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.

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Bayesian model
The Gambia
childhood infection
influenza
serology

Access to Document

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,

day = "15",

doi = "10.1093/infdis/jiaa338",

language = "English",

volume = "225",

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TY - JOUR

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

UN SDGs

Keywords

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