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

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.

Applications of GPS-tracked personal and fixed-location PM(2.5) continuous exposure monitoring.

UNLABELLED: Continued development of personal air pollution monitors is rapidly improving government and research capabilities for data collection. In this study, we tested the feasibility of using GPS-enabled personal exposure monitors to collect personal exposure readings and short-term daily PM2.5 measures at 15 fixed locations throughout a community. The goals were to determine the accuracy of fixed-location monitoring for approximating individual exposures compared to a centralized outdoor air pollution monitor, and to test the utility of two different personal monitors, the RTI MicroPEM V3.2 and TSI SidePak AM510. For personal samples, 24-hr mean PM2.5 concentrations were 6.93 µg/m³ (stderr = 0.15) and 8.47 µg/m³ (stderr = 0.10) for the MicroPEM and SidePak, respectively. Based on time-activity patterns from participant journals, exposures were highest while participants were outdoors (MicroPEM = 7.61 µg/m³, stderr = 1.08, SidePak = 11.85 µg/m³, stderr = 0.83) or in restaurants (MicroPEM = 7.48 µg/m³, stderr = 0.39, SidePak = 24.93 µg/m³, stderr = 0.82), and lowest when participants were exercising indoors (MicroPEM = 4.78 µg/m³, stderr = 0.23, SidePak = 5.63 µg/m³, stderr = 0.08). Mean PM(2.5) at the 15 fixed locations, as measured by the SidePak, ranged from 4.71 µg/m³ (stderr = 0.23) to 12.38 µg/m³ (stderr = 0.45). By comparison, mean 24-h PM(2.5) measured at the centralized outdoor monitor ranged from 2.7 to 6.7 µg/m³ during the study period. The range of average PM(2.5) exposure levels estimated for each participant using the interpolated fixed-location data was 2.83 to 19.26 µg/m³ (mean = 8.3, stderr = 1.4). These estimated levels were compared with average exposure from personal samples. The fixed-location monitoring strategy was useful in identifying high air pollution microclimates throughout the county. For 7 of 10 subjects, the fixed-location monitoring strategy more closely approximated individuals' 24-hr breathing zone exposures than did the centralized outdoor monitor. Highlights are: Individual PM(2.5) exposure levels vary extensively by activity, location and time of day; fixed-location sampling more closely approximated individual exposures than a centralized outdoor monitor; and small, personal exposure monitors provide added utility for individuals, researchers, and public health professionals seeking to more accurately identify air pollution microclimates. IMPLICATIONS: Personal air pollution monitoring technology is advancing rapidly. Currently, personal monitors are primarily used in research settings, but could they also support government networks of centralized outdoor monitors? In this study, we found differences in performance and practicality for two personal monitors in different monitoring scenarios. We also found that personal monitors used to collect outdoor area samples were effective at finding pollution microclimates, and more closely approximated actual individual exposure than a central monitor. Though more research is needed, there is strong potential that personal exposure monitors can improve existing monitoring networks.