TY - JOUR
T1 - Using Automation, Prioritization, and Collaboration to Manage a COVID-19 Case Surge in Maricopa County, Arizona, 2020
AU - Scott, Sarah E.
AU - Mrukowicz, Christina
AU - Collins, Jennifer
AU - Jehn, Megan
AU - Charifson, Mia
AU - Hobbs, Katherine C.
AU - Zabel, Karen
AU - Chronister, Sara
AU - Howard, Brandon J.
AU - White, Jessica R.
N1 - Funding Information:
The authors received the following financial support for the research, authorship, and/or publication of this article: This work was supported by the US Treasury as part of the Coronavirus Aid, Relief, and Economic Security Act.
Publisher Copyright:
© 2022, Association of Schools and Programs of Public Health.
PY - 2022/11/1
Y1 - 2022/11/1
N2 - 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.
AB - 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.
KW - COVID-19
KW - communicable disease
KW - contact tracing
KW - disease outbreaks
KW - internet-based intervention
KW - public health practice
UR - http://www.scopus.com/inward/record.url?scp=85133521005&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85133521005&partnerID=8YFLogxK
U2 - 10.1177/00333549221100798
DO - 10.1177/00333549221100798
M3 - Article
C2 - 35786066
AN - SCOPUS:85133521005
SN - 0033-3549
VL - 137
SP - 29S-34S
JO - Public Health Reports
JF - Public Health Reports
IS - 2_suppl
ER -