Model-based Prediction and Optimal Control of Pandemics by Nonpharmaceutical Interventions

• Type of Material: Article
• Authors: Reza Sameni, Emory University
• Language: English
• Date: 2021-11-18
• Publisher: Institute of Electrical and Electronics Engineers
• Publication Version: Preprint (Prior to Peer Review)
• Copyright Statement: 2021 the author
• Final Published Version (URL): http://dx.doi.org/10.1109/JSTSP.2021.3129118
• Title of Journal or Parent Work: IEEE Journal of Selected Topics in Signal Processing
• Keywords: finite-horizon optimal control; pandemic forcasting; non-pharmaceutical interventions; COVID-19; pandemic control; compartmental modeling; extended Kalman filtering
• Research Categories: Health Sciences, Health Care Management; Health Sciences, Public Health

Abstract

A model-based signal processing framework is proposed for pandemic trend forecasting and control, by using non-pharmaceutical interventions (NPI) at regional and country levels worldwide. The control objective is to prescribe quantifiable NPI strategies at different levels of stringency, which balance between human factors (such as new cases and death rates) and cost of intervention per region/country. Due to infrastructural disparities and differences in priorities of regions and countries, strategists are given the flexibility to weight between different NPIs and to select the desired balance between the human factor and overallNPI cost. The proposed framework is based on a finite-horizon optimal control (FHOC) formulation of the bi-objective problem and the FHOC is numerically solved by using an ad hoc extended Kalman filtering/smoothing framework for optimal NPI estimation and pandemic trend forecasting. The algorithm enables strategists to select the desired balance between the human factor and NPI cost with a set of weights and parameters. The parameters of the model, are partially selected by epidemiological facts from COVID-19 studies, and partially trained by using machine learning techniques. The developed algorithm is applied on ground truth data from the Oxford COVID-19 Government Response Tracker project, which has categorized and quantified the regional responses to the pandemic for more than 300 countries and regions worldwide, since January 2020. The dataset was used for NPI-based prediction and prescription during the XPRIZE Pandemic Response Challenge.