A Closed-Loop Framework for Inference, Prediction, and Control of SIR Epidemics on Networks

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...

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Bibliographic Details
Published in:IEEE transactions on network science and engineering 2021-07, Vol.8 (3), p.2262-2278
Main Authors: Hota, Ashish R., Godbole, Jaydeep, Pare, Philip E.
Format: Article
Language:English
Subjects:
Bayesian Inference
Coronaviruses
COVID-19
Disease control
Epidemic Processes on Networks
Epidemics
Geometric Programming
Inference
Non-Pharmaceutical Interventions
Nonlinear Observer
Optimization
Parameter Estimation
Resource allocation
Resource management
SIR Epidemic Model
Sociology
Statistics
Testing
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Summary: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illustrate the significance of early testing and the emergence of a second wave of infections if NPIs are prematurely withdrawn.
ISSN:2327-4697
2334-329X
DOI:10.1109/TNSE.2021.3085866