Mathematical modelling has been used extensively to predict COVID-19 cases across different countries. This research provides a micro perspective in understanding the progression of case fatality ratio in 20 districts of Karnataka State of India. The case fatality ratio is an indicator to suggest if the pandemic is seeing a declining trend and the focus of this study is assessing the progression of this metric. Panel data with 7060 observations, consisting of daily infected, recovered and deaths are considered from March 2020 to March 2021. Time series models of state space exponential smoothing (ETS) and Auto Regressive Integrated Moving Average (ARIMA) are applied and compared to forecast the CFR district-wise. ARIMA model performs better than the ETS model. Further, the model is validated using residual analysis. To get an overall perspective of CFR progression across the state, daily average CFR for Karnataka state is evaluated;models of ETS, ARIMA and Long Short-Term Memory (LSTM) are applied and compared. LSTM which handles long term dependencies, and ARIMA provide reliable forecasts. This study provides useful insights to the regulatory bodies to ensure strategic allocation of healthcare resources in required regions. © 2021 IEEE.
Modelling the Case Fatality Ratio of COVID-19