DOI: 10.5061/dryad.n2z34tnb3
title: Mapping the prevalence of household-scale livestock ownership by animal taxon in low- and middle-income countries

description: Animal husbandry is widely practiced on the household scale in communities
 in low- and middle-income countries (LMICs) and, while having economic and
 health benefits, exposes household members to risk of zoonotic infections
 to an extent that is unclear. While demand for georeferenced information
 on infectious disease risk factors and drivers is growing, spatial
 variation in livestock ownership remains poorly characterized at high
 resolution. This study aimed to use geostatistical methods to model and
 map the prevalence of livestock husbandry in LMICs for three major animal
 taxa: poultry, swine, and ruminants. Microdata relating to ownership of
 livestock animal species were sourced from various population-based survey
 programs, which together cover the majority of LMICs, and categorized.
 These were georeferenced and spatially matched with a panel of time-fixed
 environmental and demographic spatial covariates. INLA models were fitted
 to the resulting database, and probabilities for ownership of each
 livestock taxon were predicted based on the model parameter estimates. The
 results indicated widespread poultry ownership across rural Central
 America, the Amazon basin, tropical Africa, and river basins and forests
 of East Asia. Swine husbandry is the least widely practiced among the
 three livestock taxa and concentrated in an undulating belt of higher
 prevalence extending from central China, through southeast Asia to
 Northeastern India. Rearing of ruminant livestock appears widespread
 across subequatorial Africa, Central Asia, the Gobi Desert, the Himalayas,
 Mongolia, and northern India. The models perform impressively by most
 standard evaluation metrics, and the patterns in their predictions align
 with external evidence. The distribution of this important risk factor for
 infectious disease transmission can be modeled using publicly available
 data sources to generate plausible and potentially actionable predictions
 over wide geographic areas and identify regions of high exposure to animal
 disease reservoirs. The resulting predicted prevalence estimates are made
 available as supplementary files in GIS-compatible format.
description: # Mapping the prevalence of household-scale livestock ownership by animal
 taxon in low- and middle-income countries Dataset DOI:
 [10.5061/dryad.n2z34tnb3](https://doi.org/10.5061/dryad.n2z34tnb3) ##
 Summary of analysis underlying this dataset Full background, methods,
 results, and discussion can be found in this preprint article:
 [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5490726](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5490726) ## Description of file structure and contents The resulting model outputs are made available in the DRYAD repository as 6 GeoTIFF files. For each of the three livestock taxon outcomes - poultry, swine, and ruminants - there is a file containing the predicted prevalence values and a second containing the standard error of those predictions at all locations in eligible countries at a 5-decimal-degree resolution. Each GeoTIFF contains a single band corresponding to the variable value. ## Definitions of variables and units: The definitions of the variables in each file are as follows: * Livestock_ownership_supplementary_file_3a_Poultry.tif - The predicted percentage prevalence of poultry livestock ownership. * Livestock_ownership_supplementary_file_3b_Poultry_SE.tif - The standard error of the predicted percentage prevalence of poultry livestock ownership. * Livestock_ownership_supplementary_file_3c_Swine.tif - The predicted percentage prevalence of swine livestock ownership. * Livestock_ownership_supplementary_file_3d_Swine_SE.tif - The standard error of the predicted percentage prevalence of swine livestock ownership. * Livestock_ownership_supplementary_file_3e_Ruminants.tif - The predicted percentage prevalence of ruminant livestock ownership. * Livestock_ownership_supplementary_file_3f_Ruminants_SE.tif - The standard error of the predicted percentage prevalence of ruminant livestock ownership. Predictions and standard errors are for all LMICs outside of Europe and are derived from integrated nested Laplace approximation (INLA) models fitted to household survey data. The units are percentages ranging from 0 - 100%. ## Code/software The files can be imported to and analyzed in ArcGIS Pro, QGIS, or any other standard GIS software. ### Other sources from which the data were derived The input data is publicly available, third-party-owned data sourced from the following household survey programs: | Source | URL | | :-------------------------------- | :--------------------------------------------------------------------------------------------------------------------- | | Demographic and Health Survey | [https://dhsprogram.com/](https://dhsprogram.com/) | | Malaria Indicator Survey | [https://dhsprogram.com/](https://dhsprogram.com/) | | AIDS Indicator Survey | [https://dhsprogram.com/](https://dhsprogram.com/) | | Multiple Indicator Cluster Survey | [https://mics.unicef.org/surveys](https://mics.unicef.org/surveys) | | China Family Panel Studies | [https://www.isss.pku.edu.cn/cfps/en/data/public/index.htm](https://www.isss.pku.edu.cn/cfps/en/data/public/index.htm) | ## Human subjects data The human subject data used in this analysis were collected and owned by third parties, not by the authors. They are all publicly available and can be accessed from the following sources in the same manner that the authors accessed them: - Demographic and Health Surveys, Malaria Indicator Surveys, and AIDS Indicator Surveys - https://dhsprogram.com/ - Multiple Indicator Cluster Surveys - https://mics.unicef.org/surveys - Botswana Family Health Survey - https://microdata.statsbots.org.bw/index.php/catalog/9 - Encuesta Nicaragüense de Demografía y Salud - https://www.inide.gob.ni/Home/endesa - Encuesta Nacional de Salud y Nutrición - https://www.salud.gob.ec/encuesta-nacional-de-salud-y-nutricion-ensanut/ - Pesquisa Nacional de Demografia e Saúde da Criança e da Mulher - https://bvsms.saude.gov.br/bvs/pnds/ The data on the covariate predictors are available from the sources cited in Table 2. The authors do not have the right to share these datasets, but did not have any special access privileges that others would not have.