Theoretical Models and Preliminary Results for Contact Tracing and Isolation
21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022
; 3:1672-1674, 2022.
Article
in English
| Scopus | ID: covidwho-1958140
ABSTRACT
Efficient contact tracing and isolation is an effective strategy to control epidemics, as seen in the Ebola epidemic and COVID-19 pandemic. An important consideration in contact tracing is the budget on the number of individuals asked to quarantine-the budget is limited for socioeconomic reasons (e.g., having a limited number of contact tracers). Here, we present a Markov Decision Process (MDP) framework to formulate the problem of using contact tracing to reduce the size of an outbreak while limiting the number of people quarantined. We formulate each step of the MDP as a combinatorial problem, MinExposed, which we demonstrate is NP-Hard. Next, we develop two approximation algorithms, one based on rounding the solutions of a linear program and another (greedy algorithm) based on choosing nodes with a high (weighted) degree. A key feature of the greedy algorithm is that it does not need complete information of the underlying social contact network, making it implementable in practice. Using simulations over realistic networks, we show how the algorithms can help in bending the epidemic curve with a limited number of isolated individuals. © 2022 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
Approximation Algorithms; Contact Tracing; COVID-19; Autonomous agents; Budget control; Disease control; Linear programming; Markov processes; Multi agent systems; Combinatorial problem; Greedy algorithms; Markov Decision Processes; NP-hard; Number of contacts; Number of peoples; Process framework; Socio-economics; Theoretical modeling
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022
Year:
2022
Document Type:
Article
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