Log In Sign Up

Data-driven Optimization Model for Global Covid-19 Intervention Plans

by   Chang Liu, et al.

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.


page 1

page 2

page 3

page 4


Optimal Epidemic Control as a Contextual Combinatorial Bandit with Budget

In light of the COVID-19 pandemic, it is an open challenge and critical ...

Hawkes Processes for Invasive Species Modeling and Management

The spread of invasive species to new areas threatens the stability of e...

Masks and COVID-19: a causal framework for imputing value to public-health interventions

During the COVID-19 pandemic, the scientific community developed predict...

Impact of Interventional Policies Including Vaccine on Covid-19 Propagation and Socio-Economic Factors

A novel coronavirus disease has emerged (later named COVID-19) and cause...

Selecting the Most Effective Nudge: Evidence from a Large-Scale Experiment on Immunization

We evaluate a large-scale set of interventions to increase demand for im...

Reinforcement Learning for Optimization of COVID-19 Mitigation policies

The year 2020 has seen the COVID-19 virus lead to one of the worst globa...