Covid-19 is spreading, is AI coming to your rescue?
With the Covid-19 virus on the rise in multiple countries around the world, authorities are battling to control its spread and minimize human loss. In a globally connected world, this is an extremely complex problem.
Gathering information and understanding the spread of the disease is the first step to be able to control it. Important insights range from basic statistics such as mortality rate, to more complex correlations between e.g. a country’s health care system and the growth of infected people.
Around the world, data scientists are gathering data, extracting information and building models aimed to help experts deploy the best solutions and countermeasures in haltering the virus’ spread.
If you are an ambitious and curious data scientist, you might be interested in doing some digging yourself. Finding data is the of course the first step, and there is plenty available to find online. One option is to use the daily updated dataset provided by John Hopkins CSSE found on GitHub: https://github.com/CSSEGISandData/COVID-19. This repo is updated daily with statistics on number of infected people, deaths, and recoveries by country and region.
To get some ideas about what information can be extracted from the data, and what might be interesting, free online blog posts are a good source for inspiration, one example can be found here: https://towardsdatascience.com/9-fascinating-novel-coronavirus-statistics-and-data-visualizations-710cfa039dfd.
If you are interested in digging even deeper, more advanced methods can be used to predict the spread of the disease. One such example is described in https://towardsdatascience.com/using-kalman-filter-to-predict-corona-virus-spread-72d91b74cc8, where an adaptive Kalman filter is used for prediction of the virus spread.