Estimating the location of individual livestock holdings and their populations in two developing countries for use in spatial disease spread models

Infectious diseases of food animals, such as Foot-and-Mouth Disease (FMD), pose severe impacts on animal trade, animal products and subsequently endanger global food security. FMD is endemic in many parts of the world and is associated with substantial economic losses, which require risk assessments, preparedness planning, and evaluation of the effectiveness of mitigation strategies that fit within a country’s sociopolitical and socioeconomic constraints. Spatially-explicit stochastic simulation models (SESS) have become a common tool for estimating the spread and impact of FMD. SESS models incorporate uncertainty in the input and output parameters, heterogeneity in disease processes, and integrate geographic locations and spatial proximity of holdings that affect their relative exposure and transmission risk. An essential input to these models is locational data for holdings of animals and associated animal populations in each holding.

Several efforts have been made to predict the location and population of livestock holdings or population density at different spatial resolutions. These methods or data cannot be used in developing countries because either the data is too coarse, or the inputs required for the methods are not available in resource-limited countries. As such, there is a need to adapt the practical and reliable existing methods to generate simulated datasets depicting the location and population of individual livestock holdings in developing countries for use in SESS models.

We generated spatially-resolved simulated datasets for the location and population density of individual livestock holdings in Pakistan and Thailand. Firstly, we microsimulated and downscaled the census data to individual holdings based on statistical distributions. Second, geospatial probability surfaces were created based on a survey of expert veterinarians and empirical holding locations. Third, holdings were randomly placed on the probability surface based on a set of rules. These holdings were assigned population of livestock by joining downscaled data and random holdings. The combined dataset on the location and population of individual livestock holdings was, finally, used to generate the density of holdings.

To our knowledge, this was the first attempt to estimate the locations and populations of individual livestock holdings in developing countries. These data pave the way for the application of SESS models in developing countries to understand the spread of FMD and evaluate mitigation strategies. The control of such an important animal disease would improve livestock health, improve economic gains for producers, and help alleviate poverty and hunger, which will complement efforts to attain the 2030 Sustainable Development Goals.