Journal
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
Volume 8, Issue 3, Pages 2262-2278Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TNSE.2021.3085866
Keywords
Epidemics; Testing; COVID-19; Statistics; Sociology; Resource management; Optimization; Epidemic Processes on Networks; SIR Epidemic Model; COVID-19; Nonlinear Observer; Bayesian Inference; Parameter Estimation; Non-Pharmaceutical Interventions; Geometric Programming
Funding
- Mitigation of COVID-19 and Future Pandemics from the c3.ai Digital Transformation Institute - C3.ai Inc.
- Microsoft Corporation
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Motivated by the COVID-19 pandemic, a closed-loop framework is proposed to control the spread of the epidemic on networks by incorporating key factors in testing data and evaluating the trade-off between controlling the growth-rate of the epidemic and the cost of NPIs. Results show the importance of early testing and the risk of a second wave of infections if NPIs are prematurely withdrawn.
Motivated by the ongoing pandemic COVID-19, we propose a closed-loop framework that combines inference from testing data, learning the parameters of the dynamics and optimal resource allocation for controlling the spread of the susceptible-infected-recovered (SIR) epidemic on networks. Our framework incorporates several key factors present in testing data, such as the fact that high risk individuals are more likely to undergo testing. We then present two tractable optimization problems to evaluate the trade-off between controlling the growth-rate of the epidemic and the cost of non-pharmaceutical interventions (NPIs). We illustrate the significance of the proposed closed-loop framework via extensive simulations and analysis of real, publicly-available testing data for COVID-19. Our results illustrate the significance of early testing and the emergence of a second wave of infections if NPIs are prematurely withdrawn.
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