Representation of animal distributions in space: how geostatistical estimates impact simulation modeling of foot-and-mouth disease spread

Abstract

-Modeling potential disease spread in wildlife populations is important for predicting, responding to and recovering from a foreign animal disease incursion. To make spatial epidemic predictions, the target animal species of interest must first be represented in space. We conducted a series of simulation experiments to determine how estimates of the spatial distribution of white-tailed deer impact the predicted magnitude and distribution of foot-and-mouth disease (FMD) outbreaks. Outbreaks were simulated using a susceptible-infected-recovered geographic automata model. The study region was a 9-county area (24 000 km 2 ) of southern Texas. Methods used for creating deer distributions included dasymetric mapping, kriging and remotely sensed image analysis. The magnitudes and distributions of the predicted outbreaks were evaluated by comparing the median number of deer infected and median area affected (km 2 ), respectively. The methods were further evaluated for similar predictive power by comparing the model predicted outputs with unweighted pair group method with arithmetic mean (UPGMA) clustering. There were significant differences in the estimated number of deer in the study region, based on the geostatistical estimation procedure used (range: 385 939-768 493). There were also substantial differences in the predicted magnitude of the FMD outbreaks (range: 1 563-8 896) and land area affected (range: 56-447 km 2 ) for the different estimated animal distributions. UPGMA clustering indicated there were two main groups of distributions, and one outlier. We recommend that one distribution from each of these two groups be used to model the range of possible outbreaks. Methods included in cluster 1 (such as county-level disaggregation) could be used in conjunction with any of the methods in cluster 2, which included kriging, NDVI split by ecoregion, or disaggregation at the regional level, to represent the variability in the model predicted outbreak distributions. How animal populations are represented needs to be considered in all spatial disease spread models.spatial modeling / epidemic modeling / deer density / sensitivity analysis

An approach for de-identification of point locations of livestock premises for further use in disease spread modeling

“…Confident use of model outputs to inform decision-making is dependent upon the quality of model inputs, including animal population data. The spatial scales and distributions of animal populations used as model inputs to simulate disease transmission and control can influence model outputs such as those describing disease dynamics, outbreak severity and extent (Highfield et al, 2008;Tildesley et al, 2010;Carpenter, 2011;Reeves, 2012). The true location of each livestock premises is the ideal data source.…”

Geography and computational social science

“…The relationship between mobility, accessibility to the built environment, and obesity is a significant point of inquiry in public health research (Hill et al 2003). The spatial mechanics of diffusion and spatial interaction are opening new avenues of inquiry in epidemiology research of human (Ferguson 2007) and animal populations (Highfield et al 2008). Examination of human activity patterns and their relationships to time geography is of great currency in transportation research (Kwan et al 2003).…”

A mobility model for classical swine fever in feral pig populations

“…Wild or feral animal populations may act as disease reservoirs which may continuously spill over to the domestic animal population, making control and eradication significantly more difficult than if the disease were to reside solely in the domestic sub-population [29]. For example, a foot-and-mouth outbreak in deer in California in 1924 required extensive efforts over a two year period to eradicate the disease in a single national park [17,24]. It is thus pertinent to develop simulation models which allow for an examination of the mechanisms underlying the potential for disease transmission between domestic and wild animals.…”

“…However, animal population, distribution and movement data on wild or feral animal populations is scarce. Recent work [7,17,33] uses spatial models to estimate transmission of foot-and-mouth disease among feral pig and deer populations, in Queensland, Australia and Texas, USA. These researchers utilise a cellular automata technique, dividing the landscape into a discrete grid of cells [12].…”

“…These models are used to examine the effects of various containment or eradication strategies, namely culling, vaccination and movement bans. Spatial simulation models based on cellular automata have also been developed for wild [7,17,33] and domestic [12] animal populations.…”