Identifying High-risk Areas of Foot-and-mouth Disease Outbreak Using a Spatiotemporal Score Statistic: A Case of South Korea

Abstract

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.

Regional Disparities in COVID-19 Vaccine Hesitancy: The Moderating Role of Social Distancing and Vaccine Rollout in the U.S.

“…Given additional regional variables, COVID-19 vaccine estimations may be performed to predict the timing of herd immunity in the future and the spatial distribution of vaccine hesitancy at granular geographic areas (Centers for Disease Control and Prevention 2021; Gu 2021). Third, the HPS microdata was available only at the state level, which can be resolved as more COVID-19 research note geographically-granular effects of the pandemic on our society (Yilmazkuday 2021; Pak et al 2020; Yao and Murray 2014). Furthermore, a panel survey-based research may allow researcher to control for individual-level fixed-effects which were not considered in this paper due to data limitation.…”

Regional Implications of COVID-19

“…First, noticing the local disparities in the prevalence of the disease and mortality rates, scholars employed spatial analysis techniques to map and understand the geographic distribution of COVID-19 cases, identifying hotspots, and examining factors contributing to their emergence (Amdaoud, Arcuri, and Levratto 2021a, 2021b; Bourdin et al 2023), following the example of previous work in health geography (Curtis and Riva 2010; Pak et al 2020). Questions arose regarding the influence of population density, urban-rural divide, and spatial inequalities in healthcare accessibility.…”