ArticleBMC medical research methodology2019
Sample size calculation for estimating key epidemiological parameters using serological data and mathematical modelling.
Article in BMC medical research methodology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Pooled it
- Optimizing age-structured sampling for estimating the seroconversion rate in malaria seroepidemiology: a simulation study.Scientific reports · 2026Article
- Estimating the incidence of SARS-CoV-2 infections by jointly modelling seroprevalence, hospitalization and mortality data, February 2020 - January 2021, Belgium.Archives of public health = Archives belges de sante publique · 2026Article
- A simulation-based method to inform serosurvey design for estimating the force of infection using existing blood samples.PLoS computational biology · 2023Article
- serosim: An R package for simulating serological data arising from vaccination, epidemiological and antibody kinetics processes.PLoS computational biology · 2023Article
- Optimizing prevalence estimates for a novel pathogen by reducing uncertainty in test characteristics.Epidemics · 2022Article
- Article
- Prevalence and predictors of anti-SARS-CoV-2 serology in a highly vulnerable population of Rio de Janeiro: A population-based serosurvey.Lancet regional health. Americas · 2022Article
- Ovine Neosporosis: The Current Global Situation.Animals : an open access journal from MDPI · 2022Review
- Response: Commentary: Statistical Modeling for the Prediction of Infectious Disease Dissemination With Special Reference to COVID-19 Spread.Frontiers in public health · 2021Article
- Rubella seroprevalence using residual samples from the South African measles surveillance program: a cross-sectional analytic study.Human vaccines & immunotherapeutics · 2020Article
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOur 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.
methodsStatistical 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.
resultsThe 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.
conclusionsWhen 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.
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