Well Well Well

Not that I trust Technology Review but a recent piece got me interested. They note:

The new survey looked for antibodies to covid-19 in the blood of 3,300 residents of Santa Clara County, which is home to Palo Alto, top venture capital firms, and the headquarters of tech giants Intel, Sun Microsystems, and Nvidia. According to the study’s authors, which include data skeptic John Ioannidis of Stanford University, actual infections in the region vastly outnumber confirmed ones by a factor of more than 50, leading them to conclude that the pathogen is killing less than 0.2% of those infected in the area. The Stanford team decribes their work as “the first large-scale community-based prevalence study in a major US county completed during a rapidly changing pandemic, and with newly available test kits.” Prevalence data like this should eventually provide a big-picture idea of how deadly the respiratory virus is. That is because the larger the number of people whose infections go unnoticed, the lower the death rate may finally prove to be.

The above referenced paper notes:

After adjusting for population and test performance characteristics, we estimate that the seroprevalence of antibodies to SARS-CoV-2 in Santa Clara County is between 2.49% and 4.16%, with uncertainty bounds ranging from 1.80% (lower uncertainty bound of the lowest estimate), up to 5.70% (upper uncertainty bound of the highest estimate). Test performance characteristics are the most critical driver of this range, with lower estimates associated with data suggesting the test has a high sensitivity for identifying SARSCoV-2, and higher estimates resulting from data suggesting over 30% of positive cases are missed by the test. These results represent the first large-scale community-based prevalence study in a major US county completed during a rapidly changing pandemic, and with newly available test kits. We consider our estimate to represent the best available current evidence, but recognize that new information, especially about the test kit performance, could result in updated estimates. For example, if new estimates indicate test specificity to be less than 97.9%, our SARS-CoV-2 prevalence estimate would change from 2.8% to less than 1%, and the lower uncertainty bound of our estimate would include zero. On the other hand, lower sensitivity, which has been raised as a concern with point-of-care test kits, would imply that the population prevalence would be even higher. New information on test kit performance and population should be  incorporated as more testing is done and we plan to revise our estimates accordingly. The most  important implication of these findings is that the number of infections is much greater than the  reported number of cases. Our data imply that, by April 1 (three days prior to the end of our survey) between 48,000 and 81,000 people had been infected in Santa Clara County. The reported number of
confirmed positive cases in the county on April 1 was 956, 50-85-fold lower than the number of infections predicted by this study.The infection to case ratio, also referred to as an under-ascertainment rate, of at least 50, is meaningfully higher than current estimates. This  ascertainment rate is a fundamental parameter of many projection and epidemiologic models, and is used as a calibration target for understanding epidemic stage and calculating fatality rates. The under-ascertainment for COVID-19 is likely a function of reliance on PCR for case identification which misses convalescent cases, early spread in the absence of systematic testing, and asymptomatic or lightly symptomatic infections that go undetected 

This is what we have been saying for a long while. The daily data I have been presenting shows at best a 0.7% prevalence. That is measured ONLY on those who shown severe symptoms, not on the population. We need large scale county by county random weekly tests. So far our relatively clueless state leaders remain with their heads in the sand.

The good news however is that the mortality rate is pari passu with a severe influenza epidemic and not the disaster we have been told it was. At what point will the facts true up with what Government does? 

Also worth reading the Nature piece.