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.
Highlights
Trachoma remains the world’s leading infectious cause of blindness. 200 million people are reported to be at risk of infection, across 42 endemic countries [1]
Mathematical models are used by researchers to examine the spread of infectious diseases and understand how they can be controlled
For trachoma we identify four different model structures which could all represent the PLOS Neglected Tropical Diseases | DOI:10.1371/journal.pntd
Summary
Trachoma remains the world’s leading infectious cause of blindness. 200 million people are reported to be at risk of infection, across 42 endemic countries [1]. Trachoma remains the world’s leading infectious cause of blindness. 200 million people are reported to be at risk of infection, across 42 endemic countries [1]. Two goals have been developed to assist endemic countries striving to achieve the elimination of trachoma as a public health problem. Mathematical modelling has been successful in helping to formulate guidelines for the ongoing surveillance and control of a range of infectious diseases including malaria [3], onchocerciasis [4], lymphatic filariasis [5] and soil-transmitted helminths [6]. Mathematical models can be used to provide guidance on suggested timelines to elimination or control, for a given set of initial conditions and available interventions. To generate informative and accurate predictions, models need to be informed by high quality epidemiological data, in terms of the duration of infection and disease, as it is these states which are detectable through diagnostic tests
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