  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.
