Trachoma, caused by ocular Chlamydia trachomatis infection, is the leading infectious cause of blindness worldwide and been targeted for global elimination as a public health problem by 2030. As we approach the endgame, there is broad interest in the use of serologic surveys to support control programs. IgG antibody responses to C. trachomatis in children enable accurate population-level assessments of trachoma endemicity because they integrate exposure over time and reflect recent transmission. After years of assay development, a key gap in the field is to formalize the epidemiologic methods used for trachoma serology surveys. Our overall objective is to advance the methods used for the design and analysis of trachoma serology surveys. We will assemble a large, contemporary global dataset for trachoma serology across a gradient of endemicity, paired with clinical signs and molecular measures of infection (>100,000 blood specimens tested in 19 studies from 2010-2024). Aim 1 will develop robust methods to translate antibody response into population- level measures of transmission from endemic settings to post-elimination. We hypothesize that as populations approach elimination age-seroprevalence curves will flatten and seroconversion rates, a measure of force of infection, will approach zero. We will estimate age-seroprevalence curves semi-parametrically, and derive summary measures from the curves (e.g., seroprevalence, force of infection). We will compare serologic measures between populations of different endemicity. We further hypothesize that different serologic summary measures (mean IgG levels, seroprevalence, force of infection) will provide similar information about heterogeneity in transmission. We will compare serologic measures with one-another and with separate measures of trachoma (PCR infection, clinical signs) across geographic scales from villages to districts. Aim 2 will determine if model-based geostatistics improve the efficiency of serological survey design and enable finer scale targeting of control programs as populations approach elimination. We hypothesize that as trachoma approaches elimination, it will become more focal with “hotspots” of elevated seroprevalence among children that shrink in scale from districts down to individual villages. We hypothesize that if surveys account for this spatial structure in their design they will more efficiently monitor trachoma than random samples alone, and control programs that use spatial predictions to make treatment decisions at smaller spatial scales could more narrowly target antibiotic distribution. In analyses of 11 georeferenced studies that span a range of endemicity, we will apply recent advances in geospatial design to trachoma serology and compare prevalence estimates using the new approach with the current standard, population-based random samples. We seek to identify the most efficient sampling strategies to inform decision making as populations approach elimination, a...