Improving our forecasts for trachoma elimination: What else do we need to know?

Area of Science:

• Epidemiology
• Mathematical Biology
• Public Health

Background:

• The World Health Organization (WHO) aims to eliminate trachoma, a leading infectious cause of blindness, by 2020.
• Mathematical modeling is crucial for assessing intervention impacts on infectious disease prevalence.

Purpose of the Study:

• To evaluate four mechanistic mathematical models for trachoma transmission.
• To compare model performance in fitting prevalence data and predicting intervention outcomes for trachoma elimination.

Main Methods:

• Fitting four distinct mechanistic models to age-specific Polymerase Chain Reaction (PCR) and Trachomatous inflammation, follicular (TF) prevalence data.
• Estimating model parameters, including infection and disease episode duration, using Markov Chain Monte Carlo.
• Assessing model fit using the Deviance Information Criterion and simulating intervention scenarios.

Main Results:

• Model 2, which incorporated re-infection, demonstrated the best statistical fit to the prevalence data.
• All models faced challenges fitting the high prevalence of active disease in the youngest age cohort.
• Simulations indicated that Model 2 and Model 4 predicted successful reduction of active trachoma prevalence within 5 years, unlike Model 3.

Conclusions:

• Model structure is a critical factor influencing predictions for trachoma elimination strategies.
• The chosen mathematical model can lead to divergent outcomes for the same intervention scenarios.
• Findings highlight the importance of model selection when planning interventions for neglected tropical diseases.