Estimation of Seasonal Influenza Attack Rates and Antibody Dynamics in Children Using Cross-Sectional Serological Data

Amanda Minter; Katja Hoschler; Ya Jankey Jagne; Hadijatou Sallah; Edwin Armitage; Benjamin Lindsey; James A. Hay; Steven Riley; Thushan I. De Silva; Adam J. Kucharski

doi:10.1093/infdis/jiaa338

Estimation of Seasonal Influenza Attack Rates and Antibody Dynamics in Children Using Cross-Sectional Serological Data

Amanda Minter
, Katja Hoschler
, Ya Jankey Jagne
, Hadijatou Sallah
, Edwin Armitage
, Benjamin Lindsey
, James A. Hay
, Steven Riley
, Thushan I. De Silva
, Adam J. Kucharski^*

^*Corresponding author for this work

Respiratory Virus Unit

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

Directly measuring evidence of influenza infections is difficult, especially in low-surveillance settings such as sub-Saharan Africa. Using a Bayesian model, we estimated unobserved infection times and underlying antibody responses to influenza A/H3N2, using cross-sectional serum antibody responses to 4 strains in children aged 24-60 months. Among the 242 individuals, we estimated a variable seasonal attack rate and found that most children had ≥1 infection before 2 years of age. Our results are consistent with previously published high attack rates in children. The modeling approach highlights how cross-sectional serological data can be used to estimate epidemiological dynamics.

Original language	English
Pages (from-to)	1750-1754
Number of pages	5
Journal	Journal of Infectious Diseases
Volume	225
Issue number	10
DOIs	https://doi.org/10.1093/infdis/jiaa338
Publication status	Published - 15 May 2022

Bibliographical note

Publisher Copyright:
© 2020 The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America.

Keywords

Bayesian model
The Gambia
childhood infection
influenza
serology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1093/infdis/jiaa338

Cite this

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title = "Estimation of Seasonal Influenza Attack Rates and Antibody Dynamics in Children Using Cross-Sectional Serological Data",

abstract = "Directly measuring evidence of influenza infections is difficult, especially in low-surveillance settings such as sub-Saharan Africa. Using a Bayesian model, we estimated unobserved infection times and underlying antibody responses to influenza A/H3N2, using cross-sectional serum antibody responses to 4 strains in children aged 24-60 months. Among the 242 individuals, we estimated a variable seasonal attack rate and found that most children had ≥1 infection before 2 years of age. Our results are consistent with previously published high attack rates in children. The modeling approach highlights how cross-sectional serological data can be used to estimate epidemiological dynamics.",

keywords = "Bayesian model, The Gambia, childhood infection, influenza, serology",

author = "Amanda Minter and Katja Hoschler and Jagne, \{Ya Jankey\} and Hadijatou Sallah and Edwin Armitage and Benjamin Lindsey and Hay, \{James A.\} and Steven Riley and \{De Silva\}, \{Thushan I.\} and Kucharski, \{Adam J.\}",

note = "Publisher Copyright: {\textcopyright} 2020 The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America.",

year = "2022",

month = may,

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doi = "10.1093/infdis/jiaa338",

language = "English",

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T1 - Estimation of Seasonal Influenza Attack Rates and Antibody Dynamics in Children Using Cross-Sectional Serological Data

AU - Minter, Amanda

AU - Hoschler, Katja

AU - Jagne, Ya Jankey

AU - Sallah, Hadijatou

AU - Armitage, Edwin

AU - Lindsey, Benjamin

AU - Hay, James A.

AU - Riley, Steven

AU - De Silva, Thushan I.

AU - Kucharski, Adam J.

PY - 2022/5/15

Y1 - 2022/5/15

N2 - Directly measuring evidence of influenza infections is difficult, especially in low-surveillance settings such as sub-Saharan Africa. Using a Bayesian model, we estimated unobserved infection times and underlying antibody responses to influenza A/H3N2, using cross-sectional serum antibody responses to 4 strains in children aged 24-60 months. Among the 242 individuals, we estimated a variable seasonal attack rate and found that most children had ≥1 infection before 2 years of age. Our results are consistent with previously published high attack rates in children. The modeling approach highlights how cross-sectional serological data can be used to estimate epidemiological dynamics.

AB - Directly measuring evidence of influenza infections is difficult, especially in low-surveillance settings such as sub-Saharan Africa. Using a Bayesian model, we estimated unobserved infection times and underlying antibody responses to influenza A/H3N2, using cross-sectional serum antibody responses to 4 strains in children aged 24-60 months. Among the 242 individuals, we estimated a variable seasonal attack rate and found that most children had ≥1 infection before 2 years of age. Our results are consistent with previously published high attack rates in children. The modeling approach highlights how cross-sectional serological data can be used to estimate epidemiological dynamics.

KW - Bayesian model

KW - The Gambia

KW - childhood infection

KW - influenza

KW - serology

UR - https://www.scopus.com/pages/publications/85130643645

U2 - 10.1093/infdis/jiaa338

DO - 10.1093/infdis/jiaa338

M3 - Article

C2 - 32556290

AN - SCOPUS:85130643645

SN - 0022-1899

VL - 225

SP - 1750

EP - 1754

JO - Journal of Infectious Diseases

JF - Journal of Infectious Diseases

IS - 10

ER -

Estimation of Seasonal Influenza Attack Rates and Antibody Dynamics in Children Using Cross-Sectional Serological Data

Abstract

Bibliographical note

Keywords

UN SDGs

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