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

ABSTRACT: 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: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: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 R0 were consistent over time for three of the four models until week 12 of 2018, and there was consistency in the estimation of R0 across the SPC and SS models, and in the ICU attack rates estimated by the ICU and the synthesis model. Estimation and predictions varied according to the assumed levels of pre-season immunity. 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.

SUBMITTER: Birrell PJ

PROVIDER: S-EPMC7158152 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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

Birrell Paul J PJ Zhang Xu-Sheng XS Corbella Alice A van Leeuwen Edwin E Panagiotopoulos Nikolaos N Hoschler Katja K Elliot Alex J AJ McGee Maryia M Lusignan Simon de S Presanis Anne M AM Baguelin Marc M Zambon Maria M Charlett André A Pebody Richard G RG Angelis Daniela De D

BMC public health 20200415 1

<h4>Background</h4>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.<h4>Methods</h4>Four transmission models were used in forecasting the 2017/2018 seasonal influenza epidemic in England: a stratif ...[more]

PMID: 32293372

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Project description:The 2017-2018 influenza epidemic season in Russia was characterized by a relatively low morbidity and mortality. We evaluated herd immunity prior to the 2017-2018 influenza season in hemagglutination inhibition assay, and performed characterization of influenza viruses isolated from severe or fatal influenza cases and from influenza cases in people vaccinated in the fall of 2017. During the 2017-2018 epidemic season, 87 influenza A and B viruses were isolated and viruses of the 75 influenza cases, including selected viral isolates and viruses analyzed directly from the original clinical material, were genetically characterized. The analyzed A(H1N1)pdm09 viruses belonged to clade 6B.1, B/Yamagata-like viruses belonged to clade 3, and B/Victoria-like viruses belonged to clade 1A and they were antigenically similar to the corresponding vaccine strains. A(H3N2) viruses belonged to clade 3C.2a and were difficult to characterize antigenically and the analysis indicated antigenic differences from the corresponding egg-grown vaccine strain. The next generation sequencing revealed the presence of D222/G/N polymorphism in the hemagglutinin gene in 32% of the analyzed A(H1N1)pdm09 lethal cases. This study demonstrated the importance of monitoring D222G/N polymorphism, including detection of minor viral variants with the mutations, in the hemagglutinin gene of A(H1N1)pdm09 for epidemiological surveillance. One strain of influenza virus A(H1N1)pdm09 was resistant to oseltamivir and had the H275Y amino acid substitution in the NA protein. All other isolates were susceptible to NA inhibitors. Prior to the 2017-2018 epidemic season, 67.4 million people were vaccinated, which accounted for 46.6% of the country's population. Just before the epidemic season 33-47% and 24-30% of blood sera samples collected within the territory of Russia showed the presence of protective antibody titers against vaccine strains of influenza A and influenza B/Victoria-like, respectively. Mass vaccination of the population had evidently reduced the severity of the flu epidemic during the 2017-2018 influenza epidemic season in Russia.

Dataset Information

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

Publications

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

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