An Agent Based Model for assessing spread and health systems burden for COVID-19 using a synthetic population in Telangana state, India

ObjectivesTo assess the transmission dynamics and the health systems burden of COVID-19 using an Agent Based Modeling (ABM) approach using a synthetic population. Study designThe study used a synthetic population with 31,738,240 agents representing 90.67 percent of the overall population of Telangana state, India as per 2011 Census of India. Lockdown phases as per Indian scenario considering the effects of post-lockdown, use of control measures and immunity on secondary infections were studied. MethodsThe counts of people in different health states were measured separately for each district of Telangana. The model was run for 365 days and six scenarios with varying proportions of people using control measures (100%, 75% and 50%) and varying immunity periods (90 and 180 days). Sensitivity Analysis has been done for two districts to compare the change in transmission dynamics when incubation period and asymptomatic proportion are changed. ResultsResults indicate that the peak values were attained soon after the lockdown was lifted. The risk estimates indicate that protection factor values are higher when more proportion of people adopt control measures. ConclusionsABM approach helps to analyze grassroot details compared to compartmental models. Risk estimates allow the policymakers to determine the protection offered, its strength and percentage of population shielded by use of control measures.