ArticleInfluenza and other respiratory viruses2018
Influenza forecast optimization when using different surveillance data types and geographic scale.
Article in Influenza and other respiratory viruses, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Measuring population immunity against influenza using individual antibody titres: a multicountry, retrospective observational study.The Lancet. Infectious diseases · 2026Observational
- Predicting seasonal influenza outbreaks with regime shift-informed dynamics for improved public health preparedness.Scientific reports · 2024Article
- DiD IT?: a differences-in-differences investigation tool to quantify the impact of local incidents on public health using real-time syndromic surveillance health data.Epidemiology and infection · 2023Article
- Accurate influenza forecasts using type-specific incidence data for small geographic units.PLoS computational biology · 2021Article
- Optimized Forecasting Method for Weekly Influenza Confirmed Cases.International journal of environmental research and public health · 2020Article
- Development and validation of influenza forecasting for 64 temperate and tropical countries.PLoS computational biology · 2019Article
- Influenza forecast optimization when using different surveillance data types and geographic scale.Influenza and other respiratory viruses · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundAdvance warning of influenza incidence levels from skillful forecasts could help public health officials and healthcare providers implement more timely preparedness and intervention measures to combat outbreaks. Compared to influenza predictions generated at regional and national levels, those generated at finer scales could offer greater value in determining locally appropriate measures; however, to date, the various influenza surveillance data that are collected by state and county departments of health have not been well utilized in influenza prediction.
objectivesTo assess whether an influenza forecast model system can be optimized to generate accurate forecasts using novel surveillance data streams.
methodsHere, we generate retrospective influenza forecasts with a dynamic, compartmental model-inference system using surveillance data for influenza-like illness (ILI), laboratory-confirmed cases, and pneumonia and influenza mortality at state and county levels. We evaluate how specification of 3 system inputs-scaling, observational error variance (OEV), and filter divergence (lambda)-affects forecast accuracy.
resultsIn retrospective forecasts, and across data types, there were no clear optimal combinations for the 3 system inputs; however, scaling was most critical to forecast accuracy, whereas OEV and lambda were not.
conclusionsForecasts using new data streams should be tested to determine an appropriate scaling value using historical data and analyzed for forecast accuracy.
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