Defining the relationship between infection prevalence and clinical incidence of Plasmodium falciparum malaria.
Defining the relationship between infection prevalence and clinical incidence of Plasmodium falciparum malaria.
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DOI：
10.1038/ncomms9170
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发表时间：
2015-09-08
影响因子：
16.6
通讯作者：
Gething PW
中科院分区：
文献类型：
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作者：
Cameron E;Battle KE;Bhatt S;Weiss DJ;Bisanzio D;Mappin B;Dalrymple U;Hay SI;Smith DL;Griffin JT;Wenger EA;Eckhoff PA;Smith TA;Penny MA;Gething PW
在许多国家，卫生系统的数据仍然过于薄弱，无法准确地列举恶性疟原虫疟疾病例。为此，开发了制图方法，将感染流行率图与数学关系联系起来，以预测临床疟疾发病率。微观模拟（或“代理为基础的”）模型代表了一个强大的新的范例，用于定义这种关系，但是，模型结构和校准数据的差异意味着，还没有共识，最佳形式用于疾病负担估计。在这里，我们开发了一个贝叶斯统计过程，结合功能回归为基础的模型仿真与马尔可夫链蒙特卡罗抽样校准三个选定的微观模拟模型对一个专门建立的数据集的年龄结构的患病率和发病率计数。这允许生成按年龄、传播季节性、治疗水平和暴露史分层的患病率-发病率关系的集合预测，由此我们预测随着传播和患病率的逐步降低，大规模干预活动的投资回报将加速。 数学模型被用来预测疟疾负担，为疾病控制工作提供信息。在这里，卡梅隆等人使用贝叶斯统计来校准以前的模型对年龄结构的患病率和发病率的数据集，生成分层预测的患病率-发病率的关系。
In many countries health system data remain too weak to accurately enumerate Plasmodium falciparum malaria cases. In response, cartographic approaches have been developed that link maps of infection prevalence with mathematical relationships to predict the incidence rate of clinical malaria. Microsimulation (or ‘agent-based') models represent a powerful new paradigm for defining such relationships; however, differences in model structure and calibration data mean that no consensus yet exists on the optimal form for use in disease-burden estimation. Here we develop a Bayesian statistical procedure combining functional regression-based model emulation with Markov Chain Monte Carlo sampling to calibrate three selected microsimulation models against a purpose-built data set of age-structured prevalence and incidence counts. This allows the generation of ensemble forecasts of the prevalence–incidence relationship stratified by age, transmission seasonality, treatment level and exposure history, from which we predict accelerating returns on investments in large-scale intervention campaigns as transmission and prevalence are progressively reduced. Mathematical models are used to predict malaria burden to inform disease control efforts. Here, Cameron et al. use Bayesian statistics to calibrate previous models against a data set of age-structured prevalence and incidence, generating stratified forecasts of the prevalence–incidence relationship.