SynthesisJournal of the Royal Society, Interface2016
Behavioural change models for infectious disease transmission: a systematic review (2010-2015).
Synthesis in Journal of the Royal Society, Interface, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 150 papers, 5 of them syntheses that pooled it.
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
150 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- A scoping review of COVID-19 modelling studies in Belgium 2020-2024: incorporation of behaviour and lessons learned.Archives of public health = Archives belges de sante publique · 2026Pooled it
- Non-pharmaceutical interventions in containing COVID-19 pandemic after the roll-out of coronavirus vaccines: a systematic review.BMC public health · 2024Pooled it
- Systematic Literature Review on the Spread of Health-related Misinformation on Social Media.Social science & medicine (1982) · 2019Pooled it
- Population-level mathematical modeling of antimicrobial resistance: a systematic review.BMC medicine · 2019Pooled it
- Lessons from a decade of individual-based models for infectious disease transmission: a systematic review (2006-2015).BMC infectious diseases · 2017Pooled it
- Memory mechanisms for behavioural change in Bayesian individual-level spatial epidemic models.Infectious Disease Modelling · 2026Article
- Multivariable Behavioral Change Modeling of Epidemics in the Presence of Undetected Infections.Statistics in medicine · 2026Article
- Modelling assessment of epidemiological strategies based on self-screening tools.Communications medicine · 2026Article
- Heterogeneous estimations of non-pharmaceutical mitigation behavior during the COVID-19 pandemic.Scientific data · 2026Article
- Modeling individual self-protective behavior during epidemics.PLoS computational biology · 2026Article
- App-based epidemic game in a university campus reveals how risk perception and behavioral interventions shape disease transmission dynamics.Scientific reports · 2026Article
- Estimating the effect of self-protection on transmission dynamics of SARS-CoV-2 in Germany in 2021: a modelling study.BMC public health · 2026Article
- Intraindividual variability in non-household contacts: a German longitudinal study, April 2020-December 2021.BMC infectious diseases · 2026Article
- Social, mobility and contact networks in shaping health behaviours and infectious disease dynamics: a scoping review.Infectious diseases of poverty · 2025Article
- Epydemix: An open-source Python package for epidemic modeling with integrated approximate Bayesian calibration.PLoS computational biology · 2025Article
- Improving policy design and epidemic response using integrated models of economic choice and disease dynamics with behavioral feedback.PLoS computational biology · 2025Article
- Learned behavioral avoidance can alter outbreak dynamics in a model for waterborne infectious diseases.Journal of mathematical biology · 2025Article
- Comparative evaluation of behavioral epidemic models using COVID-19 data.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Unifying human infectious disease models and real-time awareness of population- and subpopulation-level intervention effectiveness.Royal Society open science · 2025Article
- Behavior-driven forecasts of neighborhood-level COVID-19 spread in New York City.PLoS computational biology · 2025Article
90 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We review behavioural change models (BCMs) for infectious disease transmission in humans. Following the Cochrane collaboration guidelines and the PRISMA statement, our systematic search and selection yielded 178 papers covering the period 2010-2015. We observe an increasing trend in published BCMs, frequently coupled to (re)emergence events, and propose a categorization by distinguishing how information translates into preventive actions. Behaviour is usually captured by introducing information as a dynamic parameter (76/178) or by introducing an economic objective function, either with (26/178) or without (37/178) imitation. Approaches using information thresholds (29/178) and exogenous behaviour formation (16/178) are also popular. We further classify according to disease, prevention measure, transmission model (with 81/178 population, 6/178 metapopulation and 91/178 individual-level models) and the way prevention impacts transmission. We highlight the minority (15%) of studies that use any real-life data for parametrization or validation and note that BCMs increasingly use social media data and generally incorporate multiple sources of information (16/178), multiple types of information (17/178) or both (9/178). We conclude that individual-level models are increasingly used and useful to model behaviour changes. Despite recent advancements, we remain concerned that most models are purely theoretical and lack representative data and a validation process.
Indexed as
Identifiers
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.