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

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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.