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Zoning is crucial for controlling animal infectious diseases and movement plays a major role in disease transmission. However, movement-based zoning has received little research attention. This study aimed to identify biosecurity zones divided by administrative unit, based on communities detected in movement network. We used vehicle entry data from November 2013 to January 2017. We split the data to analyze changes in networks over time and seasons (3 summer and 4 winter). The HN algorithm for mega-scale networks was used to detect communities. We identified biosecurity zones based on the geographical concentration of facilities belonging to the same communities. Jenks Natural Breaks Method was used to determine whether facilities were agglomerated. The zone classifications derived for seven seasons were overlaid to identify an integrated zone classification. The number of significant communities declined from 10 to 7 over time, from which we inferred that separated communities tended to aggregate. Therefore, biosecurity zones that were separate in the past merged and the number of zones decreased. From the overlay, seven biosecurity zones were derived. These zones are different from the conventional control zones, which do not consider movement. Therefore, these biosecurity zones can be used as an alternative control zone to complement existing zoning systems in Korea.

The objective of this study is to identify high-risk areas of foot-and-mouth disease (FMD) in South Korea using nationwide data collected for the disease cases that occurred during the period from December 2014 to April 2015. High-risk areas of FMD occurrence are defined as local clusters or hot spots, where the frequency of disease occurrence is higher than expected. An issue in the FMD detection study is in identifying a spatial pattern deviated significantly from the expected value under the null hypothesis that no spatial process is investigated. While identifying geographic clusters is challenging to reveal the causes of disease outbreak, it is most useful to detect and monitor potential areas of risk occurrence and suggest a further in-depth investigation. This study extended a traditional score statistic (SC) that has limited to identify the spatial pattern by proposing a spatiotemporal score statistic (STSC) that incorporates a temporal component into the SC approach. STSC, a local spatial statistic, was utilized to detect clusters around the known foci with a latent period. This study demonstrated STSC could better exploit the advantage of the original SC and improve the cluster detection due to the latent time component. The empirical results of STSC are expected to provide more useful policy implications with agencies in charge of preventing and controlling the spread of epidemics when deciding where to concentrate the limited resources available.

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