S02 Mapping global coldspots of veterinary capacity

Abstract

Maps have been instrumental in prioritizing interventions against infectious diseases of global importance (Reich & Haran, 2018). In human medicine, high-resolution maps have helped to quantify the burden of diseases such as malaria and dengue. Similarly, in animal health, mapping efforts have focused on diseases such as Avian Influenza, Rift Valley Fever, or antimicrobial resistance, which pose threats to the livelihoods of those who raise animals for subsistence.

However, mapping the accessibility of veterinary care has received relatively little attention thus far. Veterinarians are at the forefront of defense against animal and zoonotic diseases (Bellemain, 2013). Yet, efforts to map the distribution of veterinarians have primarily concentrated on regional or provincial levels within a limited number of countries. Although crucially important, these efforts can overshadow important heterogeneities in access to veterinary care in countries currently transitioning from subsistence farming to commercial farming, which have also been suggested to be the most vulnerable to disease emergence.

In the past decade, online platforms enabled users to locate veterinarians based on postal codes or addresses (Royal College of Veterinary Surgeons, 2023). The purpose of these platforms is to match patients with veterinarians working in their vicinity. Whilst not primarily intended for public health, these platforms are an unprecedented opportunity to get insights into the fine-scale distribution of veterinarians, the factors driving that distribution, and supplement existing national-level data sources on veterinary capacity.

In this study, we predicted and mapped the global distribution of veterinarians at the 10x10 km² resolution using a global address book of veterinarians assembled from online sources. Specifically, we used web-scraping techniques and queried online web maps to collect the locations of 303,745 veterinarians across 115 countries. These locations were modeled through a Log-Gaussian Poisson Regression model implemented in a Bayesian framework provided by the Integrated Nested Laplace Approximation (INLA). By comparing the distribution map of veterinarians with density maps of animals raised for food we mapped regions where animals are more than 1 hour away from veterinarians. The 91.3% of these regions, referred to as “veterinary coldspots”, were found in low- and middle-income countries. Finally, we furtherly identified, for 103 countries, the geographic locations where veterinary services should be scaled up to maximize access to care for animals raised for food.