Source detection on networks using spatial temporal graph convolutional networks | IEEE Conference Publication | IEEE Xplore
By early January 2021, the number of confirmed COVID-19 cases has reached 83.6 millions world-wide, and over 1.8 million people have lost their lives. One important method for limiting transmission of an infectious disease consists of identifying epidemic cluster sources and isolating them from the population. Epidemiologists conduct source detection by analysing the genetic evolution of virus strains [1] or by contact tracing [2], which can be time-consuming and labor-intensive. However, COVID-19 has demonstrated limits to contact tracing when prevalence is widespread, for example in the United States, and methods are needed for source detection in such situations. 2024 7th International Conference on Data Science and Information Technology (DSIT) Published: 2024 2020 IEEE International Conference on Big Data (Big Data) Published: 2020 About IEEE Xplore | Contact Us | Help | Accessibility | Terms of Use | Nondiscrimination Policy | IEEE Ethics Reporting | Sitemap | IEEE Privacy Policy
By early January 2021, the number of confirmed COVID-19 cases has reached 83.6 millions world-wide, and over 1.8 million people have lost their lives. One important method for limiting transmission of an infectious disease consists of identifying epidemic cluster sources and isolating them from the population. Epidemiologists conduct source detection by analysing the genetic evolution of virus strains [1] or by contact tracing [2], which can be time-consuming and labor-intensive. However, COVID-19 has demonstrated limits to contact tracing when prevalence is widespread, for example in the Unit
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