The present pandemic has tremendously raised the health systems’ burden around the globe. It is important to understand the transmission dynamics of the infection and impose localized strategies across different geographies to curtail the spread of the infection. The present study was designed to assess the transmission dynamics and the health systems’ burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using an agent-based modeling (ABM) approach. The study used a synthetic population with 31,738,240 agents representing 90.67 percent of the overall population of Telangana, India. The effects of imposing and lifting lockdowns, nonpharmaceutical interventions, and the role of immunity were analyzed. The distribution of people in different health states was measured separately for each district of Telangana. The spread dramatically increased and reached a peak soon after the lockdowns were relaxed. It was evident that is the protection offered is higher when a higher proportion of the population is exposed to the interventions. ABMs help to analyze grassroots details compared to compartmental models. Risk estimates provide insights on the proportion of the population protected by the adoption of one or more of the control measures, which is of practical significance for policymaking.

Objectives: To assess the transmission dynamics and the health systems burden of COVID-19 using an Agent Based Modeling (ABM) approach using a synthetic population. Study design: The 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. District-level localized parameters were assigned to agents as local models prove to be much helpful for policymakers. Methods: The 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 of recovered patients (90 and 180 days). Results: Results 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 such as use of face mask and social distancing. Population Attributable Risk values measured longitudinally indicated higher values like 60.41% and 47.18% when 75 percent of people followed control measures during lockdowns. Conclusions: ABM approach helps to analyze grassroot details compared to compartmental models. Risk estimates allows the policymakers to determine the protection offered, its strength and percentage of population shielded by use of control measures.