Predicting spatial spread of rabies in skunk populations using surveillance data reported by the public

Abstract

Background: Prevention and control of wildlife disease invasions relies on the ability to predict spatio-temporal dynamics and understand the role of factors driving spread rates, such as seasonality and transmission distance. Passive disease surveillance (i.e., case reports by public) is a common method of monitoring emergence of wildlife diseases, but can be challenging to interpret due to spatial biases and limitations in data quantity and quality.

Methodology/principal findings: We obtained passive rabies surveillance data from dead striped skunks (Mephitis mephitis) in an epizootic in northern Colorado, USA. We developed a dynamic patch-occupancy model which predicts spatio-temporal spreading while accounting for heterogeneous sampling. We estimated the distance travelled per transmission event, direction of invasion, rate of spatial spread, and effects of infection density and season. We also estimated mean transmission distance and rates of spatial spread using a phylogeographic approach on a subsample of viral sequences from the same epizootic. Both the occupancy and phylogeographic approaches predicted similar rates of spatio-temporal spread. Estimated mean transmission distances were 2.3 km (95% Highest Posterior Density (HPD95): 0.02, 11.9; phylogeographic) and 3.9 km (95% credible intervals (CI95): 1.4, 11.3; occupancy). Estimated rates of spatial spread in km/year were: 29.8 (HPD95: 20.8, 39.8; phylogeographic, branch velocity, homogenous model), 22.6 (HPD95: 15.3, 29.7; phylogeographic, diffusion rate, homogenous model) and 21.1 (CI95: 16.7, 25.5; occupancy). Initial colonization probability was twice as high in spring relative to fall.

Conclusions/significance: Skunk-to-skunk transmission was primarily local (< 4 km) suggesting that if interventions were needed, they could be applied at the wave front. Slower viral invasions of skunk rabies in western USA compared to a similar epizootic in raccoons in the eastern USA implies host species or landscape factors underlie the dynamics of rabies invasions. Our framework provides a straightforward method for estimating rates of spatial spread of wildlife diseases.

Fig 1. Map of study area.

Each dot represents the location where a dead skunk was reported and subsequently tested for rabies. Grid cells show the scale at which the study area was gridded (8 x 8 km sites, i = 1, …, M).

Fig 2. Effects of covariates on initial colonization probability.

Predicted γ as a function of different factors using Model 16 Transmission distance = 3.9 km 95% CI (1.4, 11.3) (neighborhood+distance+season, Table 1). A) Decay of initial colonization probability with distance to nearest infected grid cell in km. B) Proportion of local neighborhood (“queen’s neighbors”) infected. C) Season factor with two levels. Median values of initial colonization probability are indicated by the horizontal black line. D) Relative contribution of different effects (see section SM5 in S1 Text for calculations). Shading in A and B indicates 95% credible intervals.

Fig 3. Phylogeographic inference of viral spatial dynamics.

A) Spatio-temporal projection of the maximum clade credibility tree from the phylogeographic analysis. Transparent polygons indicate the 80% HPD of infected area through time. Grey shading on the left indicates landscape that is above 1829 m. Grey lines indicate major highways. Blue lines indicate all waterways, including very minor and ephemeral ones. Stars indicate the centers of major cities (as in Fig 1). B) Spatial expansion of RABV in skunks, displayed as the distance from the inferred epizootic origin in the phylogeographic analysis. Grey shading indicates the HPD₉₅. The solid black line is the median distance. C) The distance traversed by each branch in the phylogenetic tree divided by the expected number of infections along that branch, assuming a generation time of 30 days. The histogram shows distances calculated from the 144 branches of 500 randomly-selected trees from the posterior distribution of the phylogeographic analysis. Grey bars are the inner HPD₉₅.

Fig 4. Rabies occupancy probability over time.

Top. Occupancy probability for all sites over time (blue line, shading: 95% credible intervals) using Model 11b, Table 1). Grey bars: number of samples collected. Middle. Occupancy probability in space and time. Bottom. Coefficient of variation for occupancy probabilities over space and time. Middle and bottom: Data were aggregated over a 6-month time frame (corresponding to the X-axis labels of the top row plot). Black triangles represent city locations (Fort Collins, Greeley, Boulder and Longmont); sizes scaled to the human population size of the cities. Topographical divide from Fig 1, which indicates land above 1829 m (6000 feet), is indicated by the black line.

Fig 5. Rates of spatial spread.

Model 11b was used to predict spatial spread of rabies. Points are the predicted distance in km of new colonizations from the southern-most grid-cell row. Size of the points corresponds to numbers of new colonizations at a given distance from the southern-most row. The slope (red line) gives a monthly rate of southerly spread (21.1 km/yr). Shaded area gives the 95% confidence intervals of the slope. The 6-month intervals shown on the X-axis correspond to those in Fig 4.