Predictive Analysis of COVID-19 Time-series Data from Johns Hopkins University
This work addresses forecasting COVID-19 spread for public health planning, but it appears incremental as it applies existing methods to new data without claiming major breakthroughs.
The study tackled predicting COVID-19 infection numbers for different countries over the next 14 days using time-series data from Johns Hopkins University, achieving results through methods like polynomial regression and extreme learning machines with a sliding window approach, but no concrete numbers were provided in the abstract.
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.