Improving our forecasts for trachoma elimination: What else do we need to know?
Improving our forecasts for trachoma elimination: What else do we need to know?
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DOI：
10.1371/journal.pntd.0005378
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发表时间：
2017-02-01
影响因子：
3.8
通讯作者：
Gambhir, Manoj
中科院分区：
文献类型：
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作者：
Pinsent, Amy;Gambhir, Manoj
世界卫生组织（世卫组织）的目标是到2020年消除沙眼作为一个公共卫生问题。数学模型用于一系列传染病，以评估不同干预战略对感染或疾病流行的影响。在这里，我们评估了四种不同的机械数学模型，可以真实地代表沙眼传播的性能。我们将四种不同的沙眼传播机制模型与横断面年龄特异性聚合酶链反应（PCR）和沙眼性炎症、滤泡（TF）患病率数据进行了拟合。我们估计每个模型中的4或3个参数，包括使用马尔可夫链蒙特卡罗的个体感染和疾病发作的持续时间。我们通过计算偏差信息准则来评估每个模型对数据的拟合性能。然后，我们对每个模型结构的不同干预措施的实施进行建模，以评估不同模型结构消除沙眼的可行性。在疾病状态下允许一些再感染的模型结构（模型2）在统计学上是表现最好的模型。所有模型都很难适应最年轻年龄组中活动性疾病的高患病率。我们的模拟表明，对于模型3，随着每年抗生素治疗和传播减少，将活动性疾病患病率降低到
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