Less Wrong COVID-19 Projections With Interactive Assumptions

COVID-19 pandemic is an enigma with uncertainty caused by biological and health systems factors. Although many models have been developed all around the world, transparent models that allow interacting with the assumptions will become more important as we test various strategies for lockdown, testing and social interventions and enable effective policy decisions. In this paper we developed a suite of models to guide development of policies under different scenarios when the lockdown opens. These had been deployed to create an interactive dashboard called COVision which includes the Agent based Models (ABM) and classical compartmental models i.e Susceptible-Infected-Recovered (SIR) and Susceptible-Exposed-Infected-Recovered (SEIR) approaches. Our tool allows simulation of scenarios by changing strength of lockdown, basic reproduction number(R0), asymptomatic spread, testing rate, contact rate (Beta), recovery rate (Gamma), incubation period and starting number of cases. We optimized ABMs and classical compartmental models to fit the actual data, both of which performed well in terms of R-squared, root mean squared error (RMSE) and mean absolute percentage error (MAPE). Out of the three models in our suite, ABM was able to capture the data better than SIR and SEIR and achieved an RSQ of 92.3% for India and 89% for Maharashtra for the next 30 days. We also computed R0 using SIR and SEIR models which were found to be decreasing over the different periods of lockdown indicating the effectiveness of policies and interventions. Finally, we formulated ICU bed requirements using our best models. Our evaluation suggests that ABM models were able to capture the dynamic nature of the epidemic for a longer duration of time while classical SIR and SEIR models performed inefficiently for longer terms. The visual interactivity and ability to simulate outcomes under different parameters will allow the policymakers to make informed decisions for estimating the strength of lockdown to be implemented and testing rates. Further, our models were able to highlight the differences at state level for the parameters such as R0 and contact rates and hence can be applied for state specific decision making. An interactive dashboard http://covision.tavlab.iiitd.edu.in have been hosted as a web-server for the war level monitoring of the covid19 pandemic in India in public domain