Developing New Statistical Methods for Vector-Borne Disease Surveillance to Improve Accuracy while Reducing Cost

项目摘要/摘要 媒介传播疾病监测需要监测受感染媒介的地理分布，是一种 成本高昂的过程，通常涉及在现场收集病媒，然后测试它们的存在 各种各样的病原体。对于财政紧张的公共卫生实验室和地方政府来说，这些成本往往令人望而却步 因此，人们对许多媒介传播疾病的地理分布知之甚少。这 对于新出现的疾病和那些地理范围不断扩大的疾病来说尤其如此。坚定地理解 感染各种病原体的病媒的空间分布对使用 潜在的暴露，以指导诊断和治疗决策。为了提供这些评估，该项目将 制定多种具有成本效益的病媒传播疾病监测战略，包括主动和被动 战略。主动策略通过利用池测试技术来降低成本；即，而不是测试向量 一个接一个地，多个载体被物理地合并以形成一个集合的样本，该样本被测试 目标病原体。这些技术有可能极大地降低测试成本，特别是对于 监视努力，但这样做的代价是复杂得多的数据结构。要克服这一点 复杂，我们将开发一套新的空间和时空回归模型，可用于 为了分析池测试数据，最终目标是能够更好地了解地理分布和 各种媒介传播疾病的扩大。被动战略将利用现有的信息，以及 通过这项提议收集的数据，以开发媒介活动水平的“即时预测”。这些即兴转播将 吸收气候和栖息地适宜性、天气模式和其他关键环境因素进行预测 因此，可以在没有任何相关成本的情况下实时更新。这些即时播报旨在 补充积极的监测工作和实地收集，特别是在没有大规模收集的地区 可行。为了验证我们的监测战略，我们计划进行一项雄心勃勃的研究，旨在收集和 斑点热立克次体沿斑点亚种(南部)不断扩大的地理范围检测蜱类 Carolina)和A.americanum(中西部)。这项研究的数据将用于验证和告知 设计我们的主动和被动监视战略。总而言之，我们的建议旨在转变 通过降低成本、提高准确性和量化风险实现病媒传播疾病监测的范例 实时，同时阐明斑点热群立克次体感染的时空模式 物种。

Project Summary/Abstract Vector-borne disease surveillance requires monitoring the geographic distribution of infected vectors and is a costly process that typically involves collecting vectors in the field and subsequently testing them for the presence of various pathogens. These costs are often prohibitive to financially strained public health labs and local municipalities, thus leaving the geographic distribution of many vector-borne diseases poorly understood. This is especially true for emerging diseases and those with expanding geographical ranges. Firmly understanding the spatial distribution of vectors infected with various pathogens is critical for healthcare providers who are using potential exposure to guide diagnostic and treatment decisions. To provide these assessments, this project will develop multiple cost-efficient vector-borne disease surveillance strategies, including both active and passive strategies. The active strategy reduces cost by leveraging pool testing techniques; i.e., rather than testing vectors one-by-one, multiple vectors are physically amalgamated to form a pooled specimen which is tested for the pathogen of interest. These techniques have the potential to drastically reduce testing cost, especially for surveillance efforts, but do so at the expense of a far more complicated data structure. To overcome this complication, we will develop a novel suite of spatial and spatio-temporal regression models which can be used to analyze pool testing data with an end goal of being able to better understand the geographic distribution and expansion of various vector-borne diseases. The passive strategies will leverage existing information, as well as data collected through this proposal, to develop ‘nowcasts’ of vector activity levels. These nowcasts will assimilate climate and habitat suitability, weather patterns and other key environmental factors to forecast activity, and therefore can be updated in real time without any associated cost. These nowcasts are intended to supplement active surveillance efforts and field collection, especially in areas where large scale collection is not feasible. To validate our surveillance strategies, we plan to undertake an ambitious study aimed at collecting and testing ticks for spotted fever group Rickettsia along the expanding geographic range of A. maculatum (South Carolina) and A. americanum (the Midwest). The data from this study will be used to validate and inform the design of both our active and passive surveillance strategies. In summary, our proposal seeks to transform the paradigm of vector-borne disease surveillance by reducing costs, improving accuracy, and quantifying risk in real time, while elucidating the spatio-temporal patterns vector infection by spotted fever group Rickettsia species.