Predicting future ocular Chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data

Abstract

Trachoma is an infectious disease characterized by repeated exposures to Chlamydia trachomatis (Ct) that may ultimately lead to blindness. Certain areas, particularly in Africa, pose persistent challenges to elimination of trachoma as a public health problem. Efficiently identifying 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. Median Ct infection prevalence among children 0-5 years old increased from 6% at enrollment to 29% by month 36. 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 clinical 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, programmatic tool for identifying communities with high levels of active ocular Ct infections, but accurate, future prediction in the context of changing transmission remains an open challenge.