Establishment of a spatial-temporal model for tracking contagious diffusion of disease clustering

• Main Authors: I-Hsiang Wang, 王逸翔
• Other Authors: 溫在弘
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/81733428347146504604

碩士 === 國立臺灣大學 === 地理環境資源學研究所 === 101 === Most of the space-time analyses were developed to detect space-time clusters from an epidemic outbreak, such as SaTScan. However, they failed to detect the dynamic of the diffusion process. Our objective is to propose an analytical procedure to track the dynamics of space-time clusters from a contagious diffusion process. We used locations and illness onset time of dengue fever cases as a case study to demonstrate the framework of analytical procedure. For each pair of cases who are close in time and space, we defined them as clustering and infection pairs based on their space-time distance. We assigned a probability of infection to each infection pair, and developed a measurement called ''Common Origin Probability (C.O.P.)'' for each clustering pair based on the probability. The ‘origin’ was the individual or environment that infected others, and the C.O.P. represented the probability that 2 nodes infected by the same origin. Clustering pairs with high C.O.P. value are cases close in space and time and likely infected from the same origin and form space-time sub-clusters. We tracked their temporal progression based on the probability of infection, and identify different dynamic behaviors such as emergence, disappearance, growth, shrinking, splitting and merging. Areas displaying splitting and merging behaviors represent different risk patterns. The former represents places with dangerous and infections environments, and the latter represents vulnerable places surrounded by infectious environments. Identifying dynamic behaviors of sub-clusters can provide spatial and temporal insights into epidemic progression and risk patterns of disease clustering.