ArticlePLoS computational biology2018
Accounting for non-stationarity in epidemiology by embedding time-varying parameters in stochastic models.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers.
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22 citing papers in PubMed.
- Efficient sequential Bayesian inference for state-space epidemic models using ensemble data assimilation.PLoS computational biology · 2026Article
- Feasibility of short-term hospital mask demand forecasting using a backpropagation neural network under data scarcity.Scientific reports · 2026Article
- A Bayesian model for repeated cross-sectional epidemic prevalence survey data.PLoS computational biology · 2025Article
- Prediction and control of cholera outbreak: Study case of Cameroon.Infectious Disease Modelling · 2024Article
- Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models.PLoS computational biology · 2024Article
- Spatially explicit effective reproduction numbers from incidence and mobility data.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- An approximate diffusion process for environmental stochasticity in infectious disease transmission modelling.PLoS computational biology · 2023Article
- Modeling the time-dependent transmission rate using gaussian pulses for analyzing the COVID-19 outbreaks in the world.Scientific reports · 2023Article
- A Model for Highly Fluctuating Spatio-Temporal Infection Data, with Applications to the COVID Epidemic.International journal of environmental research and public health · 2022Article
- A new estimation method for COVID-19 time-varying reproduction number using active cases.Scientific reports · 2022Article
- Article
- A sliding window approach to optimize the time-varying parameters of a spatially-explicit and stochastic model of COVID-19.PLOS global public health · 2022Article
- Quantifying Non-Stationarity with Information Theory.Entropy (Basel, Switzerland) · 2021Article
- Dynamics of the COVID-19 epidemic in Ireland under mitigation.BMC infectious diseases · 2021Article
- Estimation of the reproduction number of influenza A(H1N1)pdm09 in South Korea using heterogeneous models.BMC infectious diseases · 2021Article
- A mechanistic and data-driven reconstruction of the time-varying reproduction number: Application to the COVID-19 epidemic.PLoS computational biology · 2021Article
- Parallel trends in the transmission of SARS-CoV-2 and retail/recreation and public transport mobility during non-lockdown periods.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2021Article
- Range of reproduction number estimates for COVID-19 spread.Biochemical and biophysical research communications · 2021Article
- TrackingPloS one · 2021Article
- Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models.PLoS computational biology · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The spread of disease through human populations is complex. The characteristics of disease propagation evolve with time, as a result of a multitude of environmental and anthropic factors, this non-stationarity is a key factor in this huge complexity. In the absence of appropriate external data sources, to correctly describe the disease propagation, we explore a flexible approach, based on stochastic models for the disease dynamics, and on diffusion processes for the parameter dynamics. Using such a diffusion process has the advantage of not requiring a specific mathematical function for the parameter dynamics. Coupled with particle MCMC, this approach allows us to reconstruct the time evolution of some key parameters (average transmission rate for instance). Thus, by capturing the time-varying nature of the different mechanisms involved in disease propagation, the epidemic can be described. Firstly we demonstrate the efficiency of this methodology on a toy model, where the parameters and the observation process are known. Applied then to real datasets, our methodology is able, based solely on simple stochastic models, to reconstruct complex epidemics, such as flu or dengue, over long time periods. Hence we demonstrate that time-varying parameters can improve the accuracy of model performances, and we suggest that our methodology can be used as a first step towards a better understanding of a complex epidemic, in situation where data is limited and/or uncertain.
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