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ToolsSimms, Ian, Charlett, André, Colón-González, Felipe J., Blomquist, Paula B., Lake, Iain R., Zaidi, Asad, Shadwell, Stephanie, Sedgwick, James, Paranthaman, Karnith and Vivancos, Roberto (2025) Adapting the Flexible Farrington Algorithm for daily situational awareness and alert system to support public health decision making during the SARS-CoV-2 epidemic, England. Epidemiology and Infection, 153. ISSN 0950-2688
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Abstract
The Flexible Farrington Algorithm (FFA) is widely used to detect infectious disease outbreaks at national/regional levels on a weekly basis. The rapid spread of SARS-CoV-2 alongside the speed at which diagnostic and public health interventions were introduced made the FFA of limited use. We describe how the methodology was adapted to provide a daily alert system to support local health protection teams (HPT) working in the 316 English lower tier local authorities. To minimize the impact of a rapidly changing epidemiological situation the FFA was altered to use eight weeks of data. The adapted algorithm was based on reported positive counts using total tests as an offset. Performance was assessed using the root mean square error (RMSE) over a period. Graphical reports were sent to local teams enabling targeted public health action. From 1 July 2020 results were routinely reported. Adaptions accommodated the impact on reporting because of changes in diagnostic strategy (introduction of lateral flow devices). RMSE values were relatively small compared to observed counts, increased during periods of increased reporting, and was relatively higher in the northern and western areas of the country. The exceedance reports were well received. This presentation should be considered as a successful proof-of-concept.
| Item Type: | Article |
|---|---|
| Additional Information: | Data availability statement: The anonymized datasets used in our study are confidential records supplied to UK Health Security Agency under Regulation 3 of The Health Service (Control of Patient Information) Regulations 2020 and under Sect. 251 of the NHS Act 2006. In accordance with the UKHSAs duty of confidentiality and associated legal restrictions, the datasets analyzed during the current study are available from the corresponding author on reasonable request. |
| Uncontrolled Keywords: | covid-19,exceedance algorithm,flexible farrington,outlier,quasi-poisson regression,sars-cov-2,statistical surveillance,covid-19,flexible farrington,infectious diseases,epidemiology,sdg 3 - good health and well-being ,/dk/atira/pure/subjectarea/asjc/2700/2725 |
| Faculty \ School: | Faculty of Science > School of Environmental Sciences University of East Anglia Research Groups/Centres > Theme - ClimateUEA |
| UEA Research Groups: | Faculty of Science > Research Groups > Environmental Social Sciences University of East Anglia Schools > Faculty of Science > Tyndall Centre for Climate Change Research Faculty of Science > Research Centres > Tyndall Centre for Climate Change Research Faculty of Science > Research Centres > Centre for Ecology, Evolution and Conservation |
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| Depositing User: | LivePure Connector |
| Date Deposited: | 24 Feb 2025 12:30 |
| Last Modified: | 03 Mar 2025 09:30 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/98569 |
| DOI: | 10.1017/S0950268825000160 |
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