171,909 Cases; 6,351 Deaths; 77,783 Recoveries.
These are some metrics as on March 16, 2020, of Coronavirus Outbreak that began its rampage on the last day of 2019 and brought the world into a frenzy in less than three months.
In the 2020s, to better combat this global health emergency we can understand the spread of COVID-19 with the arsenal of new-age data-driven techniques.
From supply chains to consumption patterns, the virus has affected everybody down to the lowest common denominator. Machine learning and data engineering can be leveraged to analyze news reports and social posts, information coordination can be made better, and predictions can be made.
It is critical to determine where the virus would surface in order to block its spread effectively. We are trying to understand how is the virus interacting with the population at large.
~BlueDot, a company running AI and data-driven surveillance for COVID-19
How can AI, data science and machine learning be instrumental?
Is the virus more prevalent in certain areas than in others, and why? Is the spread only correlated to the primary sources (directly coming from infected countries) and secondary cases (people primary sources are coming in contact with) or there is more to the story?
What are the trends …
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