Serum samples from 119 individuals obtained prior to the emergence of SARS-CoV-2 were used as negative controls. Neighborhood Groups Map, https://data.sfgov.org/Geographic-Locations-and-Boundaries/Planning-Neighborhood-Groups-Map/iacs-ws63, 2019). Cumulative incidence by planning neighborhood from March – June 2020 in Quarfloxin (CX-3543) Fig.?4c used publicly available data from the San Francisco department of Public Health FOXO4 (https://data.sfgov.org/COVID-19/COVID-19-Cases-by-Geography-and-Date/d2ef-idww). Figures?3 and ?and44 visualize Supplementary Tables?2 and 3. Physique?2 visualizes the distribution of samples, although because the underlying raw data for Fig.?2 are at the individual level, they have not been shared with the manuscript for ethical reasons, although the summarized demographic distributions of the samples are included in the manuscript (Table?1) and access to full raw data can be requested from the authors by contacting Bryan Greenhouse. Data for poverty rates shown in Fig.?2c come from the American Community Survey 2019 (https://data.census.gov/cedsci/). The code used for this analysis can be found on Github at https://github.com/EPPIcenter/scale-it/ (DOI:10.5281/zenodo.4695335)26. Abstract Serosurveillance provides a unique opportunity to quantify the proportion of the population that has been exposed to pathogens. Here, we developed and piloted Serosurveillance for Continuous, ActionabLe Epidemiologic Intelligence of Transmission (SCALE-IT), a platform through which we systematically tested remnant samples from routine blood draws in two major hospital networks in San Francisco for SARS-CoV-2 antibodies during the early months of the pandemic. Importantly, SCALE-IT allows for algorithmic sample selection and rich data on covariates by leveraging electronic health record data. We estimated overall seroprevalence at 4.2%, corresponding to a case ascertainment rate of only 4.9%, and identified important heterogeneities by neighborhood, homelessness status, and race/ethnicity. Neighborhood seroprevalence estimates from SCALE-IT were comparable to local community-based surveys, while providing results encompassing the entire city Quarfloxin (CX-3543) that have been previously unavailable. Leveraging this hybrid serosurveillance approach has strong potential for application beyond this local context and for diseases other than SARS-CoV-2. (LIINC) study (https://www.liincstudy.org/) and used as positive controls. Importantly, participants in this cohort represent a range of contamination severities (ranging from asymptomatic Quarfloxin (CX-3543) to severe), age, sex, and Quarfloxin (CX-3543) ethnicity and race. Serum samples from 119 individuals obtained prior to the emergence of SARS-CoV-2 were used as unfavorable controls. The overall sensitivity of our serial testing approach using positive and negative controls was 93.7% (95% CrI?=?89.0%, 97.2%) and specificity was 99.6% (95% CrI?=?98.2%, 100.0%) (Supplementary Tables?1, 5 and 6, Supplementary Methods?1). Analytic methods Raw seropositivity was decided as the proportion of all samples from unique individuals that tested positive around the confirmatory assay. We then produced estimates of seroprevalence adjusted for the sensitivity and specificity of the serial testing approach, incorporating potential conditional dependence of the assessments as described in Gardner et al.28 (see Supplementary Methods?1). We stratified by covariates to obtain seroprevalence estimates for each stratum (age, sex, insurance status, ethnicity, and neighborhood). To identify neighborhoods, we geocoded sample addresses using the Google Cloud Geocoding API using the R package29. Samples (thanks Gabriel Chodick, Oliver Laeyendecker, and the other, anonymous reviewer(s) for their contribution to the peer review of this work. Peer review reports are available. Publishers note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. These authors contributed equally: Isobel Routledge, Adrienne Epstein, Saki Takahashi. Supplementary information The online version contains supplementary material available at 10.1038/s41467-021-23651-6..