Probabilistic forecasts of trachoma transmission at the district level: A statistical model comparison

Amy Pinsent, Fengchen Liu, Michael Deiner, Paul M Emerson, Ana Bhaktiari, Travis C Porco, Thomas M Lietman, Manoj Gambhir

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)


The World Health Organization and its partners are aiming to eliminate trachoma as a public health problem by 2020. In this study, we compare forecasts of TF prevalence in 2011 for 7 different statistical and mechanistic models across 9 de-identified trachoma endemic districts, representing 4 unique trachoma endemic countries. We forecast TF prevalence between 1–6 years ahead in time and compare the 7 different models to the observed 2011 data using a log-likelihood score. An SIS model, including a district-specific random effect for the district-specific transmission coefficient, had the highest log-likelihood score across all 9 districts and was therefore the best performing model. While overall the deterministic transmission model was the least well performing model, although it did comparably well to the other models for 8 of 9 districts. We perform a statistically rigorous comparison of the forecasting ability of a range of mathematical and statistical models across multiple endemic districts between 1 and 6 years ahead of the last collected TF prevalence data point in 2011, assessing results against surveillance data. This study is a step towards making statements about likelihood and time to elimination with regard to the WHO GET2020 goals.

Original languageEnglish
Pages (from-to)48-55
Number of pages8
Publication statusPublished - 1 Mar 2017


  • Elimination
  • Forecasting
  • Model comparison
  • Trachoma

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