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SIRTEM: Spatially Informed Rapid Testing for Epidemic Modeling and Response to COVID-19

Authors :
Fahim Tasneema Azad
Robert W. Dodge
Allen M. Varghese
Jaejin Lee
Giulia Pedrielli
K. Selçuk Candan
Gerardo Chowell-Puente
Source :
ACM Transactions on Spatial Algorithms and Systems. 8:1-43
Publication Year :
2022
Publisher :
Association for Computing Machinery (ACM), 2022.

Abstract

COVID-19 outbreak was declared a pandemic by the World Health Organization on March 11, 2020. To minimize casualties and the impact on the economy, various mitigation measures have being employed with the purpose to slow the spread of the infection, such as complete lockdown, social distancing, and random testing. The key contribution of this article is twofold. First, we present a novel extended spatially informed epidemic model, SIRTEM, Spatially Informed Rapid Testing for Epidemic Modeling and Response to COVID-19 , that integrates a multi-modal testing strategy considering test accuracies. Our second contribution is an optimization model to provide a cost-effective testing strategy when multiple test types are available. The developed optimization model incorporates realistic spatially based constraints, such as testing capacity and hospital bed limitation as well.

Details

ISSN :
23740361 and 23740353
Volume :
8
Database :
OpenAIRE
Journal :
ACM Transactions on Spatial Algorithms and Systems
Accession number :
edsair.doi...........16164225d0906d275ffe62d2e3ca198e
Full Text :
https://doi.org/10.1145/3555310