVID-19 pandemic: The Reconnect cross-sectional survey.PLoS medicine · 2026Article
- Social Mixing Patterns and Chikungunya Reemergence Risk in French Polynesia.JAMA network open · 2026Article
- Identification of age-group reservoirs for persistent vaccine-type pneumococcal carriage in rural Gambia.Pneumonia (Nathan Qld.) · 2025Article
- Social contacts and mixing patterns in rural Gambia.BMC infectious diseases · 2025Article
- Reconstructed influenza A/H3N2 infection histories reveal variation in incidence and antibody dynamics over the life course.PLoS biology · 2024Article
- Surveillance of respiratory viruses by aerosol screening in indoor air as an early warning system for epidemics.Environmental microbiology reports · 2024Article
- Reconstructed influenza A/H3N2 infection histories reveal variation in incidence and antibody dynamics over the life course.medRxiv : the preprint server for health sciences · 2024Article
- Improving Estimates of Social Contact Patterns for Airborne Transmission of Respiratory Pathogens.Emerging infectious diseases · 2022Article
- Correlates of the country differences in the infection and mortality rates during the first wave of the COVID-19 pandemic: evidence from Bayesian model averaging.Scientific reports · 2022Article
- Estimating local outbreak risks and the effects of non-pharmaceutical interventions in age-structured populations: SARS-CoV-2 as a case study.Journal of theoretical biology · 2022Article
- Novel Epidemic Metrics to Communicate Outbreak Risk at the Municipality Level: Dengue and Zika in the Dominican Republic.Viruses · 2022Article
- Analyzing the Impact of Demographic Variables on Spreading and Forecasting COVID-19.Journal of healthcare informatics research · 2022Article
- Age-seroprevalence curves for the multi-strain structure of influenza A virus.Nature communications · 2021Article
- Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model.PLoS computational biology · 2021Article
- Age-specific social mixing of school-aged children in a US setting using proximity detecting sensors and contact surveys.Scientific reports · 2021Article
- 'Dark matter', second waves and epidemiological modelling.BMJ global health · 2020Review
- A multicriteria approach for risk assessment of Covid-19 in urban district lockdown.Safety science · 2020Article
- Effects of non-pharmaceutical interventions on COVID-19 cases, deaths, and demand for hospital services in the UK: a modelling study.The Lancet. Public health · 2020Article
- Effects of temperature, humidity, and diurnal temperature range on influenza incidence in a temperate region.Influenza and other respiratory viruses · 2020Article
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Authors and funding
8 authors.
Funding
Abstract
Variability in the risk of transmission for respiratory pathogens can result from several factors, including the intrinsic properties of the pathogen, the immune state of the host and the host's behaviour. It has been proposed that self-reported social mixing patterns can explain the behavioural component of this variability, with simulated intervention studies based on these data used routinely to inform public health policy. However, in the absence of robust studies with biological endpoints for individuals, it is unclear how age and social behaviour contribute to infection risk. To examine how the structure and nature of social contacts influenced infection risk over the course of a single epidemic, we designed a flexible disease modelling framework: the population was divided into a series of increasingly detailed age and social contact classes, with the transmissibility of each age-contact class determined by the average contacts of that class. Fitting the models to serologically confirmed infection data from the 2009 Hong Kong influenza A/H1N1p pandemic, we found that an individual's risk of infection was influenced strongly by the average reported social mixing behaviour of their age group, rather than by their personal reported contacts. We also identified the resolution of social mixing that shaped transmission: epidemic dynamics were driven by intense contacts between children, a post-childhood drop in risky contacts and a subsequent rise in contacts for individuals aged 35-50. Our results demonstrate that self-reported social contact surveys can account for age-associated heterogeneity in the transmission of a respiratory pathogen in humans, and show robustly how these individual-level behaviours manifest themselves through assortative age groups. Our results suggest it is possible to profile the social structure of different populations and to use these aggregated data to predict their inherent transmission potential.
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