Predicting future community-level ocular Chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data.
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 c...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-03-01
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| Series: | PLoS Neglected Tropical Diseases |
| Online Access: | https://doi.org/10.1371/journal.pntd.0010273 |
| _version_ | 1828186039998480384 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Christine Tedijanto |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T07:12:26Z |
| format | Article |
| id | doaj.art-157f2843a5524a1b80ff995075f99447 |
| institution | Directory Open Access Journal |
| issn | 1935-2727 1935-2735 |
| language | English |
| last_indexed | 2024-04-12T07:12:26Z |
| publishDate | 2022-03-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Neglected Tropical Diseases |
| spelling | doaj.art-157f2843a5524a1b80ff995075f994472022-12-22T03:42:34ZengPublic Library of Science (PLoS)PLoS Neglected Tropical Diseases1935-27271935-27352022-03-01163e001027310.1371/journal.pntd.0010273Predicting future community-level ocular Chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data.Christine TedijantoSolomon AragieZerihun TadesseMahteme HaileTaye ZeruScott D NashDionna M WittbergSarah GwynDiana L MartinHugh J W SturrockThomas M LietmanJeremy D KeenanBenjamin F ArnoldTrachoma 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.https://doi.org/10.1371/journal.pntd.0010273 |
| spellingShingle | 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 Predicting future community-level ocular Chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data. PLoS Neglected Tropical Diseases |
| title | Predicting future community-level ocular Chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data. |
| title_full | Predicting future community-level ocular Chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data. |
| title_fullStr | Predicting future community-level ocular Chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data. |
| title_full_unstemmed | Predicting future community-level ocular Chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data. |
| title_short | Predicting future community-level ocular Chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data. |
| title_sort | predicting future community level ocular chlamydia trachomatis infection prevalence using serological clinical molecular and geospatial data |
| url | https://doi.org/10.1371/journal.pntd.0010273 |
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