Evolution-informed forecasting of seasonal influenza A (H3N2)

Du, Xiangjun; King, Aaron A.; Woods, Robert J.; Pascual, Mercedes

doi:10.1126/scitranslmed.aan5325

Cited by 59 publications

(42 citation statements)

References 66 publications

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“…We estimated that protection in both children and adults wanes with an average half-life of 3.5-7 years, lasts longer against H1N1pdm09 than H3N2, and lasts slightly longer in adults compared to children against H3N2. These timescales are consistent with the estimated decay of immunity over 2-10 years due to antigenic evolution in population-level models [42,43]. In contrast to adults, the dependence of protection on HI titer in children leads to substantial variation in susceptibility over time (Fig.…”

Section: Discussionsupporting

confidence: 84%

Age-specific differences in the dynamics of protective immunity to influenza

Ranjeva

Subramanian

Fang

et al. 2018

Preprint

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Influenza A viruses evolve rapidly to escape host immunity, such that individuals can be infected multiple times with the same subtype. The form and duration of protective immunity after each influenza infection are poorly understood. Here, we quantify the dynamics of protective immunity against influenza A virus infections by fitting individual-level mechanistic models to longitudinal serology from children and adults in a household cohort study. We find that most protection in children is explained by antibody titers measured by the hemagglutination inhibition (HI) assay. In contrast, in adults, HI antibody titers explain a smaller fraction of protection. Protection against circulating strains wanes to approximately 50% of peak levels 3.5-7 years after infection in both age groups, and wanes faster against influenza A(H3N2) than A(H1N1)pdm09. Protection against H3N2 lasts longer in adults than in children. Our results suggest that the focus of influenza antibody responses changes over time from the highly mutable hemagglutinin head to other epitopes, consistent with the immunological theory of original antigenic sin, and that this change of focus might affect protection. Additionally, we estimate that imprinting, or primary infection with a subtype of one phylogenetic group, has little to no effect on the risk of non-medically attended influenza infections in adults. We also find no evidence of long-term cross-protection between subtypes. This work underscores the need for longitudinal data on multiple components of the immune response to better understand the development of immunity and differences in susceptibility within populations.

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Section: Discussionsupporting

confidence: 84%

Age-specific differences in the dynamics of protective immunity to influenza

Ranjeva

Subramanian

Fang

et al. 2018

Preprint

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“…Inspecting the posterior distribution for δ, the majority of samples attained values greater than 0.9. The inferred magnitude implies long term retention of immunity acquired from natural infection (assuming no further vaccination or infection event to alter the immunity landscape), in agreement with prior studies supporting infection acquired immunity waning over a time scale exceeding a year [30][31][32]).…”

Section: Fitting the Extended Model To The Datasupporting

confidence: 86%

Seasonal influenza: Modelling approaches to capture immunity propagation

Hill

Petrou

Lusignan

et al. 2019

Preprint

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Method:Data were extracted from four RCGP Research and Surveillance Centre (RSC) databases [1]. UK general practice is a registration based system where all citizens can register with a single GP of their choice. Practices are computerised, and data entered into computerised medical record systems either as coded data [2], or free text. We extracted the coded data, and our results are based on this element of the record [3]. We extract all coded data, pseudonymising as close to sources as possible. Where patients have a range of codes inserted suggesting they opt out of record sharing we do not analyse their data [4].

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“…This increased interest has been fueled in part by the promise of 'big data', that near real-time data streams of information ranging from large-scale population behavior [1] to microscopic changes in a pathogen 1 [2] could lead to measurable improvements in how disease transmission is measured, forecasted, and controlled [3]. With the spectre of a global pandemic looming, improving infectious disease forecasting continues to be a central priority of global health preparedness efforts.…”

Section: Introductionmentioning

confidence: 99%

Forecasting seasonal influenza in the U.S.: A collaborative multi-year, multi-model assessment of forecast performance

Reich

Brooks

Fox

et al. 2018

Preprint

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Influenza infects an estimated 9 to 35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multi-institution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the US for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of 7 targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the US, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1, 2 and 3 weeks ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision-making.

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Evolution-informed forecasting of seasonal influenza A (H3N2)

Cited by 59 publications

References 66 publications

Age-specific differences in the dynamics of protective immunity to influenza

Age-specific differences in the dynamics of protective immunity to influenza

Seasonal influenza: Modelling approaches to capture immunity propagation

Forecasting seasonal influenza in the U.S.: A collaborative multi-year, multi-model assessment of forecast performance

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