Widespread application of insecticide remains the primary form of control for Chagas disease in Central America, despite only temporarily reducing domestic levels of the endemic vector Triatoma dimidiata and having little long-term impact. Recently, an approach emphasizing community feedback and housing improvements has been shown to yield lasting results. However, the additional resources and personnel required by such an intervention likely hinders its widespread adoption. One solution to this problem would be to target only a subset of houses in a community while still eliminating enough infestations to interrupt disease transfer. Here we develop a sequential sampling framework that adapts to information specific to a community as more houses are visited, thereby allowing us to efficiently find homes with domiciliary vectors while minimizing sampling bias. The method fits Bayesian geostatistical models to make spatially informed predictions, while gradually transitioning from prioritizing houses based on prediction uncertainty to targeting houses with a high risk of infestation. A key feature of the method is the use of a single exploration parameter, $\alpha$, to control the rate of transition between these two design targets. In a simulation study using empirical data from five villages in southeastern Guatemala, we test our method using a range of values for $\alpha$, and find it can consistently select fewer homes than random sampling, while still bringing the village infestation rate below a given threshold. We further find that when additional socioeconomic information is available, much larger savings are possible, but that meeting the target infestation rate is less consistent, particularly among the less exploratory strategies. Our results suggest new options for implementing long-term T. dimidiata control.
翻译:杀虫剂的广泛应用仍然是中美洲南美锥虫病的主要控制形式,尽管只是暂时降低了地方病病媒Triatoma dimidiata的国内水平,而且几乎没有长期影响。最近,强调社区反馈和住房改善的方法已证明能够产生持久的结果。然而,这种干预措施所需的额外资源和人员可能妨碍其广泛采用。解决这个问题的一个解决办法是只针对社区中的一小部分房屋,同时仍然消除足够的病虫害以阻止疾病转移。我们在这里制定了一个顺序抽样框架,随着更多房屋的参观,能够适应社区特有的信息,从而使我们能够高效率地找到有寄生虫的病媒的家庭,同时尽量减少抽样偏差。这一方法符合Bayesian地理统计学模型,以便作出空间知情的预测,同时逐渐从预测不确定的房屋优先转向高危房屋。这种方法的一个主要特征是使用单一勘探参数,即$alpha,以控制这两个设计目标之间的过渡速度。在进行模拟研究时,利用5个村庄中的实验数据,特别是降低采样偏差率,我们用一个连续的村级标准进行测试,而我们又能够更慢地测量一个方法。我们发现,在较慢的村庄中,在选择的取样率中,我们可以找到更多的选择。