Deep Reinforcement Learning Interdependent Healthcare Critical Infrastructure Simulation model for Dynamically Varying COVID-19 scenario – A case study of a Metro City

Inability to respond to the growing trend of COVID -19 cases and the study and analysis of Healthcare Critical Infrastructure interdependencies during COVID-19 pandemic scenario is relatively new. One of the most frequently identified shortfalls in knowledge related to enhancing Healthcare Critical Infrastructure (HCI) preparedness during the COVID-19 pandemic scenario is the inability to forecast the growth trend of COVID-19 cases in a geographic area and incomplete understanding of interdependencies between Critical infrastructures related to HCI. As the number of cases surges at a healthcare facility, the facility, and its interdependent CI services should be prepared to handle the susceptible stress. The goal of the paper is to be able to predict the growth trend of COVID-19 cases using Spatiotemporal Long Short-Term Memory (ST-LSTM) for a geographic area. Based on the predicted growth trend of the COVID-19 cases a Multi-Agent Deep Reinforcement Learning (MADRL) simulation model will provide an accurate representation of healthcare critical infrastructure characteristics, operations, and interdependencies services.