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

开发新的媒介传播疾病监测统计方法，以提高准确性并降低成本

• 批准号: 10774013
• 负责人: Stella Coker Watson Self
• 金额: $ 75.86万
• 依托单位: UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-19 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10774013
• 关键词: Amblyomma; Ambylomma americanum; Annual Reports; Area; Arizona; Bacteria; Biocompatible Materials; Case Fatality Rates; Case Study; Centers for Disease Control and Prevention (U.S.); Climate; Collection; Complication; Data; Delaware; Dermacentor; Diagnosis; Diagnostic; Disease; Disease Surveillance; Doxycycline; Environment; Environmental Risk Factor; Exposure to; Fever; Financial Hardship; Future; Geographic Distribution; Geography; Goals; Guidelines; Habitats; Health Personnel; Illinois; Incidence; Infection; Laboratories; Methods; Midwestern United States; Modeling; Monitor; Municipalities; Nature; Pathogenicity; Patients; Pattern; Population; Predisposition; Procedures; Process; Public Health; Reporting; Reproduction; Rhipicephalus sanguineus; Rickettsia; Rickettsia Infections; Rickettsia parkeri; Rickettsia rickettsii; Risk; Rocky Mountain Spotted Fever; South Carolina; Spatial Distribution; Specimen; Statistical Methods; Statistical Models; Surveillance Methods; Techniques; Testing; Tick-Borne Diseases; Ticks; Time; United States; Update; Vector-transmitted infectious disease; Weather; accurate diagnosis; cost; cost effective; cost efficient; data streams; data structure; design; disorder risk; empowerment; gulf coast; improved; interest; land cover; machine learning method; novel; pathogen; spatiotemporal; spotted fever; surveillance strategy; tool; trend; vector; vector tick; vector-borne infection; weather patterns

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.

项目总结/摘要 病媒传播疾病监测需要监测受感染病媒的地理分布， 通常涉及在现场收集载体并随后测试它们是否存在 各种病原体。这些费用通常使财政紧张的公共卫生实验室和当地卫生机构望而却步。 这使得人们对许多病媒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 被各种病原体感染的媒介的空间分布对于使用 潜在的接触，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，包括主动和被动监测战略 战略布局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，多个载体被物理合并以形成合并的样本，该样本被测试用于 感兴趣的病原体这些技术有可能大大降低测试成本，特别是对于 监控工作，但这样做的代价是一个更复杂的数据结构。为了克服这个 复杂性，我们将开发一套新的空间和时空回归模型，可用于 分析池测试数据，最终目标是能够更好地了解地理分布， 各种病媒传播疾病的蔓延。被动策略将利用现有信息，以及 通过这一提议收集的数据，以编制病媒活动水平的“即时预报”。这些即时预报将 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 活动，因此可以在真实的时间内更新，而无需任何相关成本。这些即时预报旨在 补充积极的监测工作和实地收集，特别是在大规模收集工作无法进行的地区。 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 沿着沿着A.斑（南 卡罗莱纳）和A.美国（中西部）。本研究的数据将用于验证和告知 主动和被动监视策略的设计。总而言之，我们的建议旨在改变 通过降低成本、提高准确性和量化风险， 真实的时间，同时阐明了斑点热群立克次体媒介感染的时空模式 物种