Emerging dynamics from high-resolution spatial numerical epidemics.
computational biology
epidemiology
global health
high perfomance computing
parallel computing
systems biology
Journal
eLife
ISSN: 2050-084X
Titre abrégé: Elife
Pays: England
ID NLM: 101579614
Informations de publication
Date de publication:
15 10 2021
15 10 2021
Historique:
received:
18
06
2021
accepted:
07
10
2021
pubmed:
16
10
2021
medline:
25
11
2021
entrez:
15
10
2021
Statut:
epublish
Résumé
Simulating nationwide realistic individual movements with a detailed geographical structure can help optimise public health policies. However, existing tools have limited resolution or can only account for a limited number of agents. We introduce Epidemap, a new framework that can capture the daily movement of more than 60 million people in a country at a building-level resolution in a realistic and computationally efficient way. By applying it to the case of an infectious disease spreading in France, we uncover hitherto neglected effects, such as the emergence of two distinct peaks in the daily number of cases or the importance of local density in the timing of arrival of the epidemic. Finally, we show that the importance of super-spreading events strongly varies over time.
Identifiants
pubmed: 34652271
doi: 10.7554/eLife.71417
pii: 71417
pmc: PMC8568339
doi:
pii:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2021, Thomine et al.
Déclaration de conflit d'intérêts
OT, SA, CB, MB, MS No competing interests declared
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