In recent years, machine learning has found applications in new and often unexpected areas. With the novel coronavirus outbreak in 2019 and 2020, it makes sense that many have tried to apply machine learning and artificial intelligence to various problems relating to the disease. From modeling the spread of the disease to searching for possible drugs and vaccines, machine learning has been integral to understanding many of the problems caused by the COVID-19 pandemic.
Case Study: Disease Dynamics
A simple internet search will lead you to hundreds of dashboards showing the current number of coronavirus cases around the world. This stems from how easy it is to access data relating to the virus, especially from reputable sources like Kaggle or Johns Hopkins. This data, along with sophisticated models for disease dynamics has, for example, enabled predictive modeling for the number of people who actually have the virus and the risk of hosting an event in any county in the US.
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