We provide a predictive analysis of the spread of COVID-19, also known as SARS-CoV-2, using the dataset made publicly available online by the Johns Hopkins University. Our main objective is to provide predictions of the number of infected people for different countries in the next 14 days. The predictive analysis is done using time-series data transformed on a logarithmic scale. We use two well-known methods for prediction: polynomial regression and neural network. As the number of training data for each country is limited, we use a single-layer neural network called the extreme learning machine (ELM) to avoid over-fitting. Due to the non-stationary nature of the time-series, a sliding window approach is used to provide a more accurate prediction.
@article{arxiv.2005.05060,
title = {Predictive Analysis of COVID-19 Time-series Data from Johns Hopkins University},
author = {Alireza M. Javid and Xinyue Liang and Arun Venkitaraman and Saikat Chatterjee},
journal= {arXiv preprint arXiv:2005.05060},
year = {2020}
}