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

• Main Authors: Stéphanie Blaizot, Sereina A. Herzog, Steven Abrams, Heidi Theeten, Amber Litzroth, Niel Hens
• Format: Article
• Language: English
• Published: BMC 2019-03-01
• Series: BMC Medical Research Methodology
• Subjects: Infectious diseases; Mathematical models; Study design; Sample size; Allocation; Precision
• Online Access: http://link.springer.com/article/10.1186/s12874-019-0692-1

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.