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

• Published in: PLoS neglected tropical diseases Vol. 16; no. 3; p. e0010273
• Main Authors: Tedijanto, Christine, Aragie, Solomon, Tadesse, Zerihun, Haile, Mahteme, Zeru, Taye, Nash, Scott D., Wittberg, Dionna M., Gwyn, Sarah, Martin, Diana L., Sturrock, Hugh J. W., Lietman, Thomas M., Keenan, Jeremy D., Arnold, Benjamin F.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.03.2022; Public Library of Science (PLoS)
• Subjects: Age groups; Analysis; Anti-Bacterial Agents - therapeutic use; Antibiotics; Antigens; Azithromycin; Biology and Life Sciences; Blindness; Care and treatment; Child; Child, Preschool; Children; Chlamydia; Chlamydia trachomatis; Complications and side effects; Computer and Information Sciences; Context; Correlation; Development and progression; Disease transmission; Ethiopia - epidemiology; Gaussian process; Geospatial data; Geostatistics; Humans; Immunoglobulin G; Infant; Infant, Newborn; Infections; Infectious diseases; Inflammation; Learning algorithms; Machine learning; Medicine and Health Sciences; Methods; Nucleotide sequence; PCR; People and Places; Performance prediction; Physical Sciences; Prevalence; Prevention; Public health; Research and Analysis Methods; Risk factors; Sanitation; Seroepidemiologic Studies; Serology; Sexually transmitted diseases; Spatial data; STD; Trachoma; Trachoma - prevention & control; Transmission; Tropical diseases; Ethiopia; Africa
• ISSN: 1935-2735; 1935-2727; 1935-2735
• DOI: 10.1371/journal.pntd.0010273

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 R 2 = 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.