Mapping the prevalence of household-scale livestock ownership by animal taxon in low- and middle-income countries

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.