ArticlePLoS computational biology2023
An approximate diffusion process for environmental stochasticity in infectious disease transmission modelling.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Efficient sequential Bayesian inference for state-space epidemic models using ensemble data assimilation.PLoS computational biology · 2026Article
- Estimating Environmental Transmission Risk From Host Movement Data.Ecology and evolution · 2025Article
- Sliding mode control with stochastic modeling and mobility interaction for managing epidemic spread in high-population regions.Parasite epidemiology and control · 2025Article
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3 authors.
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Abstract
Modelling the transmission dynamics of an infectious disease is a complex task. Not only it is difficult to accurately model the inherent non-stationarity and heterogeneity of transmission, but it is nearly impossible to describe, mechanistically, changes in extrinsic environmental factors including public behaviour and seasonal fluctuations. An elegant approach to capturing environmental stochasticity is to model the force of infection as a stochastic process. However, inference in this context requires solving a computationally expensive "missing data" problem, using data-augmentation techniques. We propose to model the time-varying transmission-potential as an approximate diffusion process using a path-wise series expansion of Brownian motion. This approximation replaces the "missing data" imputation step with the inference of the expansion coefficients: a simpler and computationally cheaper task. We illustrate the merit of this approach through three examples: modelling influenza using a canonical SIR model, capturing seasonality using a SIRS model, and the modelling of COVID-19 pandemic using a multi-type SEIR model.
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