Using Automation, Prioritization, and Collaboration to Manage a COVID-19 Case Surge in Maricopa County, Arizona, 2020. (November 2022)

• Record Type: Journal Article
• Authors: Scott, Sarah E.; Mrukowicz, Christina; Collins, Jennifer; Jehn, Megan; Charifson, Mia; Hobbs, Katherine C.; Zabel, Karen; Chronister, Sara; Howard, Brandon J.; White, Jessica R.
• Other Names: Haddad Maryam B. guest-editor.; McLean Jody E. guest-editor.; Feldman Sue S. guest-editor.; Sizemore Erin E. guest-editor.; Taylor Melanie M. guest-editor.
• Is Part Of: Public health reports. Volume 137:Number 2(2022)Supplement
• Page Start: 29S
• Page End: 34S
• Publication Date: 2022-11
• Subjects: COVID-19 -- communicable disease -- contact tracing -- public health practice -- disease outbreaks -- internet-based intervention; Public health -- United States -- Periodicals; 614.0973
• DOI: 10.1177/00333549221100798
• Languages: English
• ISSNs: 0033-3549

Abstract

During summer 2020, the Maricopa County Department of Public Health (MCDPH) responded to a surge in COVID-19 cases. We used internet-based platforms to automate case notifications, prioritized investigation of cases more likely to have onward transmission or severe COVID-19 based on available preinvestigation information, and partnered with Arizona State University (ASU) to scale investigation capacity. We assessed the speed of automated case notifications and accuracy of our investigation prioritization criteria. Timeliness of case notification—the median time between receipt of a case report at MCDPH and first case contact—improved from 11 days to <1 day after implementation of automated case notification. We calculated the sensitivity and positive predictive value (PPV) of the investigation prioritization system by applying our high-risk prioritization criteria separately to data available pre- and postinvestigation to determine whether a case met these criteria preinvestigation, postinvestigation, or both. We calculated the sensitivity as the percentage of cases classified postinvestigation as high risk that had also been classified as high risk preinvestigation. We calculated PPV as the percentage of all cases deemed high risk preinvestigation that remained so postinvestigation. During June 30 to July 31, 2020, a total of 55 056 COVID-19 cases with an associated telephone number (94% of 58 570 total cases) were reported. Preinvestigation, 8799 (16%) cases met high-risk criteria. Postinvestigation, 17 037 (31%) cases met high-risk criteria. Sensitivity was 52% and PPV was 98%. Automating case notifications, prioritizing investigations, and collaborating with ASU improved the timeliness of case contact, focused public health resources toward high-priority cases, and increased investigation capacity. Establishing partnerships between health departments and academia might be a helpful strategy for future surge capacity planning.