A wide variety of terrestrial plant and animal pathogens have evolved transmission cycles that require vectors, typically arthropods, that transmit the pathogen when it feeds on a host. These vector-borne diseases (VBDs) represent a serious threat to human, animal, and plant health as well as negatively impacting economic welfare worldwide. For example, approximately a third of the human population is at risk for infections transmitted by mosquitoes alone, and vectors transmit many important diseases of plants and livestock. Yearly, VBDs account for 17% of human infectious diseases and billions of dollars in crop and livestock losses. In order to better prevent and predict outbreaks of VBDs, many types of information and data on interactions of vectors with their environments over space and time need to be combined. However, efforts to do this have been hindered by data collected on vectors being isolated, difficult to access, and kept in disparate formats. The main goal of this project is to build a centralized open access data platform called VectorByte. It will contain standardized data on vector traits and population abundance. This will allow data to be more easily shared and used by the disease ecology community and by other interested communities. Further, freely available tools to analyze and model these data will be developed, combined with educational materials, including tutorials on using the databases and data analysis tools. Training early career scientists will be accomplished through workshops and mentoring of postdoctoral researchers, graduate students, and undergraduates within the project. Training workshops covering use of the databases and statistical methods appropriate for the data will target early career scientists from underrepresented groups and regions, as well as practitioners from the broader public health and vector control community. This combined audience will enable feedback from the applied realm about best user practice and will promote collaborative opportunities to bridge between tools developed within the academic community and real world decisions. The platform and training will in turn support research and mathematical modelling efforts that will lead to a better understanding of why outbreaks occur when and where they do and will allow for development and assessment of potential control strategies for these diseases.
There is mounting empirical evidence that the traits of vectors vary across time, environmental conditions, and within and between populations. This variation has knock-on effects for the dynamics of vector populations, and therefore also for transmission of vector-borne infections and the efficacy of control strategies. Mathematical and statistical models can be used to better understand the links between traits, populations, and transmission. However, doing this well requires detailed data ranging from laboratory measurements of individual-level traits of vectors to observed population dynamics of the vectors, all of which are often difficult to obtain or use. Further, data for VBD systems are often archived in inconsistent formats and locations. The VectorByte project will develop a user-friendly informatics platform with a global scope for depositing, accessing, and visualizing data in order to fill these gaps for the VBD community. The VectorByte platform has the potential to transform VBD disease research by providing Findable, Accessible, Interoperable, and Reusable (FAIR) data, necessary to build, test, and validate models of VBDs not currently possible with available open data. This project will enable VBD researchers to increase the impact of their data through standardized formatting and centralized location, increasing the sustainability of data while simultaneously increasing the potential for reuse. These data standards will also facilitate comparison of VBD systems and the construction of open and testable models of VBD dynamics.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
