Stochastic and spatio-temporal analysis of the Middle East Respiratory Syndrome outbreak in South Korea, 2015

Lee, Hyunsun

Besides the mathematical models to predict epidemics, the temporal and spatial proximity of subjects has been studied and used to characterize an epidemic that occurs through direct contacts at different places and times. Earlier, spatial patterns were analyzed to describe the geographical spread of plant and human diseases (Campbell & Noe, 1985; Nicot, Rouse, & Yandell, 1984; Snow, 1855) . John Snow (Snow, 1855) greatly contributed to the early medical geography by mapping and analyzing the major cholera outbreak of London in 1854. However, it was later realized that the disease outbreak patterns needed to be explained not only spatially but also temporally. The techniques of spatio-temporal autocorrelation were introduced with an example of population diffusion in North-west England by Bennett (Bennett, 1975a (Bennett, , 1975b and it was well summarized in subsequent literature (Reynolds & Madden, 1988) . Recently, the spatio-temporal analysis and autocorrelation were used to analyze fatal infectious human diseases such as Dengue fever epidemics in Southern Vietnam (Cuong et al., 2013) , and the spread of Severe Acute Respiratory Syndrome (SARS) in mainland China (Cao et al., 2016) , etc. Cao, in the analysis of the spread of SARS in China (Cao et al., 2016) , used Bayesian Maximum Entropy modeling and observed the empirical covariance based on a fitted theoretical covariance model with behavior of sine fluctuation in space and exponential decay in time.