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Fast Mining and Forecasting of Co-evolving Epidemiological Data Streams
2022
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Given a large, semi-infinite collection of co-evolving epidemiological data containing the daily counts of cases/deaths/recovered in multiple locations, how can we incrementally monitor current dynamical patterns and forecast future behavior? The world faces the rapid spread of infectious diseases such as SARS-CoV-2 (COVID-19), where a crucial goal is to predict potential future outbreaks and pandemics, as quickly as possible, using available data collected throughout the world. In this paper,
doi:10.1145/3534678.3539078
fatcat:g65ndlkvxnhnjpuqxrfldn2zne