Example of stochastic thinking applied to testing to better estimate infection levels. [Tests are pooled](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3500568/) in larger groups so that aggregate positives can be determined at a higher throughput. If a test comes back positive, then the group members are tested individually to identify positives. This divide & conquer strategy is only efficient for populations at low infection levels though.

What's disturbing about the Italy example above is that if the number of confirmed cases limits to 130,000, then the rest of Italy's population of 60 million is really unknown. So aggregate testing can more quickly estimate how many more people are infected w/o symptoms or have anti-bodies (via a different test).

What's disturbing about the Italy example above is that if the number of confirmed cases limits to 130,000, then the rest of Italy's population of 60 million is really unknown. So aggregate testing can more quickly estimate how many more people are infected w/o symptoms or have anti-bodies (via a different test).