Epidemic Control Modeling using Parsimonious Models and Markov Decision Processes

TL;DR

This paper presents a simplified stochastic epidemic model combined with a Markov decision process to optimize COVID-19 control policies, demonstrating how swift action can prevent healthcare collapse and save lives.

Contribution

It introduces a parsimonious epidemic model and an optimal policy framework for balancing healthcare and economic costs during COVID-19.

Findings

Optimal policies act swiftly to prevent healthcare system collapse.

Simulations show early intervention reduces total epidemic costs.

Analysis suggests delayed response contributed to India's healthcare crisis.

Abstract

Many countries have experienced at least two waves of the COVID-19 pandemic. The second wave is far more dangerous as distinct strains appear more harmful to human health, but it stems from the complacency about the first wave. This paper introduces a parsimonious yet representative stochastic epidemic model that simulates the uncertain spread of the disease regardless of the latency and recovery time distributions. We also propose a Markov decision process to seek an optimal trade-off between the usage of the healthcare system and the economic costs of an epidemic. We apply the model to COVID-19 data from New Delhi, India and simulate the epidemic spread with different policy review times. The results show that the optimal policy acts swiftly to curb the epidemic in the first wave, thus avoiding the collapse of the healthcare system and the future costs of posterior outbreaks. An analysis of the recent collapse of the healthcare system of India during the second COVID-19 wave suggests that many lives could have been preserved if swift mitigation was promoted after the first wave.