Mapping the prevalence of household-scale livestock ownership by animal taxon in low- and middle-income countries: A prediction model using template model builder

Abstract

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 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, though such predictions in data-sparse regions (particularly Muslim-majority areas) represent regional covariate patterns rather than fine-scale measurements. To address non-stationarity in swine spatial structure, region-specific spatial kernels were implemented. 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.