A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases
Ver/ Abrir
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
comunitat-uji-handle4:
INVESTIGACIONMetadatos
Título
A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 casesFecha de publicación
2022Editor
SpringerISSN
1436-3240; 1436-3259Cita bibliográfica
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-wTipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://link.springer.com/article/10.1007/s00477-021-02168-wVersión
info:eu-repo/semantics/submittedVersionPalabras clave / Materias
Resumen
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. [-]
Publicado en
Stochastic Environmental Research and Risk Assessment (2022)Entidad financiadora
Ministerio de Ciencia, Innovación y Universidades | Universitat Jaume I | Comissió Europea
Código del proyecto o subvención
MICIU/ICTI2017-2020/PID2019-107392RB-I00 | UJI-B2018-04 | FPA-2016-2054
Url de la subvención
http://dx.doi.org/10.13039/501100011033
Título del proyecto o subvención
Análisis estadístico de eventos en espacio-tiempo sobre redes y trayectorias. características de segundo orden, modelos paramétricos, inferencia y análisis de marcas funcional
Derechos de acceso
© 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
Aparece en las colecciones
- INIT_Articles [751]
- MAT_Articles [762]