COVID-19 and Rumors: A Dynamic Nested Optimal Control Model

Unfortunately, the COVID-19 outbreak has been accompanied by the spread of rumors and depressing news. Herein, we develop a dynamic nested optimal control model of COVID-19 and its rumor outbreaks. The model aims to curb the epidemics by reducing the number of individuals infected with COVID-19 and reducing the number of rumor-spreaders while minimizing the cost associated with the control interventions. We use the modified approximation Karush–Kuhn–Tucker conditions with the Hamiltonian function to simplify the model before solving it using a genetic algorithm. The present model highlights three prevention measures that affect COVID-19 and its rumor outbreaks. One represents the interventions to curb the COVID-19 pandemic. The other two represent interventions to increase awareness, disseminate the correct information, and impose penalties on the spreaders of false rumors. The results emphasize the importance of interventions in curbing the spread of the COVID-19 pandemic and its associated rumor problems alike.