An Agent Based Model methodology for assessing spread and health systems burden for Covid-19 using a synthetic population from India

MS, N.; Jammy, G. R.; Pant, R.; Choudhury, L.; R, A.; Yeldandi, V.; SP, A.; P, R.

Covid-19 disease, caused by SARS-CoV-2 virus, has infected over four million people globally. It has been declared as a global public health emergency by the World Health Organization. Researchers and governments are striving to do their best to fight against this pandemic. Several Mathematical models mostly based on compartmental modeling are being used for projections for Covid-19 in India. These projections are used for policy level decisions and public health prevention activities. Compartmental models are mostly used for Covid-19 projections. Unlike compartmental models, which consider population average, the Agent based models (ABM) models consider individual behavior in the models for projections. ABMs, yet rarely used for Covid-19, provide better insights into projections compared to compartmental models. We present an ABM approach with a small synthetic population of India, to examine the patterns and trends of the Covid-19 in terms of infected, admitted, critical cases requiring intensive care and/ or ventilator support, mortality and recovery. The parameters for the ABM model are defined and model run for a period of 365 days for three different non-pharmaceutical intervention (NPI) scenarios. AnyLogic platform was used for the ABM simulations. Results revealed that the peak values and slope of the curve declined as NPI became more stringent. The results could provide a platform for researchers and modelers to explore this approach for conducting ABM for Covid-19 projections with inclusion of interventions and health system preparedness.