Data scientists use digital tools, machine learning to predict number of patients to contract coronavirus

Data scientists and epidemiologists are working together to develop a tool that will forecast the number of patients who will contract the novel coronavirus using big data, machine learning and other digital tools, according to The Wall Street Journal.

The end goal is to provide front-line healthcare workers with real-time information about the spread of COVID-19.

Outbreak analytics, the approach these experts are taking, gathers all available data on an epidemic, including confirmed cases, fatalities, test results, tracing contacts of infected people, maps of population densities and demographics, traveler flows and migration, availability of healthcare services, drug stockpiles and more.

This data is then processed by machine learning software to recognize patterns. Then algorithmic models can predict the number of new cases that are likely to arise.

“Besides advancements in medicine, advancements in information technology and digital data are how we defeat this pandemic and prevent another Spanish flu-like outcome,” Brian Hopkins, vice president and principal analyst at Forrester Research, told WSJ.

More articles on AI:
White House, Microsoft, Chan Zuckerberg seek AI experts to develop tools for coronavirus research dataset
Qventus AI predicts major capacity shortfalls in ICU beds due to coronavirus
Illinois ACO deploys AI to identify high risk COVID-19 patients

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