COVID-19 Surveillance for Local Decision Making
| Autor: | Alexander Wu, Alexandria Jones, Pranav Padmanabhan, Anne Trinh, John Marschhausen, Ayaz Hyder, Radhika Iyer, Alexander Evans |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Public Health Methodology Coronavirus disease 2019 (COVID-19) education Pilot Projects Plan (drawing) School district 03 medical and health sciences 0302 clinical medicine Public health surveillance Pandemic medicine Humans Public Health Surveillance 030212 general & internal medicine Intersectoral Collaboration Ohio Schools 030505 public health Data collection business.industry Data Collection Public health Public Health Environmental and Occupational Health COVID-19 Public relations Socioeconomic Factors 0305 other medical science business |
| Zdroj: | Public Health Rep |
| ISSN: | 1468-2877 0033-3549 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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