Detecting COVID-19 Clusters at High Spatiotemporal Resolution, New York City, New York, USA, June–July 2020
Sharon K. Greene, Eric Peterson, Dominique Balan, Lucretia Jones, Gretchen M. Culp, Annie D. Fine, Martin Kulldorff
Abstract
S patiotemporal analysis of high-resolution corona- virus disease data can help health offi cials monitor disease spread and target interventions Publicly available data have been used to detect COVID-19 spatiotemporal clusters at county and daily resolution levels across the United States (3; R. Amin et al., unpub. data, https://doi.org/10.1101Amin et al., unpub. data, https://doi.org/10. /2020.05.22. 20110155) .05.22. 20110155) and spatial clusters at ZIP code resolution in New York City (NYC), New York, USA (4).
Topics & Concepts
Scan statisticCoronavirus disease 2019 (COVID-19)OutreachSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakCensusGeographyMedicineDemographyCartographyVirologyEnvironmental healthOutbreakPathologyStatisticsPolitical scienceSociologyMathematicsDiseaseInfectious disease (medical specialty)PopulationLawData-Driven Disease SurveillanceCOVID-19 epidemiological studiesHuman Mobility and Location-Based Analysis