title: Probabilistic forecasts of trachoma transmission at the district level: A statistical model comparison.
abstract: 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.
authors.affiliation: Department of Public Health and Preventative Medicine, Monash University, Melbourne, Australia. Electronic address: amy.pinsent@monash.edu.
authors.forename: Amy
authors.initials: A
authors.lastname: Pinsent
authors.affiliation: F.I. Proctor Foundation, University of California San Francisco, San Francisco, CA, USA.
authors.forename: Fengchen
authors.initials: F
authors.lastname: Liu
authors.affiliation: F.I. Proctor Foundation, University of California San Francisco, San Francisco, CA, USA; Department of Ophthalmology, University of California San Francisco, San Francisco, CA, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Deiner
authors.affiliation: International Trachoma Initiative, Atlanta, GA, USA; School of Public Health, Emory University, Atlanta, GA, USA.
authors.forename: Paul
authors.initials: P
authors.lastname: Emerson
authors.affiliation: International Trachoma Initiative, Atlanta, GA, USA.
authors.forename: Ana
authors.initials: A
authors.lastname: Bhaktiari
authors.affiliation: F.I. Proctor Foundation, University of California San Francisco, San Francisco, CA, USA; Department of Ophthalmology, University of California San Francisco, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.
authors.forename: Travis C
authors.initials: TC
authors.lastname: Porco
authors.affiliation: F.I. Proctor Foundation, University of California San Francisco, San Francisco, CA, USA; Department of Ophthalmology, University of California San Francisco, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA; Global Health Sciences, University of California San Francisco, San Francisco, CA, USA.
authors.forename: Thomas
authors.initials: T
authors.lastname: Lietman
authors.affiliation: Department of Public Health and Preventative Medicine, Monash University, Melbourne, Australia.
authors.forename: Manoj
authors.initials: M
authors.lastname: Gambhir
mesh_terms: D005544:Forecasting; D006801:Humans; D015233:Models, Statistical; D015995:Prevalence; D014141:Trachoma / Q000453:epidemiology* / Q000635:transmission*
keywords: Elimination; Forecasting; Model comparison; Trachoma