Computational protocol to perform a spatiotemporal reconstruction of an epidemic

Here, we present a computational protocol to perform a spatiotemporal reconstruction of an epidemic. We describe steps for using epidemiological data to depict how the epidemic changes over time and for employing clustering analysis to group geographical units that exhibit similar temporal epidemic...

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Detalles Bibliográficos
Autores principales: Nodari, Riccardo, Perini, Matteo, Fois, Luca, Sterzi, Lodovico, Luconi, Ester, Vaglienti, Folco, Bandi, Claudio, Biganzoli, Elia, Galli, Massimo, Comandatore, Francesco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Protocol
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10514217/
https://www.ncbi.nlm.nih.gov/pubmed/37717214
http://dx.doi.org/10.1016/j.xpro.2023.102548
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author Nodari, Riccardo
Perini, Matteo
Fois, Luca
Sterzi, Lodovico
Luconi, Ester
Vaglienti, Folco
Bandi, Claudio
Biganzoli, Elia
Galli, Massimo
Comandatore, Francesco
author_facet Nodari, Riccardo
Perini, Matteo
Fois, Luca
Sterzi, Lodovico
Luconi, Ester
Vaglienti, Folco
Bandi, Claudio
Biganzoli, Elia
Galli, Massimo
Comandatore, Francesco
author_sort Nodari, Riccardo
collection PubMed
description Here, we present a computational protocol to perform a spatiotemporal reconstruction of an epidemic. We describe steps for using epidemiological data to depict how the epidemic changes over time and for employing clustering analysis to group geographical units that exhibit similar temporal epidemic progression. We then detail procedures for analyzing the temporal and spatial dynamics of the epidemic within each cluster. This protocol has been developed to be used on historical data but could also be applied to modern epidemiological data. For complete details on the use and execution of this protocol, please refer to Galli et al. (2023).(1)
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spelling pubmed-105142172023-09-23 Computational protocol to perform a spatiotemporal reconstruction of an epidemic Nodari, Riccardo Perini, Matteo Fois, Luca Sterzi, Lodovico Luconi, Ester Vaglienti, Folco Bandi, Claudio Biganzoli, Elia Galli, Massimo Comandatore, Francesco STAR Protoc Protocol Here, we present a computational protocol to perform a spatiotemporal reconstruction of an epidemic. We describe steps for using epidemiological data to depict how the epidemic changes over time and for employing clustering analysis to group geographical units that exhibit similar temporal epidemic progression. We then detail procedures for analyzing the temporal and spatial dynamics of the epidemic within each cluster. This protocol has been developed to be used on historical data but could also be applied to modern epidemiological data. For complete details on the use and execution of this protocol, please refer to Galli et al. (2023).(1) Elsevier 2023-09-16 /pmc/articles/PMC10514217/ /pubmed/37717214 http://dx.doi.org/10.1016/j.xpro.2023.102548 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Protocol
Nodari, Riccardo
Perini, Matteo
Fois, Luca
Sterzi, Lodovico
Luconi, Ester
Vaglienti, Folco
Bandi, Claudio
Biganzoli, Elia
Galli, Massimo
Comandatore, Francesco
Computational protocol to perform a spatiotemporal reconstruction of an epidemic
title Computational protocol to perform a spatiotemporal reconstruction of an epidemic
title_full Computational protocol to perform a spatiotemporal reconstruction of an epidemic
title_fullStr Computational protocol to perform a spatiotemporal reconstruction of an epidemic
title_full_unstemmed Computational protocol to perform a spatiotemporal reconstruction of an epidemic
title_short Computational protocol to perform a spatiotemporal reconstruction of an epidemic
title_sort computational protocol to perform a spatiotemporal reconstruction of an epidemic
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10514217/
https://www.ncbi.nlm.nih.gov/pubmed/37717214
http://dx.doi.org/10.1016/j.xpro.2023.102548
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