Cluster analysis of hemorrhagic disease in Missouri's white-tailed deer population: 1980-2013

Abstract

Background: Outbreaks of deer hemorrhagic disease (HD) have been documented in the USA for many decades. In the year 2012, there was a severe HD outbreak in Missouri with mortalities reaching approximately 6.9 per thousand. Moreover, Missouri accounted for more than 43% of all reported epizootic HD cases in captive white-tailed deer. Using the data of suspected HD occurrence in Missouri, the primary goal of this paper was to determine if HD in Missouri's white-tailed deer occurs in spatial clusters.

Results: The main results of the cluster analysis are as follows. First, the spatial clusters of years 1980, 1988, 2005-2007, 2010, 2012, and 2013 suggest patterns of outbreaks every 6-8 years, with a potential outbreak in years 2018-2020. Secondly, these spatial clusters were more frequent in the central and southern counties.

Conclusions: The clustering analyses employed in this study have potential applications for improving surveillance programs and designing early warning systems for effective deer population management and potentially reducing the number of HD cases.

Keywords: Cluster analysis; Culicoides midges; Hemorrhagic disease; Missouri; SaTScan; White-tailed deer.

Fig. 1

Frequency (in number of years) of HD cluster occurrence for each county during the study period. The darker the shading, the more frequently it was identified in a cluster

Fig. 2

Spatial cluster of years 1980–2013 suggests presence of 6–8 years cycles of HD outbreaks in Missouri. An HD outbreak is anticipated for during 2018–2020

Fig. 3

Significant spatio-temporal cluster of HD in white-tailed deer (1980–2013). Primary and secondary clusters of HD presence are displayed as orange and yellow, respectively

Fig. 4

Significant temporal trends (annual increases) of HD in white-tailed deer (1980–2013). Primary and secondary clusters of HD presence are displayed as orange and yellow, respectively. In the cases where a secondary cluster overlaps the primary cluster, the counties in the overlap are grouped within the primary cluster