1. Introduction
In Korea, the amount of direct damage caused by foot-and-mouth disease (FMD) and avian influenza (AI) between 2010 and 2018 was 4 trillion won (about
$3.5 billion) [
1]. In addition, the incidence of livestock epidemics results in large-scale stamping out. Between 2013 and 2016, 47,922,000 livestock were killed in Korea to prevent the spread of AI, which is the second highest number in the world after the United States (49,241,700) [
2]. Korea had the highest number of livestock killed to prevent the spread of FMD globally (208,000) [
2]. Stamping out makes large-scale burial sites where animal carcasses are buried, and the environmental pollution caused by wastewater generated from these burial sites is very serious. In other words, repetitive livestock epidemics pose a serious threat to Korea’s economic and environmental sustainability.
For many animal infectious diseases, OIE (Office International des Épizooties) member countries have traditionally applied the concept of zoning to control epidemics [
3]. A zone is a part of a country defined by the veterinary authority for the purpose of international trade or disease prevention or control, which contains an animal population or subpopulation with a specific health status with respect to an infection or infestation [
4]. OIE recommends zoning as a means to limit the impact of animal disease epidemics [
4]. The rationale behind this recommendation is that zones may be selectively targeted as part of an intensive quarantine program and used to determine the boundaries for movement restriction. Therefore, zone boundaries should be established that take into consideration epidemiological, economic, social, administrative and legal factors and they should act as a biosecurity barrier against the spread of disease [
5]. However, the OIE has not provided specific procedures and criteria for establishing the zones [
6]. Thus, in many countries, the boundaries of administrative districts have been used as the zone boundaries [
5]. However, the boundaries of administrative districts do not take into account the epidemiological situation. In addition, the most widely used standard for setting zones is the straight-line distance from the epidemic facility. Korea, China, Thailand, Australia and many other countries use 10 km as a standard [
7,
8,
9]. In Korea, when an epidemic occurs, the area within a 10 km radius of the epidemic facility is designated as a control zone [
10,
11] and the high-ranked administrative division in which the epidemic facility is located is designated as the secondary control zone [
10,
11].
In this situation, the boundaries of zones may readily be breached through numerous epidemiological pathways [
3]. Thus, the relatively recent concept of compartments is emerging as a means to prevent and control animal infectious diseases. Compartments are defined as animal subpopulations contained in one or more establishments, separated from other susceptible populations by a common biosecurity management system, and with a specific animal health status with respect to one or more infections or infestations for which the necessary surveillance, biosecurity and control measures have been applied for the purposes of international trade or disease prevention and control in a country or zone [
4]. Compared to zones, compartments consider non-spatial factors other than geographical proximity that may jeopardize the biosecurity of a compartment [
12]. Generally, these non-spatial factors include the movement of animals, equipment, other fomites, feed and people [
12]. In short, compartments are set up on a facility basis, while zones are based on geographical boundaries. In addition, compartments are established based on many epidemiological factors, such as movement, while zones are established based on animal’s health status.
Many studies have reported that animal movement is one of the leading causes of animal infectious disease transmission [
13,
14,
15,
16,
17,
18]. However, in Korea, not only animal movement but also vehicle movements for various purposes between livestock facilities play a major role in spreading infectious diseases [
18]. In the case of FMD, vehicles for animal movement are the most important cause of transmission (54.2%); feed-carrier vehicles (18.9%) and other vehicles (8.4%) also contribute significantly to transmission [
17] (as cited in [
2], p. 3). Even in the case of AI, vehicle contact (28.9%) is a more important cause of transmission than animal movement (7.1%) [
18]. Studies have been conducted on the relationship between movement networks and animal infectious diseases. Kao et al. [
19] introduced network-based frameworks for analyzing the spread of epidemics, and subsequent studies [
12,
15,
16,
20,
21,
22] analyzed network characteristics and simulated the spread of disease through animal movement networks. In these studies, the spread of disease through a network has been analyzed in both ungulates [
15,
16,
19,
22] and poultry [
12,
20,
21]. In other words, previous studies assume that both AI and FMD can propagate through a movement network.
Studies have also been conducted to identify compartments through the structure of animal movement networks [
12,
15,
19,
22], as these have a considerable effect on the spread of disease. In networks, groups of actors appear that frequently interact with each other. Social Network Analysis (SNA) refers to these groups as communities (sometimes referred to as clusters, subgroups, etc.) and identifying these various communities is one of the main aims of SNA. In an animal movement network, a community is a group of livestock facilities where frequent movements occur between them. As mentioned earlier, animal movement plays a significant role in the propagation of animal epidemics. Therefore, if an infectious disease occurs within a particular community, the infection risk at the facilities belonging to that community is very high. In other words, communities derived from movement networks can be determined as compartments that focus on the infection risk from movement. Green et al. [
15] and Kao et al. [
19] used community detection analysis in the SNA to identify communities containing several facilities. Nickbakhsh et al. [
12] and Martınez-Lopez et al. [
22] derived communities by analyzing the connections in the network and presented them as compartments for managing infectious diseases.
Using the compartments derived from a movement network structure is possible to clearly identify high-risk subpopulations, in contrast to zones based on administrative boundaries or geographical proximity. In Korea, however, the main body managing the quarantine process is the local government, and it conducts disinfection of general vehicles at the boundary of administrative districts. In this situation, facility-based compartments have some disadvantages, and there are still advantages to zones that are divided into administrative units for effective quarantine. Therefore, it is necessary to establish zones divided by administrative units, with the advantages of compartments that also consider the risk of facilities. However, compared to network-based compartments, network-based zones have received relatively little attention.
Community structure in animal movement networks is highly regional; facilities belonging to the same community are concentrated in specific areas [
15]. Therefore, it is possible to identify zones based on the geographical distribution of compartments. We aimed to identify zones divided by administrative unit, based on communities detected in a movement network. As mentioned earlier, if an infectious disease occurs within a particular community of a movement network, the infection risk at the facilities belonging to same community is very high. In this study, we refer to this zone as the biosecurity zone. The main research question of this study was whether biosecurity zones could be identified based on communities of livestock vehicle movement networks in Korea. This was split into sub-questions as follows: How do communities detected using movement networks change over time? 2. How are facilities in the same community spatially distributed? 3. What is the difference between the movement-based biosecurity zone and the conventional control zone?
3. Results and Discussion
3.1. Construction of Vehicle Movement Network
Table 2 and
Figure 6 show the results of the construction of the vehicle movement networks for all seven time periods.
Facilities, total connection and total movement are indicators of the size of the network. As shown in
Figure 6a–c, from winter of 2013 to summer of 2016, these three indicators tend to gradually increase. This means that the size of the network gradually increases. In the winter of 2016, these three indicators decreased. This means that the network was reduced in size in that period, which is thought to be due to the large-scale AI outbreak in winter 2016.
Connections per facilities shows, on average, how many connections one facility has. The higher this value means that each facility has more connections with other facilities. And it can be judged that the facilities of the network are more closely connected. Movements per facilities shows, on average, how many vehicle contacts have been made to single facility. These two indicators indicate how dense the network is, and the higher the value of these indicators, the more closely the facilities are connected with each other. As shown in
Figure 6d,e, The values of these two indicators tend to increase in summer and decrease in winter. This means that the density of the networks increases in summer compared to winter.
On average, each network contains 96,047 facilities and 1,095,910 connections. And there are 7,069,043 vehicle movements in each network. A single facility, on average, has connections with 11 other facilities and 74 vehicle movements occur in three months.
3.2. Detecting Communities
Table 3 shows the analysis results of community detection through the HN algorithm. The average value of modularity is 0.62 and the modularity values at all periods are 0.6 or higher. The modularity value lies in the range (−1–1), and the optimal value is 1. However, in practice, modularity values above 0.7 are rare [
28], and modularity value above 0.3 is a good indicator of significant community structure [
29]. Therefore, it can be judged that the communities are appropriately detected in all seven periods.
In the process of detecting the communities, small communities such as community D in
Figure 1 were also detected. Significant communities are communities that contain a significant number of facilities from which a geographical distribution can be identified. The sum of the number of significant communities and small communities is the number of entire communities.
By comparing the max # of facilities in small communities and min # of facilities in significant communities, it is possible to see how much smaller the small communities were than the significant communities. As shown in
Table 3, the min # of facilities in significant communities is, on average, 105.8 times the max # of facilities in small communities. In addition, many small communities have only two facilities. Therefore, small communities are so small that it made no sense to analyze the geographical distribution and they did not have meaningful roles in the movement network. Therefore, we excluded these small communities from the next step of the analysis.
Figure 7 shows that the pattern of change in the total number of communities was exactly the same as the change in the number of small communities. That is, the increase in the total number of communities was due to the increase in the number of fragmented small communities.
The number of significant communities showed a slight decrease, and the min # of facilities in significant communities showed an overall increase. The number of significant communities decreased over time, and the size of each significant community increased over time indicating that significant communities that were separated in the past tended to aggregate over time.
Figure 8 and
Figure 9 show the geographical distribution of facilities belonging to nine significant communities from the summer 2015 data, which had the most representative aspects. As expected, the figure shows that livestock facilities belonging to the same community are clustered in a particular geographical area. However, the clustered area is very large compared to the 10 km boundary that is currently set as the control zone in Korea and extends beyond the boundaries of high-ranked administrative divisions. These results confirm the need to establish biosecurity zones based on the movement networks.
It is not possible to report the distribution of facilities for all seven networks here. For all periods, facilities belonging to the same community were clustered geographically. Therefore, we could distinguish zones based on the distribution of facilities.
3.3. Biosecurity Zone for Livestock Infectious Disease
Figure 10 shows the distribution of administrative units according to the number of facilities that they contained. As shown in
Figure 10, few administrative units contained more than 50 facilities in any community (8.82% of the total administrative units). It is possible, therefore, to clearly distinguish between the administrative units in which the facilities belonging to the same community are concentrated and the administrative units where they are not.
Figure 11 shows the results of identification of zone by time period. The administrative units in which the facilities belonging to a certain community are concentrated are painted in the same color. As described above, the administrative units were divided into two classes through the Jenks Natural Breaks method, and the administrative units included in the upper classes were judged as administrative units in which the facilities were agglomerated. For example, the administrative units painted in blue in
Figure 11d are the administrative units where facilities belonging to community F in
Figure 8f are agglomerated. Comparing
Figure 9 and
Figure 11d, we can see that the administrative units where the same community’s facilities are agglomerated are identified as the same zone. The area indicated by the same color in
Figure 11 can be judged as the biosecurity zone of the time periods.
Figure 11 shows some administrative units in white, which are administrative units with no agglomerated facilities. In
Figure 11h, the dark areas are mountainous areas and red lines on the map represent metropolitan cities. From
Figure 11a–g, areas marked in white are mainly found in metropolitan cities and surrounding areas, mountainous areas and coastal areas. In other words, these areas did not contain many livestock facilities.
As the Korean livestock vehicle movement networks changes over time [
24], the results of community detection also varied with time. Therefore, the results for the biosecurity zones also differed for each time period.
Figure 11 shows the changing patterns of the biosecurity zones that were derived for each time period. As time progressed, the biosecurity zones tended to merge, and the geographical area of a single zone tended to expand. In other words, the area over which facilities had close contact became broader over time.
3.4. Integrated Biosecurity Zone for Livestock Infectious Disease
As expected,
Figure 11 shows that the classification of zones varied over time. These results show that it is not appropriate to distinguish biosecurity zones based on data for one particular period.
Seven biosecurity zones were derived by overlaying the zone classifications for each time period.
Figure 12 shows the integrated biosecurity zones. In these biosecurity zones, facilities in which vehicle movement frequently occurs are agglomerated. Therefore, if an epidemic occurs in a particular area, the epidemic may spread rapidly within the zone. Currently, a 10 km radius and high-ranked administrative divisions are being used as the control zones in Korea. However, as shown in
Figure 12, the biosecurity zones are much larger than 10 km and differ considerably from high-ranked administrative unit boundaries. Therefore, these biosecurity zones are expected to complement conventional control zones.
3.5. Implication
The results of this study have a number of policy implications. First, it is necessary to establish biosecurity zones that take epidemiological situations into consideration. In Korea, high-ranked administrative divisions are simply used as secondary control zones. However, the results of this study show that the spatial range in which vehicle movements between facilities occur differs from the spatial range of the high-ranked administrative divisions. Therefore, there is a high possibility that the current control zones will fail to effectively prevent the spread of an epidemic.
Second, the establishment of collective inter-municipality governance beyond the boundary of each municipality is necessary. As described above, the biosecurity zones based on the movement of livestock vehicles are very different from the boundaries of the present administrative units. Therefore, it is necessary to establish governance that enables uniform quarantine activities beyond the boundaries of the individual administrative unit and the central government should provide institutional and financial support for this.
3.6. Limitations and Future Study
A limitation of this study is that it considers only the movement of livestock vehicles as an epidemiological factor for deriving the biosecurity zones. However, in addition to vehicle movements, many factors are involved in the propagation of livestock epidemics. For example, migratory birds play an important role in the propagation of AI. Therefore, future studies should examine a range of epidemiological factors in order to derive more specialized biosecurity zones.
To comprehensively consider potential epidemiological situations, separate analyses for different species are needed. In Korea, some farms raise different kinds of animals together, but the KAHIS DB only lists the representative species of each farm, that is, it does not provide information on all species. Under these circumstances, it is less useful and reliable to construct networks separated by species and in this study, all the livestock related facilities were included in one network without distinguishing the species. In the future, additional data should be collected to support studies that derive biosecurity zones by species.
Using only three years of data is an additional limitation of this study. If longer-term data are available in the future, it will be possible to monitor the changing patterns in vehicle movement networks over longer time periods and to forecast the changes in the biosecurity zones.
4. Conclusions
This study derived biosecurity zones based on vehicle movement networks, which have received relatively little attention. To accomplish this, we constructed seven vehicle movement networks based on vehicle entry data and applied community detection analysis, Jenks natural breaks method and overlay techniques to identify biosecurity zones.
The size and density of livestock vehicle movement networks change with time. The density of networks tended to increase in summer and decrease in winter. The total number of communities increased over the study period, due to the increase in the number of fragmented small communities. However, the number of significant communities declined and tended to aggregate over time.
Facilities belonging to the same community were clustered geographically and it was possible, therefore, to distinguish between those administrative units that included many facilities belonging to a particular community and administrative units that did not. As the number of significant communities decreased over time, the number of biosecurity zones also decreased. As a result, biosecurity zones tended to merge, and the geographical range of any single zone expanded. Therefore, the danger of propagation when diseases occur is also increased.
Seven biosecurity zones were identified, and these are very different from the conventional control zones used in Korea. Therefore, these biosecurity zones can be used as an alternative control zone to complement existing zoning systems in Korea. It is well known that zoning is crucial to prevent the spread of animal infectious diseases and that movement networks play a major role in spreading these diseases. Nonetheless, network-based zones have received less research attention than network-based compartments. Furthermore, many countries do not consider the full suite of epidemiological factors, but simply establish the zones based on straight-line distance or administrative boundaries. In this situation, this study is meaningful in that it suggests biosecurity zones to control the spread of infectious diseases based on movement networks.