Predicting future community-level ocular < i > Chlamydia trachomatis < /i > infection prevalence using serological, clinical, molecular, and geospatial data

by Christine Tedijanto, Solomon Aragie, Zerihun Tadesse, Mahteme Haile, Taye Zeru, Scott D. Nash, Dionna M. Wittberg, Sarah Gwyn, Diana L. Martin, Hugh J. W. Sturrock, Thomas M. Lietman, Jeremy D. Keenan, Benjamin F. Arnold Trachoma is an infectious disease characterized by repeated exposures toChlamydia 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 ocularCt infections detected by PCR. We used measurements from 40 communities in the hyperendemic Amhara region of Ethiopia to assess this hypothesis. MedianCt 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 andCt 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 andCt 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 de...
Source: PLoS Neglected Tropical Diseases - Category: Tropical Medicine Authors: Source Type: research