Evidence map›Paper›PMID 32293372›Full record

ArticleBMC public health2020

Forecasting the 2017/2018 seasonal influenza epidemic in England using multiple dynamic transmission models: a case study.

Paul J Birrell, Xu-Sheng Zhang, Alice Corbella, Edwin van Leeuwen, Nikolaos Panagiotopoulos, Katja Hoschler, Alex J Elliot, Maryia McGee, Simon de Lusignan, Anne M Presanis and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. How Artificial Intelligence and New Technologies Can Help the Management of the COVID-19 Pandemic.International journal of environmental research and public health · 2021
    Review
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Paul J BirrellMRC Biostatistics Unit, University of Cambridge, Cambridge Institute of Public Health, Robinson Way, Cambridge, CB2 0SR, UK. paul.birrell@phe.gov.uk.ORCID http://orcid.org/0000-0001-8131-4893
Xu-Sheng ZhangNational Infection Service, Public Health England, 61 Colindale Avenue, London, NW9 5EQ, UK.
Alice CorbellaMRC Biostatistics Unit, University of Cambridge, Cambridge Institute of Public Health, Robinson Way, Cambridge, CB2 0SR, UK.
Edwin van LeeuwenNational Infection Service, Public Health England, 61 Colindale Avenue, London, NW9 5EQ, UK.
Nikolaos PanagiotopoulosNational Infection Service, Public Health England, 61 Colindale Avenue, London, NW9 5EQ, UK.
Katja HoschlerVirus Reference Department, National Infection Service, Public Health England, 61 Colindale Avenue, London, NW9 5EQ, UK.
Alex J ElliotReal-time Syndromic Surveillance Team, Field Service, National Infection Service, Public Health England, 5 St Philip's Place, Birmingham, B3 2PW, UK.
Maryia McGeeNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK.
Simon de LusignanNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX2 6GG, UK.
Anne M PresanisMRC Biostatistics Unit, University of Cambridge, Cambridge Institute of Public Health, Robinson Way, Cambridge, CB2 0SR, UK.
Marc BaguelinVirus Reference Department, National Infection Service, Public Health England, 61 Colindale Avenue, London, NW9 5EQ, UK.
Maria ZambonNational Infection Service, Public Health England, 61 Colindale Avenue, London, NW9 5EQ, UK.
André CharlettNational Infection Service, Public Health England, 61 Colindale Avenue, London, NW9 5EQ, UK.
Richard G PebodyNational Infection Service, Public Health England, 61 Colindale Avenue, London, NW9 5EQ, UK.
Daniela De AngelisMRC Biostatistics Unit, University of Cambridge, Cambridge Institute of Public Health, Robinson Way, Cambridge, CB2 0SR, UK.

Funding

Medical Research Council MC UU 00002/11Medical Research Council MC_UU_00002/11National Institute for Health Research Health Protection Research Units in Respiratory Infections
6 · The paper itself

Abstract

backgroundSince 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.

methodsFour 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.

resultsThe 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

conclusionsThis 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.

Indexed as

EpidemicsInfluenza A Virus, H1N1 SubtypeModels, BiologicalSeasonsAustraliaBiometryCritical CareEnglandFamily PracticeForecastingGeneral PracticeHospitalizationHumansInfluenza, HumanIntensive Care UnitsPandemicsForecastingGP consultationsIntensive care admissionsNowcastingSeasonal influenzaTransmission models

Identifiers

PMID32293372
PMCPMC7158152

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.