ArticleEmerging infectious diseases2018
Evaluation of Nowcasting for Detecting and Predicting Local Influenza Epidemics, Sweden, 2009-2014.
Article in Emerging infectious diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Identification and evaluation of epidemic prediction and forecasting reporting guidelines: A systematic review and a call for action.Epidemics · 2020Pooled it
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- Nowcasting (Short-Term Forecasting) of COVID-19 Hospitalizations Using Syndromic Healthcare Data, Sweden, 2020.Emerging infectious diseases · 2022Article
- A data-driven approach to measuring epidemiological susceptibility risk around the world.Scientific reports · 2021Article
- Nowcasting (Short-Term Forecasting) of Influenza Epidemics in Local Settings, Sweden, 2008-2019.Emerging infectious diseases · 2020Article
- Developing a Heart Transplantation Self-Management Support Mobile Health App in Taiwan: Qualitative Study.JMIR mHealth and uHealth · 2020Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The growing availability of big data in healthcare and public health opens possibilities for infectious disease control in local settings. We prospectively evaluated a method for integrated local detection and prediction (nowcasting) of influenza epidemics over 5 years, using the total population in Östergötland County, Sweden. We used routine health information system data on influenza-diagnosis cases and syndromic telenursing data for July 2009-June 2014 to evaluate epidemic detection, peak-timing prediction, and peak-intensity prediction. Detection performance was satisfactory throughout the period, except for the 2011-12 influenza A(H3N2) season, which followed a season with influenza B and pandemic influenza A(H1N1)pdm09 virus activity. Peak-timing prediction performance was satisfactory for the 4 influenza seasons but not the pandemic. Peak-intensity levels were correctly categorized for the pandemic and 2 of 4 influenza seasons. We recommend using versions of this method modified with regard to local use context for further evaluations using standard methods.
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