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SA Ahad
Jul 31, 2022
In DIY Forum
Since the end of 2019, the COVID-19 (serious special infectious pneumonia, new crown pneumonia, Wuhan pneumonia) epidemic that has ravaged the world has not only deprived countless human lives, but also deprived many survivors of their personal freedom (work from home, city closures and other controls) measures) and mental health, and the global economic toll and soaring unemployment that it caused indirectly affected the survival of most people. Facing the threat posed by infectious diseases to human society, understanding the temporal and spatial characteristics of the spread of infectious diseases, Preventing their further spread, and judging popular database vulnerable areas with lack of medical resources are all important aspects of epidemic prevention decision-making. Therefore, my doctoral dissertation proposed an improved algorithm or mathematical model for each of these three aspects from the viewpoint of geographic computing, thereby helping to improve the decision support for infectious disease prevention and treatment. Fewer people and vehicles in the urban area on the first day of the Dragon Boat Festival holiday (1) Photo Credit: WebMD First, the Modified Space-Time Density-based Spatial Clustering of Applications with Noise (MST-DBSCAN) algorithm takes into account the time gap between cases caused by the incubation period characteristics of infectious diseases; at the same time, it uses point data to "cluster" instead of traditional The "hot zone" view of spatial analysis to judge disease clusters. The combination of these two features allows MST-DBSCAN to "track" the spatiotemporal evolution pattern of each cluster of cases, as well as the interaction phenomena such as fusion and fragmentation between different clusters.
To understand the spatiotemporal characteristics of the spread of infectious diseases content media
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SA Ahad

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