Trachoma is an
infectious disease characterized by repeated exposures to Chlamydia trachomatis (Ct) that may ultimately lead to
blindness. Efficient identification of communities with high
infection burden could help target more intensive control efforts. We hypothesized that
IgG seroprevalence in combination with geospatial layers, machine learning, and model-based geostatistics would be able to accurately predict future community-level ocular Ct
infections detected by PCR. We used measurements from 40 communities in the hyperendemic Amhara region of Ethiopia to assess this hypothesis. Median Ct
infection prevalence among children 0-5 years old increased from 6% at enrollment, in the context of recent
mass drug administration (MDA), to 29% by month 36, following three years without MDA. At baseline, correlation between seroprevalence and Ct
infection was stronger among children 0-5 years old (ρ = 0.77) than children 6-9 years old (ρ = 0.48), and stronger than the correlation between active
trachoma and Ct
infection (0-5y ρ = 0.56; 6-9y ρ = 0.40). Seroprevalence was the strongest concurrent predictor of
infection prevalence at month 36 among children 0-5 years old (cross-validated R2 = 0.75, 95% CI: 0.58-0.85), though predictive performance declined substantially with increasing temporal lag between predictor and outcome measurements. Geospatial variables, a spatial Gaussian process, and stacked ensemble machine learning did not meaningfully improve predictions. Serological markers among children 0-5 years old may be an objective tool for identifying communities with high levels of ocular Ct
infections, but accurate, future prediction in the context of changing transmission remains an open challenge.