The acute HD causes death within 1—2 weeks. Secondary infections may lead to death, but if female deer survives, she will pass on antibodies to the HD virus to her offspring [ 7 ]. There have been previous studies of the transmission and spread of HD throughout the southeastern United States. However, management actions to reduce or eliminate HD outbreaks are elusive [ 9 ]. One problem is that experimental tests of management treatments are not practical unless one can reasonably predict the locations of HD outbreaks.
Models that could predict outbreaks of HD could allow tests of the efficacy of proposed management actions e. Significant clusters were most evident in Alabama, North Carolina, and South Carolina between and Therefore, there is a need to further investigate the HD dynamics in Missouri.
The most severe outbreak was in the year when every county in Missouri reported at least one case of HD with more than 10, cases of mortality. This study can be of particular interest to the Missouri Department of Conservation MDC as well as cattle and white-tailed deer breeders in the state of Missouri. The MDC provided data on the size and location of deer population and the number of suspected HD occurrences in the wild and not captured deer data.
We note that only a small percentage of the data was actually confirmed as HD due to the time constraints of viable testing after death. The remaining portion of the data was collected by MDC officials based on observed symptoms. Estimated instances of HD, by county, were available for the years , , —, , , and Estimates of deer population were available for all years except , , and The geographic center centroid of each county was used to represent the location of the presence of or absence of HD in the county.
The base is geographic and, in turn, is centered on each of several possible grid points throughout the area of study. The height of the cylinder corresponds to a period of time within the study period. Thus, for each possible geographic location, it considers multiple-sized circles around the location and multiple possible time frames. For each location and scanning window, the program computes a likelihood ratio based on the number of observed cases versus the number of expected cases both inside and outside the window, using different probability models depending on the data.
This expected value is determined by a user-defined number of replications of the data. The number of incidents remains the same, but their distribution in the region is random. The program determines the significance of a cluster based on the actual number of incidents in each window in comparison to the expected number of incidents based on all the replications. With the discrete Poisson model, the program and analysis assumes that the number of cases at each location follows a Poisson distribution and that the expected number of cases in each location is proportional to its population size.
The space—time permutation model requires only case data and the number of observed cases in a cluster is compared to what would have been expected if all cases were independent of each other in both space and time as if there were no space—time interaction.
Under the null hypothesis of no significant clusters in the window, the window with the largest likelihood statistic is the most likely cluster. The program also identifies all secondary clusters with a P value less than 0. We used three different scans within SaTScan version 9. First, for the spatial scan statistic, we used the annual data to locate clusters in each year and to observe how these clusters changed across years.
Second, the space—time scan statistic was used. The space—time permutation model is ideal because it requires only case data, with information about the spatial location and time for each case. Moreover, it has the potential of identifying clusters that may not have been significant for any one specific year but are over spans of multiple years.
Third, the spatial scan with temporal trends was applied to all cases over the study period to locate clusters with more significant variations in the percentage change in the number of cases per year.
As part of the scan analysis, we chose elliptical scanning windows. For the grid points, we used the centroid of each county. When SaTScan identified a centroid within a cluster, we assumed the entire county was within the cluster. In cases where part of a country was within a particular ellipse, those counties were not included in the cluster if the centroid of the county was not included.
The number of random Monte Carlo replications to For the years when population data was not available, SaTScan estimated the population through linear interpolation. No additional information about controls or background population at risk is necessary. There were 16, cases of suspected HD reports over all Missouri counties during the study period.
If we count the number of times each county reported at least one case, there were times a county reported at least one case out of potential reporting times. During all years represented, had the largest number of cases 10, with all counties reporting at least one case and the estimated prevalence of 6.
Table 1 provides a summary of deer population, HD incidents, the number of counties affected and prevalence per thousand. Table 2 provides the locations of the most significant cluster in each year that HD data was available. In Fig. The darker the shading, the more frequently it was identified. We observe that SaTScan identified clusters in central to southwestern Missouri more frequently.
Figure 2 shows primary and secondary clusters over the study period. Although there is a gap between and data, we can see that the outbreaks have occurred in cycles of 6—8 years.
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. Spatial cluster of years — suggests presence of 6—8 years cycles of HD outbreaks in Missouri. An HD outbreak is anticipated for during — Four significant spatio-temporal clusters were detected, where the primary cluster consists of 32 counties in the eastern and southeastern portions of Missouri.
Figure 3 shows the locations of the significant spatio-temporal primary and secondary clusters. The three secondary clusters were located in the southwest cluster 2 , a small portion in the northeast cluster 3 , and a small cluster in the center of the state cluster 4.
See Table 3 for a summary of the significant clusters and the number of counties affected. Significant spatio-temporal cluster of HD in white-tailed deer — Primary and secondary clusters of HD presence are displayed as orange and yellow, respectively. There were no instances where a cluster had a significant annual decrease, and Fig.
The primary cluster is the northernmost third of Missouri. In the cases where a secondary cluster overlaps the primary cluster, the counties in the overlap are grouped within the primary cluster. Table 4 gives the proportion of cases in each cluster and its trend of annual increase. The highest trend of annual increase belongs to Howell County in southern Missouri. However, the five counties Audrain, Calla way, Osage, Maries, and Phelps in central Missouri have the highest number of annual cases Significant temporal trends annual increases of HD in white-tailed deer — In summary, using the statistical models and the available data, we identified the significant spatial and the spatiotemporal clusters of HD in white-tailed deer population residing in Missouri.
The most significant spatiotemporal cluster was identified in the southeastern counties of Missouri see Fig. These trends and clusters are in agreement with the density of captive white-tailed deer EHD cases during the most severe outbreak in see Figure 3 of [ 19 ].
However, as shown in Fig. Thus, there is a greater likelihood of outbreaks in the central and southwestern counties. Moreover, the spatial clusters shown in Fig.
Xu et al. It is important to note that HD occurs seasonally and nearly all reported cases occur during late summer and fall. This seasonal occurrence could be related to high abundance of Culicoides biting midges during late summer and fall as they transmit the disease. In particular, it is likely that HD outbreaks are more prevalent when weather conditions during the late summer and fall cause an abundance of muddy areas where midges breed.
This could be due to high summer temperatures that cause bodies of water to recede and leave mud flats or by overly rainy and wet conditions in late spring. Those very rare HD cases that are in late fall and winter represent the chronic form of HD. As outlined below, this study carries a number of limitations related to the data.
In general, data availability in wildlife is often an issue. Populations are not enclosed nor controlled, and getting accurate population counts is impossible. Counting the number of HD occurrences depends on observations of harvested deer. Variations in deer population density, regulations on who may harvest the deer, regulation on how many deer may be harvested, and other factors affect this count.
Indirect reports from the public may not be verifiable, and some regions may be restricted to hunters and the public at large. So, in actuality, these reports are only estimations and suspected reports. Also, HD often has a localized effect on the landscape. For example, the vast majority of the reports in Benton County in western Missouri were only from the northern half of the county. Furthermore, in years when there is not a significant known outbreak, results were reported to the MDC in January of the following year if at all , and because of this time lag, there is some concern over the accuracy of the reports.
Regardless, information of the spatiotemporal clustering may improve or design local surveillance and early warning systems [ 24 , 25 ]. In particular, areas with spatial and spatiotemporal HD clusters can be targets of more frequent surveillance.
These programs can serve as a sentinel to reduce number of HD cases in local farms and to sustain free-living deer population. Currently there are no effective wildlife management tools or strategies to control or prevent the hemorrhagic diseases in wildlife [ 6 ]. However, fencing off livestock and captive white-tailed deer from ponds can reduce the probability of encountering midges.
Thus, conservationists and wildlife managers may be able to use the outcomes of the clustering analyses to establish an early warning system to reduce the number of HD cases in livestock and captive white-tailed deer. An early warning system is also necessary for correct management of the free-living deer population. In particular, an early detection of HD outbreak can critically help the MDC officials to reduce the number of hunting permits in order to sustain the deer population in subsequent seasons.
The outcomes of the clustering analysis provided in this study reveals the significant magnitudes and directions of the HD spread in Missouri in the past three decades.
In conclusion, cluster analyses can improve our understanding of the epidemiology of hemorrhagic diseases and it can lead to designing effective surveillance and early warning programs. The Missouri Department of Conservation provided the data used in this analysis.
The authors are also thankful to multiple reviewers, including Dr. Aaron Reed at the School of Biological Sciences at UMKC, for valuable suggestions to improve the readability and quality of both this paper and this research. History and progress in the study of hemorrhagic disease of deer. Google Scholar. Bluetongue virus in sheep and cattle and Culicoides variipennis and C. Whitetails then get flu like symptoms including dehydration. They head to water sources because of this and often die within 24 hours of being bitten.
Although there have not been any confirmed cases of EHD in Missouri this year there are around suspected cases scattered throughout the state. To help gauge how big the outbreak is, Sumners encourages the public to report any dead deer without apparent injuries to local conservation officials and game wardens.
Another piece of advice he offers to hunters is to think about the number of deer you choose to harvest this year, especially if the local deer herd has been lowered by EHD. To learn even more about EHD, click here.
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