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step 1: we check twitter

FIrsts things firts, we need our content. SO we start by getting a list of all of the most recent tweets by influential people. Heres an example

step 2: we analyze the tweet

in step 2 we use googles natural language processing on the tweet. We analyze the tweet for locations, inflammatory language, suggestions of violence, or indication of fake news.

Heres an example: Based on this tweet, we can see that there's going to be a really in Louisiana. We've found a super spreader event!

step 3: we do some more analysis

in step 2 we figured out there was going to be a Rally in Louisiana. We checked google maps API and identified open areas in Louisiana capital, baton rouge. We flagged these as areas to avoid. After that we searched for restaurants and grocery stores near open areas and flagged those as well.

step 4: we continuously check our work

Now that we know where to look, we can monitor news streams, live videos, and google search spikes. to flag new Corvid super spreader events. By using the confluence event streaming platform we can intake and process massive amounts of information in small amounts of time, in order to produce accurate real time analysis.

step 5: we get smarter

Hopefully we've helped you avoid a super spreader event. But if we haven't then we want to know. By collecting user feedback, we can use the tensor flow JavaScript library to improve our predictions.