2021/04/16 by Chang Liu, Liu, Chang, Akshay Budhkar +1
Computer Science · Decision Sciences · Mathematics · #Advanced Bandit Algorithms Research #COVID-19 epidemiological studies #Computers and Society (cs.CY) #FOS: Computer and information sciences #FOS: Mathematics #Optimization and Control (math.OC) #Risk and Portfolio Optimization #cs.CY #math.OC
paper · pdf · doi:10.48550/arxiv.2104.07865
arxiv created 2021/04/16 · openalex publication_date 2021/04/16 · arxiv updated 2021/04/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In the wake of COVID-19, every government huddles to find the best interventions that will reduce the number of infection cases while minimizing the economic impact. However, with many intervention policies available, how should one decide which policy is the best course of action? In this work, we describe an integer programming approach to prescribe intervention plans that optimizes for both the minimal number of daily new cases and economic impact. We present a method to estimate the impact of intervention plans on the number of cases based on historical data. Finally, we demonstrate visualizations and summaries of our empirical analyses on the performance of our model with varying parameters compared to two sets of heuristics.