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

Abstract

The World Health Organization (WHO) has targeted trachoma for elimination as a public health concern by 2020. Mathematical modelling is used for a range of infectious diseases to assess the impact of different intervention strategies on the prevalence of infection or disease. Here we evaluate the performance of four different mechanistic mathematical models that could all realistically represent trachoma transmission. We fit the four different mechanistic models of trachoma transmission to cross-sectional age-specific Polymerase Chain Reaction (PCR) and Trachomatous inflammation, follicular (TF) prevalence data. We estimate 4 or 3 parameters within each model, including the duration of an individual’s infection and disease episode using Markov Chain Monte Carlo. We assess the performance of each models fit to the data by calculating the deviance information criterion. We then model the implementation of different interventions for each model structure to assess the feasibility of elimination of trachoma with different model structures. A model structure which allowed some re-infection in the disease state (Model 2) was statistically the most well performing model. All models struggled to fit to the very high prevalence of active disease in the youngest age group. Our simulations suggested that for Model 3, with annual antibiotic treatment and transmission reduction, the chance of reducing active disease prevalence to < 5% within 5 years was very low, while Model 2 and 4 could ensure that active disease prevalence was reduced within 5 years. Model 2 here fitted to the data best of the models evaluated. The appropriate level of susceptibility to re-infection was, however, challenging to identify given the amount and kind of data available. We demonstrate that the model structure assumed can lead to different end points following the implementation of the same interventions. Our findings are likely to extend beyond trachoma and should be considered when modelling other neglected tropical diseases.

Models of Trachoma Transmission and Their Policy Implications: From Control to Elimination

“…Intensive targeting of a residual core group that remains infected following annual MDA is currently being tested in severely affected regions of Ethiopia. While transmission models may help inform the design of new strategies for these regions, a number of biological uncertainties underlying these models remain, and more detailed data would probably be required to distinguish between them [ 50 ]. Advances in mathematical and statistical modeling are allowing forecasting of the district-level prevalence of trachoma to be improved.…”

Optimising sampling regimes and data collection to inform surveillance for trachoma control

“…To simulate data we used an age-structured ordinary differential equation (ODE) transmission model. We used a previously validated model structure that was identified as the most parsimonious and appropriate model when fitting to a single cross-section of age-specific PCR and TF prevalence data [ 12 ]. We used the framework of the classic SEIR model structure, with slightly different notation to indicate the different infection states for trachoma Fig 1 .…”

“…Individuals were susceptible to infection in the ( S ) state, exposed and incubating in the ( E ) state, who would test PCR positive, infected and infectious ( ID ) with detectable TF and who would also test PCR positive and those who remained diseased but were no longer infectious to others ( D ) (TF positive only), individuals in the D state were susceptible to re-infection with a reduced probability. Those who were re-infected then returned to the AI state (both PCR and TF positive) [ 12 ]. For each endemicity we simulated 3 annual rounds of MDA with azithromycin distributed to the whole community, assuming 80% coverage and a treatment efficacy of 85% [ 13 ].…”

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

“…The first type of model evaluated was an age-structured deterministic ordinary differential equation (ODE) transmission model. We used a model structure that has been statistically chosen ( Pinsent and Gambhir, 2017 ) as the most appropriate and parsimonious when fitting to cross-sectional PCR and TF prevalence data ( West et al, 2005 ). We consider individuals as susceptible to infection ( S ), exposed and incubating ( E ), infected and infectious with detectable TF ( AI ) and those who remain diseased but no longer infectious to others ( D ), individuals in the D state are susceptible to re-infection with a reduced probability.…”