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About:
An Agent Based Modeling of COVID-19: Validation, Analysis, and Recommendations
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covidontheweb.inria.fr
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type
Academic Article
research paper
schema:ScholarlyArticle
isDefinedBy
Covid-on-the-Web dataset
title
An Agent Based Modeling of COVID-19: Validation, Analysis, and Recommendations
Creator
Khan, Mahmud
Farheen, Farhanaz
Ibtehaz, Nabil
Shamil, Md
Sohel, M
source
MedRxiv
abstract
The Coronavirus disease 2019 (COVID-19) has resulted in an ongoing pandemic worldwide. Countries have adopted Non-pharmaceutical Interventions (NPI) to slow down the spread. This study proposes an Agent Based Model that simulates the spread of COVID-19 among the inhabitants of a city. The Agent Based Model can be accommodated for any location by integrating parameters specific to the city. The simulation gives the number of daily confirmed cases. Considering each person as an agent susceptible to COVID-19, the model causes infected individuals to transmit the disease via various actions performed every hour. The model is validated by comparing the simulation to the real data of Ford county, Kansas, USA. Different interventions including contact tracing are applied on a scaled down version of New York city, USA and the parameters that lead to a controlled epidemic are determined. Our experiments suggest that contact tracing via smartphones with more than 60% of the population owning a smartphone combined with a city-wide lock-down results in the effective reproduction number (Rt) to fall below 1 within three weeks of intervention. In the case of 75% or more smartphone users, new infections are eliminated and the spread is contained within three months of intervention. Contact tracing accompanied with early lock-down can suppress the epidemic growth of COVID-19 completely with sufficient smartphone owners. In places where it is difficult to ensure a high percentage of smartphone ownership, tracing only emergency service providers during a lock-down can go a long way to contain the spread. No particular funding was available for this project.
has issue date
2020-07-08
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bibo:doi
10.1101/2020.07.05.20146977
has license
medrxiv
sha1sum (hex)
24792092b3c82883bb53192c311788f4a5ba5606
schema:url
https://doi.org/10.1101/2020.07.05.20146977
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An Agent Based Modeling of COVID-19: Validation, Analysis, and Recommendations
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covid:24792092b3c82883bb53192c311788f4a5ba5606#body_text
is
schema:about
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named entity 'THREE WEEKS'
named entity 'CONTACT TRACING'
named entity 'SPECIFIC'
named entity 'EXPERIMENTS'
named entity 'EMERGENCY SERVICE'
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