DOI: 10.5061/dryad.jwstqjqhq
title: Improving distribution models of sparsely-documented disease vectors by incorporating information on related species via joint modeling

description: 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
 species. To demonstrate this approach, we evaluated the ability of twelve
 models—which differed in their pooling of data from multiple vector
 species and inclusion of disease data—to improve distribution estimates of
 sparsely-documented vectors. We assessed our models on two simulated data
 sets, which allowed us to generalize our results and examine their
 mechanisms. We found that when the focal species is sparsely documented,
 incorporating data on related vector species reduces uncertainty and
 improves accuracy by reducing overfitting. When data on vector species are
 already incorporated, disease data only marginally improve model
 performance.  However, when data on other vectors are not
 available, disease data can improve model accuracy and reduce overfitting
 and uncertainty. We then assessed the approach on empirical data on ticks
 and tick-borne diseases in Florida and found that incorporating data on
 other vector species improved model performance. This study illustrates
 the value of exploiting correlated data via joint modeling to improve
 distribution models of data-limited species.
description: <em>Vector Data</em> 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
 (<em>A. maculatum</em>) by including 20% of available
 presence-absence data in our training set and withholding the rest for
 testing. The artificial sparse sampling allowed for a robust testing data
 set to evaluate model performances. To ensure spatial independence between
 our training and testing data, data were split using
 the <em>blockCV</em> package (Valavi et al., 2018) in R
 Version 2023.03.0+386 (R Core Team, 2023). To test the limitations of
 incorporating disease data, we selected a vector species that does not
 transmit any of the diseases within our model as our focal species.
 Empirical sample sizes are given in Supp Table 2. 
 <em>Human Disease Data</em> We
 obtained annual incidence data on three human diseases (anaplasmosis,
 ehrlichiosis, Lyme disease) from 2011 to 2019 for each county from the
 Florida Department of Health. We translated this into human disease
 presence data in a given county in a given year based on whether the
 annual incidence there was greater than zero.
 <em>Covariate
 data</em><strong><br></strong>
 We modeled vector distributions as a function of environmental
 covariates, which have been linked to tick presence: land cover (Randolph,
 2000), 30-year average maximum temperature (Ogden et al., 2020), 30-year
 average precipitation (Ogden et al., 2020), regional Palmer hydrological
 drought index (Jones and Kitron, 2000), normalized differential vegetation
 index (Randolph, 2000), and distance to the nearest waterbody (Kahl and
 Alidousti, 1997). We obtained landcover data from Global Land Cover
 Characteristics Database (Loveland et al., 2000), 30-year average climate
 data from WorldClim (Fick and Hijmans, 2017), Palmer Hydrological Drought
 Index from NOAA (Bushra and Rohli, 2017), and Normalized Difference
 Vegetation Index data from USGS Landsat (Vermote et al., 2016). Finally,
 we obtained waterbody data from the World Wildlife Foundation’s Global
 Lakes and Wetlands database (McGwire and Fisher, 2001).  Pathogen
 circulation was based on Companion Animal Parasitic Council data, which
 reports the seroprevalence in canines receiving veterinary treatment. To
 avoid considering imported cases as indicative of endemicity, we
 considered a threshold of five annual cases to signal transmission.
 Finally, to account for under-reporting (Madison-Antenucci, et al., 2020),
 we modeled reporting probability as a function of health insurance
 coverage and population size. Insurance data were obtained from County
 Health Rankings (www.countyhealthrankings.org), and population data were
 obtained from WorldPop (www.worldpop.org).
 <em>Simulated data</em> Our first
 simulation simulates data for three well-documented species (<em>A.
 americanum, A. maculatum, D. variabilis</em>) and a single
 sparsely-documented species (<em>I. scapularis</em>).
 “Well-documented” is defined as 500 samples and “sparsely-documented” is
 defined as 30 samples (Supp Figure 2). Our second simulation simulates all
 four species as well-documented (Supp Table 3).
description: # 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).  * county_pops.csv: county population sizes * fl_percent_insured.csv: county insurance coverage ## Sharing/Access information Data and code for analysis are available on GitHub (link in Related Works). Data was derived from the following sources:  * [http://vectormap.si.edu](http://vectormap.si.edu) * [https://www.inaturalist.org](https://www.inaturalist.org) * [https://www.floridahealth.gov/diseases-and-conditions/tick-and-insect-borne-diseases/tick-surveillance.html](https://www.floridahealth.gov/diseases-and-conditions/tick-and-insect-borne-diseases/tick-surveillance.html) * [https://capcvet.org/maps#/](https://capcvet.org/maps#/) * [www.countyhealthrankings.org](http://www.countyhealthrankings.org) * [www.worldpop.org](http://www.worldpop.org) ## Code/Software R, RStan