Imperfect Tests and The Effects of False Positives

The US government has been widely criticized for its failure to test as many of its citizens for COVID-19 infections as other countries. But is mass testing really as easy as it seems? This analysis of the false positive and false negative rates of tests, using published sensitivities and specificities for COVID-19 rt-PCR and antigen tests, shows that even tests with slightly less than perfect results can produce very large numbers of false positives.

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Modeling an Infection with Changing R for San Diego County

Eric Busboom.

The SIR infection model is a simple way of exploring the effects of a social distance policy on a large population such as San Diego County

`` Full notebook on github

The SIR infection model is a simple set of differential equations that describe how a disease can spread under very homogeneous, stable conditions. The model gets it’s name from the three “compartments” for people in diferent conditions:

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