Computational Data Analysis, Prediction and Forecast of Health Disaster: A Machine Learning Approach

Objective of this study is to analyse the effectiveness of social distancing in reducing the risk of COVID-19, taking Spain as an example. Also, to study the influence of advanced enforcement of social distancing in Spain against the actual date of 14th March 2020. Computational models were developed (with Python programming) using data collected by web crawling using RCrawl using R-programming. The model predicted total cases and total deaths under the given conditions of advanced social distancing in 6 previous dates. Also, this model made a forecast of total cases and total deaths for the month of May. Results suggested that by 15th May 2020, the total cases may reach 337672. If the lockdown was imposed 6 days earlier the cases would be a mere 43083 only. Also, the total deaths will be reduced 82% effectively if the lockdown was imposed 6 days earlier. It can be concluded from the results, that enforcement of social distancing at the earlier stage of any pandemic disease spread is the only effective way to reduce the risk and contain the disaster.