District-Level Forecast of Achieving Trachoma Elimination as a Public Health Problem By 2030: An Ensemble Modelling Approach

• Published in: Clinical infectious diseases Vol. 78; no. Supplement_2; p. S101
• Main Authors: Srivathsan, Ariktha, Abdou, Amza, Al-Khatib, Tawfik, Apadinuwe, Sue-Chen, Badiane, Mouctar D, Bucumi, Victor, Chisenga, Tina, Kabona, George, Kabore, Martin, Kanyi, Sarjo Kebba, Bella, Lucienne, M'po, Nekoua, Masika, Michael, Minnih, Abdellahi, Sitoe, Henis Mior, Mishra, Sailesh, Olobio, Nicholas, Omar, Fatma Juma, Phiri, Isaac, Sanha, Salimato, Seife, Fikre, Sharma, Shekhar, Tekeraoi, Rabebe, Traore, Lamine, Watitu, Titus, Bol, Yak Yak, Borlase, Anna, Deiner, Michael S, Renneker, Kristen K, Hooper, P J, Emerson, Paul M, Vasconcelos, Andreia, Arnold, Benjamin F, Porco, Travis C, Hollingsworth, T Déirdre, Lietman, Thomas M, Blumberg, Seth
• Format: Journal Article
• Language: English
• Published: United States 25.04.2024
• Subjects: Child; Child, Preschool; Disease Eradication - methods; Female; Forecasting; Global Health; Humans; Infant; Male; Mass Drug Administration; Models, Statistical; Prevalence; Public Health; Trachoma - epidemiology; Trachoma - prevention & control; World Health Organization
• ISSN: 1537-6591
• DOI: 10.1093/cid/ciae031

Assessing the feasibility of 2030 as a target date for global elimination of trachoma, and identification of districts that may require enhanced treatment to meet World Health Organization (WHO) elimination criteria by this date are key challenges in operational planning for trachoma programmes. Here we address these challenges by prospectively evaluating forecasting models of trachomatous inflammation-follicular (TF) prevalence, leveraging ensemble-based approaches. Seven candidate probabilistic models were developed to forecast district-wise TF prevalence in 11 760 districts, trained using district-level data on the population prevalence of TF in children aged 1-9 years from 2004 to 2022. Geographical location, history of mass drug administration treatment, and previously measured prevalence data were included in these models as key predictors. The best-performing models were included in an ensemble, using weights derived from their relative likelihood scores. To incorporate the inherent stochasticity of disease transmission and challenges of population-level surveillance, we forecasted probability distributions for the TF prevalence in each geographic district, rather than predicting a single value. Based on our probabilistic forecasts, 1.46% (95% confidence interval [CI]: 1.43-1.48%) of all districts in trachoma-endemic countries, equivalent to 172 districts, will exceed the 5% TF control threshold in 2030 with the current interventions. Global elimination of trachoma as a public health problem by 2030 may require enhanced intervention and/or surveillance of high-risk districts.