title: Influenza epidemic surveillance and prediction based on electronic health record data from an out-of-hours general practitioner cooperative: model development and validation on 2003-2015 data.
abstract: Annual influenza epidemics significantly burden health care. Anticipating them allows for timely preparation. The Scientific Institute of Public Health in Belgium (WIV-ISP) monitors the incidence of influenza and influenza-like illnesses (ILIs) and reports on a weekly basis. General practitioners working in out-of-hour cooperatives (OOH GPCs) register diagnoses of ILIs in an instantly accessible electronic health record (EHR) system. This article has two objectives: to explore the possibility of modelling seasonal influenza epidemics using EHR ILI data from the OOH GPC Deurne-Borgerhout, Belgium, and to attempt to develop a model accurately predicting new epidemics to complement the national influenza surveillance by WIV-ISP.
  
  
  
  Validity of the OOH GPC data was assessed by comparing OOH GPC ILI data with WIV-ISP ILI data for the period 2003-2012 and using Pearson's correlation. The best fitting prediction model based on OOH GPC data was developed on 2003-2012 data and validated on 2012-2015 data. A comparison of this model with other well-established surveillance methods was performed. A 1-week and one-season ahead prediction was formulated.
  
  
  
  In the OOH GPC, 72,792 contacts were recorded from 2003 to 2012 and 31,844 from 2012 to 2015. The mean ILI diagnosis/week was 4.77 (IQR 3.00) and 3.44 (IQR 3.00) for the two periods respectively. Correlation between OOHs and WIV-ISP ILI incidence is high ranging from 0.83 up to 0.97. Adding a secular trend (5 year cycle) and using a first-order autoregressive modelling for the epidemic component together with the use of Poisson likelihood produced the best prediction results. The selected model had the best 1-week ahead prediction performance compared to existing surveillance methods. The prediction of the starting week was less accurate (±3 weeks) than the predicted duration of the next season.
  
  
  
  OOH GPC data can be used to predict influenza epidemics both accurately and fast 1-week and one-season ahead. It can also be used to complement the national influenza surveillance to anticipate optimal preparation.
authors.affiliation: Department of Primary and Interdisciplinary Care Antwerp (ELIZA) - Centre for General Practice, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium. barbara.michiels@uantwerpen.be.
authors.forename: Barbara
authors.initials: B
authors.lastname: Michiels
authors.affiliation: Department of Epidemiology, Faculty of Public Health, Ho Chi Minh University of Medicine and Pharmacy, Ho Chi Minh, Vietnam.|Systems Medicine of Infectious Diseases (SMID), Department of Systems Immunology, Helmholtz Centre for Infection Research, Braunschweig, Germany.
authors.forename: Van Kinh
authors.initials: VK
authors.lastname: Nguyen
authors.affiliation: Department of Primary and Interdisciplinary Care Antwerp (ELIZA) - Centre for General Practice, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.|Vaccine & Infectious Disease Institute (VAXINFECTIO) - Laboratory of Medical Microbiology, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.|Epidemiology and Social Medicine (ESOC), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.
authors.forename: Samuel
authors.initials: S
authors.lastname: Coenen
authors.affiliation: Department of Primary and Interdisciplinary Care Antwerp (ELIZA) - Centre for General Practice, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.
authors.forename: Philippe
authors.initials: P
authors.lastname: Ryckebosch
authors.affiliation: Unit Epidemiology of infectious diseases - Operational Directorate Public Health and Surveillance, Belgian Scientific Institute for Public Health, Brussels, Belgium.
authors.forename: Nathalie
authors.initials: N
authors.lastname: Bossuyt
authors.affiliation: Epidemiology and Social Medicine (ESOC), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.|Interuniversity Institute of Biostatistics and statistical Bioinformatics (iBIOSTAT), Hasselt University, Hasselt, Belgium.|Vaccine & Infectious Disease Institute (VAXINFECTIO) - Centre for Health Economic Research and Modelling Infectious Diseases, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.
authors.forename: Niel
authors.initials: N
authors.lastname: Hens
mesh_terms: D000328:Adult; D039602:After-Hours Care; D001530:Belgium / Q000453:epidemiology; D003625:Data Collection; D057286:Electronic Health Records; D058872:Epidemics; D062665:Epidemiological Monitoring; D005260:Female; D058005:General Practitioners; D006801:Humans; D015994:Incidence; D007251:Influenza, Human / Q000453:epidemiology*; D008297:Male; D008962:Models, Theoretical; D012189:Retrospective Studies; D012621:Seasons
keywords: Epidemics; Epidemiology; Influenza; Influenza-like illness; Out-of-hours; Prediction; Secular; Surveillance