Improving distribution models of sparsely-documented disease vectors by incorporating information on related species via joint modeling

A necessary component of understanding vector-borne disease risk is the accurate characterization of the distributions of their vectors. Species distribution models have been successfully applied to data-rich species but may produce inaccurate results for sparsely-documented vectors. In light of global change, vectors that are currently not well-documented could become increasingly important, requiring tools to predict their distributions. One way to achieve this could be to leverage data on related species to inform the distribution of a sparsely-documented vector based on the assumption that the environmental niches of related species are not independent. Relatedly, there is a natural dependence of the spatial distribution of a disease on the spatial dependence of its vector. Here, we propose to exploit these correlations by fitting a hierarchical model jointly to data on multiple vector species and their associated human diseases to improve distribution models of sparsely-documented ..., Vector Data
Vector presence data were obtained from VectorMap and iNaturalist. Only iNaturalist data considered “research grade” were included, and we removed duplicates. To obtain absence data, we referenced VectorMap publications and assumed that if a species was not reported at a sampling location, but was included within the study, that the species was absent at that location. To avoid conflating low sampling effort with low vector presence, we based pseudo-absence locations on presence locations from chiggers, fleas, and mites from both databases and the Global Biodiversity Information Facility. We used a 1:1 ratio of presence to absence points, which produces the most accurate predicted distribution for regression techniques (Barbet-Massin et al., 2012).
We artificially sparsely sampled one species within our empirical data (A. maculatum) by including 20% of available presence-absence data in our training set and withholding the rest for testing. The artificial sparse sampling all..., , # Improving distribution models of sparsely-documented disease vectors by incorporating information on related species via joint modeling

[https://doi.org/10.5061/dryad.jwstqjqhq](https://doi.org/10.5061/dryad.jwstqjqhq)

## Description of the data and file structure

* vector_data.csv: presence/absence locations of vectors with corresponding scaled environmental covariates (please see methods for sources for covariate data).
* FL_case_data.xlsx: Human disease data. Sheets 1-3 include annual county-level count data for each disease. Sheets 4 -6 include county-level presence data for each disease. The order of counties and years reported for each disease are given in the \"meta\" sheet. 
* distance_to_pathogen_fl.xlsx: includes data on the number of cases reported in pets within each county over the study period, and whether the binary classification for circulation. d_circ is the distance to the closest county in which the disease is circulating (based on the county midpoint). 
* ...