Covid-19 Spread Analysis

Abstract

Based on the public datasets afforded by John Hopkins University and Canadian health authorities, we developed a forecasting model of Covid-19 after analyzing the spread. Data related to the cumulative amount of definite cases, per day, in each country and another dataset consisting of various life factors, scored by the people living in each country around the globe. We are going to merge these two datasets to see if there is any relationship between the spread of the virus in a country by preprocessing, merging and finding correlation between datasets we will calculate needed measures and prepare them for an analysis, then we will try to predict the spread of cases by using various methods. Time series data tracking the number of people affected by the coronavirus globally, including confirmed cases of the coronavirus, the number of people who have died due to the coronavirus and the number of people who have recovered from the deadly infection. Data science can give accurate pictures of coronavirus outcomes and also helps in tracking the spread. Secondly using Covid-19 data, we can make supply chain logistics decisions in spreadsheets supplies of personal protective equipment and ventilators to hospitals and clinics across the world. An analysis of the country, by state and region, identifying locations of highest need for supplies and ventilators according to the dataset collected. This is called a supply plan. Finally, create a set of visualizations and then add these visualizations to a presentation so that we can report on findings.