Location: Research Programs
Project Number: 3022-32000-018-009-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Aug 15, 2022
End Date: Aug 14, 2025
Objective:
The overall objective of this collaborative agreement is to develop the necessary knowledge and collect the data necessary to develop an Early Warning System for VSV that could serve as a model for development of early warning systems for other emergent vector-borne pathogens.
Approach:
1. Through our collaborations with Mexico's animal health program (CPA and SENASICA), we will acquire VS case data for the entirety of Mexico during the three-year project period. SENASICA has already shared historical case data for the country spanning the past three decades, providing an unparalleled longitudinal dataset for analysis.
2. We will establish two focal field sites within Mexico, likely in the states of Jalisco and Chihuahua, that will complement our existing field sites in Chiapas in southern New Mexico. At these sites, we will conduct serosurveillance of target groups of cattle across the three years of the study to generate age-stratified seroconversion data. Additionally, at each site we will conduct twice-yearly vector collections.
3. Our collaborators will screen serum samples and vector samples for VSV using a qPCR assay that we have been using for this purpose. Positive samples will be shipped to the USDA for viral isolation, sequencing, and phylogenetic analysis.
4. We will download a suite of environmental data that captures climate, land use and land cover, and domestic animal density across the endemic and outbreak region over the 2 years preceding our study as well as the study period, with particular effort devoted to acquiring fine-grained data at our focal field sites.
5. We will conduct an exploratory analysis to assess which environmental data most closely associate with distribution and abundance of key vector taxa, in the hopes that one or more of these variables may serve as an adequate proxy for vector surveillance.
6. We will utilize a subset of these data (training dataset) to generate a predictive model for VSV emergence, and validate this model against the remaining data (testing dataset).
