Several days ago I caught the tail end of a television interview with a doctor who was being asked if there is a way to estimate the number of people in the U.S. who have contracted Covid-19 given that only 0.6% of the total population has been tested (at the time of this publication that number is around 1%). The doctor being interviewed didn’t really have an answer to this question, but the answer is yes; it is possible to say something about the actual number even if we can’t say what that number is. Providing this answer isn’t quite within the range of what most medical doctors would normally be expected to do, but as a data scientist, answering this question is the sort of things that does fall within my wheel-house. Today I’ll be going over two approaches I take to provide some hopefully useful insight on this question, one approach that provides a broad answer that reflects the uncertainty of how much we don’t know without widespread testing, and then another that may help provide some direction for where to narrow down a more specific answer to.

To provide some context, the discussion at large within this interview was regarding the potential risks involved in lifting the shelter in place policies currently in place across the country to get the economy back on more stable footing. The heart of the question was really this; has enough of the population been exposed to the virus, and thus developed immunities to it, that any second wave of outbreaks would be significantly slowed by the amount of people that are now already effectively immune through exposure? While I don’t have the medical knowledge to say how much of the population would have to develop immunities in order to significantly slow a viral spread, I can say something about how far the spread has gone, even if only to give a window of likely infection counts.

The tricky part about this question is that only a proportion of those who have Continue reading →