Stanford statistics professor Jacob Steinhardt has presented a model that attempts to estimate COVID-19 infection prevalence in countries by using data from Singapore and Taiwan — two countries with heavy testing and public case data.
[Find the GitHub here.]
Steinhardt writes about his methods and conclusions in a Medium post:
Despite the above caveats, the estimates of prevalence are reasonably consistent across sources: the estimated UK infection prevalence from different sources is 0.65%, 0.33%, and 0.60%. The US prevalence is 0.19%, 0.14%, 0.20%, or 0.08% depending on source. China is 0.03%, 0.08%, 0.01%, or 0.13%. Other sources with less data show less consistency, but this is compatible with statistical fluctuations due to the very small N that we’re dealing with here.
If we take these numbers at face value, then the Philippines has a high rate of infection relative to confirmed cases and deaths, as does Egypt, even after accounting for 5 of the 7 Egypt cases having a single source. Moving further down, the 0.04% prevalence in India, while small, is much higher than suggested by the current case count. The Philippines and India have been scaling up efforts to fight Covid-19, but it is harder to tell for Egypt, as there has been suppression of news around Covid-19 in Egypt. Testing and equipment targeted at these countries may be particularly valuable.