Back

Design considerations for incorporating serological monitoring into trachoma prevalence surveys

Kamau, E.; Gass, K.; Harding-Esch, E. M.; Saboya Diaz, M. I.; Ante-Testard, P. A.; Kello, A. B.; Bailey, R. L.; Fornace, K.; Solomon, A. W.; Nash, S. D.; Arnold, B. F.; Global Trachoma Serosurveillance Study Group,

2025-09-07 epidemiology
10.1101/2025.09.05.25334960 medRxiv
Show abstract

Serology is increasingly used to monitor disease transmission and elimination. Embedding dried blood spot collection in trachoma surveys allows transmission intensity inference through population-level seroconversion rates (SCR) estimation, but there is no formal assessment of the required sample size. Using data from 40 prevalence surveys, we estimated intra-cluster correlation coefficient, a key design parameter, and assessed survey design considerations for incorporating serological monitoring. Design scenarios focused on recent proposed operational SCR thresholds (2.2 [no action needed] and 4.5 [action needed] per 100 child years) for interpretation of serological data in low-transmission and post-elimination settings. We evaluated SCR estimation in 42 two-stage designs by calculating precision (confidence interval width around an SCR value) and power (the measure of deviation of suggested thresholds to the SCR value). When the underlying SCR is [&le;]1.5 and >5.7 per 100 person-years, sample sizes between 300-2000 allowed good precision of SCR estimation. The same sample range would correctly classify areas as above or below the thresholds with >80% power when the underlying SCR is <1.7 or >5 per 100 person-years in lower and higher endemicity settings, respectively. Our results support estimation of serological data via the recommended population-based survey design for trachoma monitoring.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.