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Improving measles incidence inference using age-structured serological data.

Joaquin M Prada1, C Jessica E Metcalf2, Matthew J Ferrari3

  • 1Faculty of Health and Medical Sciences,University of Surrey,Guildford,UK.

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|August 7, 2018
PubMed
Summary
This summary is machine-generated.

Leveraging age data from confirmed measles cases can accurately estimate infections in non-tested samples. This approach enhances fever-rash surveillance, improving outbreak control and resource allocation for measles elimination.

Keywords:
Infectious disease epidemiologymeasles (rubeola)public healthrubella

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Area of Science:

  • Epidemiology
  • Public Health
  • Infectious Disease Surveillance

Background:

  • Measles elimination is a global health priority, with fever-rash surveillance crucial for early outbreak detection.
  • Increasingly, symptom-based surveillance captures non-measles infections like rubella, complicating accurate case identification.
  • Laboratory confirmation of measles is recommended but often logistically challenging for all suspected cases.

Purpose of the Study:

  • To develop a method for inferring measles infection status in non-tested samples using age data from confirmed cases.
  • To strengthen symptom-based surveillance by improving the accuracy of estimated measles incidence.
  • To determine the optimal proportion of cases requiring serological testing for reliable surveillance.

Main Methods:

  • An age-specific confirmation model was applied to serological data from confirmed measles cases.
  • The model inferred the infection status of non-tested samples based on age distribution.
  • Analysis was conducted using data from three countries with diverse epidemiological profiles in Africa.

Main Results:

  • Age data from confirmed measles cases can effectively infer the status of untested samples.
  • The study identified the proportion of cases needing serological testing to achieve desired accuracy levels.
  • The required testing proportion varies based on the specific epidemiological context.

Conclusions:

  • Age-stratified analysis of confirmed cases enhances the reliability of symptom-based surveillance data.
  • This approach optimizes resource allocation for laboratory confirmation, supporting efficient outbreak response.
  • The findings contribute to refining measles incidence estimates and advancing global elimination efforts.