Ayaz Hyder1,2, Anne Trinh1, Pranav Padmanabhan1, John Marschhausen3,4, Alexander Wu5, Alexander Evans6, Radhika Iyer6, Alexandria Jones6. 1. 2647 College of Public Health, The Ohio State University, Columbus, OH, USA. 2. Translational Data Analytics Institute, The Ohio State University, Columbus, OH, USA. 3. College of Education and Human Ecology, The Ohio State University, Columbus, OH, USA. 4. 128722 Hilliard City School District, OH, USA. 5. Resolve to Save Lives, New York, NY, USA. 6. 443323 Franklin County Public Health, Columbus, OH, USA.
Abstract
OBJECTIVE: Data-informed decision making is valued among school districts, but challenges remain for local health departments to provide data, especially during a pandemic. We describe the rapid planning and deployment of a school-based COVID-19 surveillance system in a metropolitan US county. METHODS: In 2020, we used several data sources to construct disease- and school-based indicators for COVID-19 surveillance in Franklin County, an urban county in central Ohio. We collected, processed, analyzed, and visualized data in the COVID-19 Analytics and Targeted Surveillance System for Schools (CATS). CATS included web-based applications (public and secure versions), automated alerts, and weekly reports for the general public and decision makers, including school administrators, school boards, and local health departments. RESULTS: We deployed a pilot version of CATS in less than 2 months (August-September 2020) and added 21 school districts in central Ohio (15 in Franklin County and 6 outside the county) into CATS during the subsequent months. Public-facing web-based applications provided parents and students with local information for data-informed decision making. We created an algorithm to enable local health departments to precisely identify school districts and school buildings at high risk of an outbreak and active SARS-CoV-2 transmission in school settings. PRACTICE IMPLICATIONS: Piloting a surveillance system with diverse school districts helps scale up to other districts. Leveraging past relationships and identifying emerging partner needs were critical to rapid and sustainable collaboration. Valuing diverse skill sets is key to rapid deployment of proactive and innovative public health practices during a global pandemic.
OBJECTIVE: Data-informed decision making is valued among school districts, but challenges remain for local health departments to provide data, especially during a pandemic. We describe the rapid planning and deployment of a school-based COVID-19 surveillance system in a metropolitan US county. METHODS: In 2020, we used several data sources to construct disease- and school-based indicators for COVID-19 surveillance in Franklin County, an urban county in central Ohio. We collected, processed, analyzed, and visualized data in the COVID-19 Analytics and Targeted Surveillance System for Schools (CATS). CATS included web-based applications (public and secure versions), automated alerts, and weekly reports for the general public and decision makers, including school administrators, school boards, and local health departments. RESULTS: We deployed a pilot version of CATS in less than 2 months (August-September 2020) and added 21 school districts in central Ohio (15 in Franklin County and 6 outside the county) into CATS during the subsequent months. Public-facing web-based applications provided parents and students with local information for data-informed decision making. We created an algorithm to enable local health departments to precisely identify school districts and school buildings at high risk of an outbreak and active SARS-CoV-2 transmission in school settings. PRACTICE IMPLICATIONS: Piloting a surveillance system with diverse school districts helps scale up to other districts. Leveraging past relationships and identifying emerging partner needs were critical to rapid and sustainable collaboration. Valuing diverse skill sets is key to rapid deployment of proactive and innovative public health practices during a global pandemic.
Entities:
Keywords:
COVID-19; decision making; public health; schools; surveillance
Authors: Calistus N Ngonghala; Enahoro Iboi; Steffen Eikenberry; Matthew Scotch; Chandini Raina MacIntyre; Matthew H Bonds; Abba B Gumel Journal: Math Biosci Date: 2020-05-01 Impact factor: 2.144
Authors: Caitlin B Biddell; Karl T Johnson; Mehul D Patel; Raymond L Smith; Hillary K Hecht; Julie L Swann; Maria E Mayorga; Kristen Hassmiller Lich Journal: Front Public Health Date: 2022-08-16