A mechanistic spatio-temporal modeling of COVID-19 data
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Scholar |
Otros documentos de la autoría: Briz-Redón, Álvaro; Iftimi, Adina; Mateu, Jorge; Romero Garcia, Carolina Soledad
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https://doi.org/10.1002/bimj.202100318 |
Metadatos
Título
A mechanistic spatio-temporal modeling of COVID-19 dataFecha de publicación
2022-08-07Editor
Wiley-VCHGmbHISSN
0323-3847; 1521-4036Cita bibliográfica
Briz-Redón, Á., Iftimi, A., Mateu, J., & Romero-García, C. (2022). A mechanistic spatio-temporal modeling of COVID-19 data. Biometrical Journal, 00, 1– 18. https://doi.org/10.1002/bimj.202100318Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://onlinelibrary.wiley.com/doi/10.1002/bimj.202100318Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
Understandingtheevolutionofanepidemicisessentialtoimplementtimelyandefficient preventive measures. The availability of epidemiological data at a finespatio-temporal scale is both novel and highly useful in this regard. ... [+]
Understandingtheevolutionofanepidemicisessentialtoimplementtimelyandefficient preventive measures. The availability of epidemiological data at a finespatio-temporal scale is both novel and highly useful in this regard. Indeed, hav-ing geocoded data at the case level opens the door to analyze the spread of thedisease on an individual basis, allowing the detection of specific outbreaks or, ingeneral, of some interactions between cases that are not observable if aggregateddata are used. Point processes are the natural tool to perform such analyses. Weanalyze a spatio-temporal point pattern of Coronavirus disease 2019 (COVID-19)cases detected in Valencia (Spain) during the first 11 months (February 2020 toJanuary 2021) of the pandemic. In particular, we propose a mechanistic spatio-temporal model for the first-order intensity function of the point process. Thismodel includes separate estimates of the overall temporal and spatial intensitiesof the model and a spatio-temporal interaction term. For the latter, while similarstudies have considered different forms of this term solely based on the physicaldistances between the events, we have also incorporated mobility data to bettercapture the characteristics of human populations. The results suggest that therehas only been a mild level of spatio-temporal interaction between cases in thestudy area, which to a large extent corresponds to people living in the same res-idential location. Extending our proposed model to larger areas could help usgain knowledge on the propagation of COVID-19 across cities with high mobilitylevels. [-]
Descripción
This article has earned an open data
badge “Reproducible Research” for
making publicly available the code
necessary to reproduce the reported
results. The results reported in this article
could fully be reproduced.
Publicado en
Biomedical Journal (2022)Entidad financiadora
Valencia Innovation Agency (AVI)
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© 2022 Wiley-VCH GmbH.
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