Year: 2021
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Liu, Chang, Budhkar, Akshay
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3154896482