Sample size calculation for estimating key epidemiological parameters using serological data and mathematical modelling View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Stéphanie Blaizot, Sereina A. Herzog, Steven Abrams, Heidi Theeten, Amber Litzroth, Niel Hens

ABSTRACT

BACKGROUND: Our work was motivated by the need to, given serum availability and/or financial resources, decide on which samples to test in a serum bank for different pathogens. Simulation-based sample size calculations were performed to determine the age-based sampling structures and optimal allocation of a given number of samples for testing across various age groups best suited to estimate key epidemiological parameters (e.g., seroprevalence or force of infection) with acceptable precision levels in a cross-sectional seroprevalence survey. METHODS: Statistical and mathematical models and three age-based sampling structures (survey-based structure, population-based structure, uniform structure) were used. Our calculations are based on Belgian serological survey data collected in 2001-2003 where testing was done, amongst others, for the presence of Immunoglobulin G antibodies against measles, mumps, and rubella, for which a national mass immunisation programme was introduced in 1985 in Belgium, and against varicella-zoster virus and parvovirus B19 for which the endemic equilibrium assumption is tenable in Belgium. RESULTS: The optimal age-based sampling structure to use in the sampling of a serological survey as well as the optimal allocation distribution varied depending on the epidemiological parameter of interest for a given infection and between infections. CONCLUSIONS: When estimating epidemiological parameters with acceptable levels of precision within the context of a single cross-sectional serological survey, attention should be given to the age-based sampling structure. Simulation-based sample size calculations in combination with mathematical modelling can be utilised for choosing the optimal allocation of a given number of samples over various age groups. More... »

PAGES

51

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12874-019-0692-1

DOI

http://dx.doi.org/10.1186/s12874-019-0692-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112606520

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30845904


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