Short-term forecasting of the prevalence of trachoma: expert opinion, statistical regression, versus transmission models

Fengchen Liu, Travis C. Porco, Abdou Amza, Boubacar Kadri, Baido Nassirou, Sheila K. West, Robin L. Bailey, Jeremy D. Keenan, Anthony W. Solomon, Paul M. Emerson, Manoj Gambhir, Thomas M. Lietman

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Background Trachoma programs rely on guidelines made in large part using expert opinion of what will happen with and without intervention. Large community-randomized trials offer an opportunity to actually compare forecasting methods in a masked fashion. Methods The Program for the Rapid Elimination of Trachoma trials estimated longitudinal prevalence of ocular chlamydial infection from 24 communities treated annually with mass azithromycin. Given antibiotic coverage and biannual assessments from baseline through 30 months, forecasts of the prevalence of infection in each of the 24 communities at 36 months were made by three methods: the sum of 15 experts’ opinion, statistical regression of the squareroot- transformed prevalence, and a stochastic hidden Markov model of infection transmission (Susceptible-Infectious-Susceptible, or SIS model). All forecasters were masked to the 36-month results and to the other forecasts. Forecasts of the 24 communities were scored by the likelihood of the observed results and compared using Wilcoxon’s signed-rank statistic. Findings Regression and SIS hidden Markov models had significantly better likelihood than community expert opinion (p = 0.004 and p = 0.01, respectively). All forecasts scored better when perturbed to decrease Fisher’s information. Each individual expert’s forecast was poorer than the sum of experts. Interpretation Regression and SIS models performed significantly better than expert opinion, although all forecasts were overly confident. Further model refinements may score better, although would need to be tested and compared in new masked studies. Construction of guidelines that rely on forecasting future prevalence could consider use of mathematical and statistical models.

Original languageEnglish (US)
Article numberA018
JournalPLoS neglected tropical diseases
Issue number8
StatePublished - 2015

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health
  • Infectious Diseases


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