A Decision Guidance System for COVID-19 Comprehensive Mitigation with Pareto-Optimal Health, Cost and Productivity Outcomes
Additional Files: techreports/GMU-CS-TR-2021-2.bib
This paper reports on the design and development of a decision guidance system to make actionable recommendations on a COVID-19 comprehensive mitigation protocol that is Pareto-Optimal in terms of health outcomes, mitigation cost and productivity loss. The comprehensive mitigation protocol includes personal protection and social distancing; use of smart applications for symptom reporting and contact tracing; targeted testing based on identification of individuals with possible exposure and/or infection via symptom reporting and contact tracing; random surveillance testing, and; shelter, quarantine and isolation procedures. The decision guidance system (1) gets, as input, expert-generated configurations of epidemiological parameters and assumptions on population behavior, (2) precomputes a database of discretized Pareto-optimal mitigation protocol alternatives based on which it (3) provides decision makers an iterative methodology of (a) Pareto-optimal KPI graphing and trade-off analysis (between health, cost and productivity outcomes), (b) detailed comparison of selected Pareto-optimal mitigation protocol alternatives, and (c) what-if analysis for selected protocol alternatives, including disease progression over the time horizon and sensitivity analysis to refine and converge on the mitigation protocol to be used.
Modeling of COVID-19 Transmission Dynamics Extended with a Comprehensive Mitigation Protocol to Predict Health, Cost and Productivity Outcomes
Additional Files: techreports/GMU-CS-TR-2021-1.bib
This paper reports on the development of a model of COVID-19 transmission dynamics that takes into account a comprehensive mitigation protocol to better inform decision makers on COVID-19 response. The comprehensive mitigation protocol includes (1) personal protection and social distancing, (2) use of smart applications for symptom reporting and contact tracing, (3) targeted testing based on identification of individuals with possible exposure and/or infection via symptom reporting and contact tracing, (4) surveillance testing, and (5) shelter, quarantine and isolation procedures. The proposed model (1) extends a common epidemiological discrete dynamic model with the comprehensive mitigation protocol, (2) uses Bayesian probability analysis to estimate the conditional probabilities of being in non-circulating epidemiological sub-compartments as a function of the mitigation protocol parameters, based on which it (3) estimates transition ratios among the compartments, and (4) computes a range of key performance indicators including health outcomes, mitigation cost and productivity loss. The proposed model can serve as a critical component for COVID-19 mitigation recommender systems, as part of a broader effort to support urgent pandemic response.