Abstract
The distribution of farm locations and sizes is paramount to characterize disease spread patterns. With some regions undergoing rapid intensification of livestock production, resulting in increased clustering of farms in peri-urban areas, measuring changes in the spatial distribution of farms is crucial to design effective interventions. However, those data are not available in many countries, their generation being resource consuming.
Here, we develop a farm distribution model (FDM), which allows predicting locations and sizes of poultry farms in countries with scarce data. It combines (i) a Log-Gaussian Cox process model (LGCP) simulating the farm distribution as a spatial Poisson point process with logarithm varying intensity, conserving the level of clustering of spatial points patterns, and (ii) a random forest (RF) model simulating farm sizes (i.e. the number of animals per farm). Spatial predictors were used to calibrate the FDM on intensive broiler and layer farm distributions in Bangladesh, Gujarat (Indian province) and Thailand.
The LGCP and RF models yielded realistic farm distributions in terms of spatial clustering, farm locations and sizes, while providing insights on spatial analysis of the poultry production systems and spatial clustering drivers. Finally, we illustrate the relevance of modelling realistic farm distributions in the context of epidemic spread by simulating pathogen transmission on an array of spatial distributions of farms. We found that farm distributions generated from the FDM yielded spreading patterns consistent with simulations using observed data, while random point patterns underestimated vulnerability to epidemics. Indeed, spatial clustering increases vulnerability to epidemics, highlighting the relevance of spatial clustering and farm sizes to study epidemic spread.
As the FDM maintains a realistic distribution of farms and their size, its use to inform mathematical models of disease transmission is very relevant for regions where these data are not available.