ArticleStochastic environmental research and risk assessment : research journal2022
A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases.
Article in Stochastic environmental research and risk assessment : research journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 3 of them syntheses that pooled it.
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Who cites it
7 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- Spatial and spatiotemporal machine learning models for COVID-19 dynamics: a review of methodology and reporting practices.Epidemiologic reviews · 2025Pooled it
- Recent advances in machine learning and Bayesian modeling for tropical disease prediction, diagnosis, and risk analysis: a scoping review.BMC infectious diseases · 2026Article
- Spatio-temporal modelling of COVID-19 infection and associated risk factors in Dakar, Senegal.PLOS global public health · 2026Article
- Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review.Bioengineering (Basel, Switzerland) · 2025Review
- Spatio-temporal forecasting of COVID-19 cases in the Netherlands for source and contact tracing.International journal of population data science · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modeling the spread of infectious diseases in space and time needs to take care of complex dependencies and uncertainties. Machine learning methods, and neural networks, in particular, are useful in modeling this sort of complex problems, although they generally lack of probabilistic interpretations. We propose a neural network method embedded in a Bayesian framework for modeling and predicting the number of cases of infectious diseases in areal units. A key feature is that our combined model considers the impact of human movement on the spread of the infectious disease, as an additional random factor to the also considered spatial neighborhood and temporal correlation components. Our model is evaluated over a COVID-19 dataset for 245 health zones of Castilla-Leon (Spain). The results show that a Bayesian model informed by a neural network method is generally able to predict the number of cases of COVID-19 in both space and time, with the human mobility factor having a strong influence on the model, together with the number of infections and deaths in nearby areas.
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Registered trials
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