A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases
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INVESTIGACIONMetadades
Títol
A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 casesData de publicació
2022Editor
SpringerISSN
1436-3240; 1436-3259Cita bibliogràfica
Niraula, P., Mateu, J. & Chaudhuri, S. A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases. Stoch Environ Res Risk Assess (2022). https://doi.org/10.1007/s00477-021-02168-wTipus de document
info:eu-repo/semantics/articleVersió de l'editorial
https://link.springer.com/article/10.1007/s00477-021-02168-wVersió
info:eu-repo/semantics/submittedVersionParaules clau / Matèries
Resum
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 ... [+]
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. [-]
Publicat a
Stochastic Environmental Research and Risk Assessment (2022)Entitat finançadora
Ministerio de Ciencia, Innovación y Universidades | Universitat Jaume I | Comissió Europea
Codi del projecte o subvenció
MICIU/ICTI2017-2020/PID2019-107392RB-I00 | UJI-B2018-04 | FPA-2016-2054
Url de la subvenció
http://dx.doi.org/10.13039/501100011033
Títol del projecte o subvenció
Análisis estadístico de eventos en espacio-tiempo sobre redes y trayectorias. características de segundo orden, modelos paramétricos, inferencia y análisis de marcas funcional
Drets d'accés
© Springer Nature Switzerland AG. Part of Springer Nature.
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/openAccess
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/openAccess
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