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.
翻译：在COVID-19之后,每个政府都聚集在一起,寻找最佳的干预措施,减少感染病例数量,同时尽量减少经济影响。然而,随着许多干预政策的出台,人们应该如何决定哪一种政策是最佳行动方针?在这项工作中,我们描述一个整数的方案拟订办法,以规定干预计划,以最佳的方式应对每日新病例的最低数量和经济影响。我们提出了一种方法,用以根据历史数据估计干预计划对病例数量的影响。最后,我们展示了我们对模型绩效的经验分析的可视化和摘要,其参数与两套牛皮学相比各不相同。
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