Less Wrong COVID-19 Projections With Interactive Assumptions


COVID-19 pandemic is an enigma with uncertainty caused by multiple 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 the 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 (CCM). Our tool allows simulation of scenarios by changing the strength of lockdown, basic reproduction number(R0), asymptomatic spread, testing rate, contact rate, recovery rate, incubation period, leakage in lockdown etc. We optimized ABM and CCMs and evaluated them on multiple error metrics. Out of these models in our suite, ABM was able to capture the data better than CCMs. Our evaluation suggests that ABM models were able to capture the dynamic nature of the epidemic for a longer duration of time while CCMs performed inefficiently. We computed R0 using CCMs which were found to be decreasing with lockdown duration, indicating the effectiveness of policies in different states of India. Models have been deployed on a dashboard hosted at http://covision.tavlab.iiitd.edu.in which allows users to simulate outcomes under different parameters and will allow the policymakers to make informed decisions and efficient monitoring of the covid19 pandemic in India.