Forecasting the 2017/2018 seasonal influenza epidemic in England using multiple dynamic transmission models: a case study.
Australia
/ epidemiology
Biometry
Critical Care
England
Epidemics
Family Practice
Forecasting
General Practice
Hospitalization
Humans
Influenza A Virus, H1N1 Subtype
Influenza, Human
/ epidemiology
Intensive Care Units
Models, Biological
Pandemics
Primary Health Care
Public Health
/ methods
Referral and Consultation
Seasons
Forecasting
GP consultations
Intensive care admissions
Nowcasting
Seasonal influenza
Transmission models
Journal
BMC public health
ISSN: 1471-2458
Titre abrégé: BMC Public Health
Pays: England
ID NLM: 100968562
Informations de publication
Date de publication:
15 Apr 2020
15 Apr 2020
Historique:
received:
03
10
2019
accepted:
04
03
2020
entrez:
16
4
2020
pubmed:
16
4
2020
medline:
15
9
2020
Statut:
epublish
Résumé
Since the 2009 A/H1N1 pandemic, Public Health England have developed a suite of real-time statistical models utilising enhanced pandemic surveillance data to nowcast and forecast a future pandemic. Their ability to track seasonal influenza and predict heightened winter healthcare burden in the light of high activity in Australia in 2017 was untested. Four transmission models were used in forecasting the 2017/2018 seasonal influenza epidemic in England: a stratified primary care model using daily, region-specific, counts and virological swab positivity of influenza-like illness consultations in general practice (GP); a strain-specific (SS) model using weekly, national GP ILI and virological data; an intensive care model (ICU) using reports of ICU influenza admissions; and a synthesis model that included all data sources. For the first 12 weeks of 2018, each model was applied to the latest data to provide estimates of epidemic parameters and short-term influenza forecasts. The added value of pre-season population susceptibility data was explored. The combined results provided valuable nowcasts of the state of the epidemic. Short-term predictions of burden on primary and secondary health services were initially highly variable before reaching consensus beyond the observed peaks in activity between weeks 3-4 of 2018. Estimates for R This exercise successfully applied a range of pandemic models to seasonal influenza. Forecasting early in the season remains challenging but represents a crucially important activity to inform planning. Improved knowledge of pre-existing levels of immunity would be valuable.
Sections du résumé
BACKGROUND
BACKGROUND
Since the 2009 A/H1N1 pandemic, Public Health England have developed a suite of real-time statistical models utilising enhanced pandemic surveillance data to nowcast and forecast a future pandemic. Their ability to track seasonal influenza and predict heightened winter healthcare burden in the light of high activity in Australia in 2017 was untested.
METHODS
METHODS
Four transmission models were used in forecasting the 2017/2018 seasonal influenza epidemic in England: a stratified primary care model using daily, region-specific, counts and virological swab positivity of influenza-like illness consultations in general practice (GP); a strain-specific (SS) model using weekly, national GP ILI and virological data; an intensive care model (ICU) using reports of ICU influenza admissions; and a synthesis model that included all data sources. For the first 12 weeks of 2018, each model was applied to the latest data to provide estimates of epidemic parameters and short-term influenza forecasts. The added value of pre-season population susceptibility data was explored.
RESULTS
RESULTS
The combined results provided valuable nowcasts of the state of the epidemic. Short-term predictions of burden on primary and secondary health services were initially highly variable before reaching consensus beyond the observed peaks in activity between weeks 3-4 of 2018. Estimates for R
CONCLUSIONS
CONCLUSIONS
This exercise successfully applied a range of pandemic models to seasonal influenza. Forecasting early in the season remains challenging but represents a crucially important activity to inform planning. Improved knowledge of pre-existing levels of immunity would be valuable.
Identifiants
pubmed: 32293372
doi: 10.1186/s12889-020-8455-9
pii: 10.1186/s12889-020-8455-9
pmc: PMC7158152
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
486Subventions
Organisme : Medical Research Council
ID : MC_UU_00002/11
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/R015600/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC UU 00002/11
Pays : United Kingdom
Organisme : National Institute for Health Research
ID : Health Protection Research Units in Respiratory Infections
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