Epidemic Control Modeling using Parsimonious Models and Markov Decision Processes
This addresses epidemic control for public health policymakers, but it is incremental as it applies existing modeling frameworks to COVID-19 data.
The paper tackled controlling COVID-19 epidemics by developing a parsimonious stochastic model and Markov decision process to optimize trade-offs between healthcare usage and economic costs, applied to New Delhi data showing that swift action in the first wave could have prevented healthcare collapse and saved lives.
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.