TY - JOUR
T1 - SIRTEM
T2 - Spatially Informed Rapid Testing for Epidemic Modeling and Response to COVID-19
AU - Azad, Fahim Tasneema
AU - Dodge, Robert W.
AU - Varghese, Allen M.
AU - Lee, Jaejin
AU - Pedrielli, Giulia
AU - Selçuk Candan, K.
AU - Chowell-Puente, Gerardo
N1 - Funding Information:
This research is supported by NSF#2026860 “RTEM: Rapid Testing as Multi-fidelity Data Collection for Epidemic Modeling,” NSF#2125246 “PanCommunity: Leveraging Data and Models for Understanding and Improving Community Response in Pandemics,” NSF#1610282 “DataStorm: A Data Enabled System for End-to-End Disaster Planning and Response,” NS#1633381 “BIGDATA: Discovering Context-Sensitive Impact in Complex Systems,” and NSF#1909555 “pCAR: Discovering and Leveraging Plausibly Causal (p-causal) Relationships to Understand Complex Dynamic Systems.” Results were obtained using the Chameleon testbed supported by the NSF.
Publisher Copyright:
© 2022 Association for Computing Machinery.
PY - 2022/11/2
Y1 - 2022/11/2
N2 - 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.
AB - 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.
KW - COVID-19
KW - multi-accuracy testing
KW - multi-city mixing
UR - http://www.scopus.com/inward/record.url?scp=85146437360&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146437360&partnerID=8YFLogxK
U2 - 10.1145/3555310
DO - 10.1145/3555310
M3 - Article
AN - SCOPUS:85146437360
SN - 2374-0353
VL - 8
JO - ACM Transactions on Spatial Algorithms and Systems
JF - ACM Transactions on Spatial Algorithms and Systems
IS - 4
M1 - 3555310
ER -