Human activity pattern implications for modeling SARS-CoV-2 transmission.
Agent-Based Modeling
COVID-19
Epidemiological Modeling
Human Activity patterns
SARS-CoV-2
SpatioTemporal Human Activity Model
Transmission Dynamics
Journal
Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513
Informations de publication
Date de publication:
Feb 2021
Feb 2021
Historique:
received:
31
08
2020
accepted:
28
11
2020
pubmed:
17
12
2020
medline:
17
2
2021
entrez:
16
12
2020
Statut:
ppublish
Résumé
SARS-CoV-2 emerged in December 2019 and rapidly spread into a global pandemic. Designing optimal community responses (social distancing, vaccination) is dependent on the stage of the disease progression, discovery of asymptomatic individuals, changes in virulence of the pathogen, and current levels of herd immunity. Community strategies may have severe and undesirable social and economic side effects. Modeling is the only available scientific approach to develop effective strategies that can minimize these unwanted side effects while retaining the effectiveness of the interventions. We extended the agent-based model, SpatioTemporal Human Activity Model (STHAM), for simulating SARS-CoV-2 transmission dynamics. Here we present preliminary STHAM simulation results that reproduce the overall trends observed in the Wasatch Front (Utah, United States of America) for the general population. The results presented here clearly indicate that human activity patterns are important in predicting the rate of infection for different demographic groups in the population. Future work in pandemic simulations should use empirical human activity data for agent-based techniques.
Sections du résumé
BACKGROUND AND OBJECTIVES
OBJECTIVE
SARS-CoV-2 emerged in December 2019 and rapidly spread into a global pandemic. Designing optimal community responses (social distancing, vaccination) is dependent on the stage of the disease progression, discovery of asymptomatic individuals, changes in virulence of the pathogen, and current levels of herd immunity. Community strategies may have severe and undesirable social and economic side effects. Modeling is the only available scientific approach to develop effective strategies that can minimize these unwanted side effects while retaining the effectiveness of the interventions.
METHODS
METHODS
We extended the agent-based model, SpatioTemporal Human Activity Model (STHAM), for simulating SARS-CoV-2 transmission dynamics.
RESULTS
RESULTS
Here we present preliminary STHAM simulation results that reproduce the overall trends observed in the Wasatch Front (Utah, United States of America) for the general population. The results presented here clearly indicate that human activity patterns are important in predicting the rate of infection for different demographic groups in the population.
CONCLUSIONS
CONCLUSIONS
Future work in pandemic simulations should use empirical human activity data for agent-based techniques.
Identifiants
pubmed: 33326924
pii: S0169-2607(20)31729-6
doi: 10.1016/j.cmpb.2020.105896
pmc: PMC7722504
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
105896Subventions
Organisme : NIH HHS
ID : S10 OD021644
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001067
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002538
Pays : United States
Informations de copyright
Copyright © 2020. Published by Elsevier B.V.
Déclaration de conflit d'intérêts
Declaration of Competing Interest None
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