Research Article
Predictive Modeling of the Spread of COVID-19: The Case of India
@INPROCEEDINGS{10.1007/978-3-030-79276-3_11, author={Sriram Sankaran and Vamshi Sunku Mohan and Mukund Seshadrinath and Krushna Chandra Gouda and Himesh Shivappa and Krishnashree Achuthan}, title={Predictive Modeling of the Spread of COVID-19: The Case of India}, proceedings={Ubiquitous Communications and Network Computing. 4th EAI International Conference, UBICNET 2021, Virtual Event, March 2021, Proceedings}, proceedings_a={UBICNET}, year={2021}, month={7}, keywords={COVID-19 ARIMA LSTM}, doi={10.1007/978-3-030-79276-3_11} }- Sriram Sankaran
Vamshi Sunku Mohan
Mukund Seshadrinath
Krushna Chandra Gouda
Himesh Shivappa
Krishnashree Achuthan
Year: 2021
Predictive Modeling of the Spread of COVID-19: The Case of India
UBICNET
Springer
DOI: 10.1007/978-3-030-79276-3_11
Abstract
COVID-19 has been the most notorious pandemic affecting the entire world resulting in numerous deaths thus crippling the world economy. While vaccines are in the process of being developed for protection, countries are implementing measures such as social distancing to prevent the spread of the virus. Also, there exists a need for developing mathematical models to predict the rate of spread of COVID-19 and quantify its impact on countries such as India. Towards this goal, we developed a realistic COVID-19 dataset consisting of state-wide distribution of number of cases in India from March-July 2020. Further, we conduct exploratory data analysis on the dataset to understand the states and their corresponding growth rates. This enables us to cluster states with exponential and non-exponential growth rates as well as assess the effectiveness of lockdown imposed to curb the spread of virus. Finally, we develop predictive models using Auto-Regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Networks (LSTM) on time-series data for top-10 affected states in India to predict the rate of spread and validate their accuracy. Finally, our models can be used to guide the development of mechanisms for optimal resource allocation of healthcare systems and response.
