Review of Forecasting Models for Coronavirus (COVID-19) Pandemic in India during Country-wise Lockdown

Gola, Abhinav; Arya, Ravi Kumar; Animesh, Animesh; Dugh, Ravi

COVID-19 is spreading widely across the globe right now. Majority of the countries are relying on models and studies such as stochastic simulations, AceMod model, neural networks-based models, exponential growth model,Weibull distribution model, and so on to forecast the number of COVID-19 cases in the coming months. The objective on utilizing these models is to ensure that strict measures can be enacted to contain the virus spread and also predict the resources required to deal with the pandemic as the disease spreads. In the past few months, several models were used to predict the infection rate for COVID-19. These models predicted the infection rates, recovery rate or death rates for the COVID-19 patients. All these different models took different approaches and different scenarios to predict the future rates. Now, that we know the real cases, we can check how accurate these models were. Some of these models were able to predict the near future quite close to the reality but most of them went astray. In this study, we review major forecasting models that were used in the context of India during country-wise lockdown and compare them. From these comparisons, we can see that while the advanced warning can be helpful in mitigating and preparing for an impending or ongoing epidemic, effects can be catastrophic if poorly fitting models are used for predictions.