Covid-19 – Day 146: Interpreting Covid-19 Seroprevalence Surveys


(E) Many have challenged the preliminary results from a study, COVID-19 Antibody Seroprevalence in Santa Clara County, published by Stanford University last Friday. That study indicated that the number of coronavirus infections in Santa Clara County could be between 50 and 80 times higher than the officially confirmed count.

When adjusted to take into account the Santa Clara County’s population and demographics, that study implies that between 2.49% and 4.16% of the county’s 1.93 million residents have had COVID-19!

The two critical issues with that study are first the data used for the population sample, and second the sensitivity and the specificity of the seroprevalence test.

As described in the following research paper “estimating SARS-CoV-2 seroprevalence and epidemiological parameters with uncertainty from serological surveys” plublished by a team led by Daniel Larremore from the University of Colorado Boulder, and Yonathan Grad from Harvard TH Chan School of Public Health, three sources of uncertainty complicate efforts to learn population seroprevalence from sub-sampling:

“First, tests may have imperfect sensitivity and specificity; estimates for COVID-19 tests on the market as of April 2020 reported specificity between 95% and 100% and sensitivity between 62% and 97%.

Second, the population sampled will likely not be a representative random sample, particularly in the first rounds of testing, when there is urgency to test using convenience samples and potentially limited serological testing capacity.

Third, there is uncertainty inherent to any model-based forecast which uses the empirical estimation of seroprevalence, regardless of the quality of the test, in part because of the uncertain relationship between seropositivity and immunity.”

Professor Larremore has created a Prevalence Calculator, which given test results in a population sample, using a test with known sensitivity and specificity, which calculate the posterior distribution of prevalence in the population.

Another quite interesting statistical analysis has been done by Professor Andrew Gelman from the Applied Statistics Center at Columbia University in his blog: “concerns with that Stanford study of coronavirus prevalence“.


Note: The picture above is a beautiful display of tulips at Filoli in Woodside in 2019.

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Categories: Coronavirus