| Abstract: |
| Most of the studies on COVID-19 assumes a constant transmission rate although it is not so since it depends on human mobility and behavior. In this research, we present a stochastic SEIRD epidemiological model of COVID-19 by considering the virus transmission rate and the patient recovery rate as random processes. For controlling the pandemic, we consider three control options: a) social contact mitigation and suppression, b) use of novel treatment modalities (during early stage of pandemic when vaccines were not available), and c) vaccination. Fundamentally the pandemic intervention problem can be viewed as a mathematical optimization problem as there are contradictory outcomes in terms of reduced infection and fatalities but with serious economic downturns. Concepts of stochastic optimal control theory are used to determine the optimal control (intervention) policy. Simulation results show the effectiveness of the control policy. |
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