ArticleInfectious Disease Modelling2023
Bayesian modeling of dynamic behavioral change during an epidemic.
Article in Infectious Disease Modelling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- 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
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- Spatial Individual-Level Models for Transmission Dynamics of Seasonal Infectious Diseases.Statistics in medicine · 2026Article
- Characterizing population-level changes in human behavior during the COVID-19 pandemic in the United States.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Understanding the interplay between epidemiological and social cognitve drivers of behaviour change during the Covid-19 pandemic.Scientific reports · 2025Article
- Conditional logistic individual-level models of spatial infectious disease dynamics.Infectious Disease Modelling · 2025Article
- Behavioural Change Piecewise Constant Spatial Epidemic Models.Infectious Disease Modelling · 2025Article
- Parameter estimation in behavioral epidemic models with endogenous societal risk-response.PLoS computational biology · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For many infectious disease outbreaks, the at-risk population changes their behavior in response to the outbreak severity, causing the transmission dynamics to change in real-time. Behavioral change is often ignored in epidemic modeling efforts, making these models less useful than they could be. We address this by introducing a novel class of data-driven epidemic models which characterize and accurately estimate behavioral change. Our proposed model allows time-varying transmission to be captured by the level of "alarm" in the population, with alarm specified as a function of the past epidemic trajectory. We investigate the estimability of the population alarm across a wide range of scenarios, applying both parametric functions and non-parametric functions using splines and Gaussian processes. The model is set in the data-augmented Bayesian framework to allow estimation on partially observed epidemic data. The benefit and utility of the proposed approach is illustrated through applications to data from real epidemics.
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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.