Year: 2020
Type: preprint
Study design: Observational Study
Abstract: Background Current prevalence of COVID-19 drives many policy decisions, but is hampered by ambiguities in testing and reporting. We propose an alternative method for estimating community prevalence that is inexpensive and timely. We test the Hypothesis that the survey sampling provides a quantitative prevalence that is similar to widespread genomic or serological testing. Methods We have built a simple, web-based survey of signs and symptoms for COVID-19 based on six questions. No personally identifiable information is collected to maintain privacy. Sampling can be directed to a population of interest such as a company, or broadcast widely to get geographic sampling. Data reporting can be real-time and plotted onto zipcode maps. Rates of prevalence were calculated from presumed COVID cases and respondents, with confidence intervals based on the Blaker method. Results The website was created quickly, and survey results were quantitatively useful after only a few days. Analyzing 3161 cases from CountCOVID.org, we found a community prevalence of 7% in Georgia that was much greater than the reported confirmed cases. Our prevalence estimate of 21% in New York City was similar to the reported 19.6% by surveillance antibody serotesting. Our estimates are validated by five other community surveillance studies using genomic or antibody testing. Conclusions Prevalence and incidence of COVID-19 symptoms in the community can be estimated by a crowd-sourced website at considerably less expense than widespread testing.
Authors: David N Ku, Ben Ku, Traci Leong, Zixiang Liu
Institutions: Georgia Institute of Technology, Emory University
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4248043163