Fast Mining and Forecasting of Co-evolving Epidemiological Data Streams

Tasuku Kimura, Yasuko Matsubara, Koki Kawabata, Yasushi Sakurai
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,
more » ... e propose a new streaming algorithm, E C , which is able to model, understand and forecast dynamical patterns in large co-evolving epidemiological data streams. Our proposed method is designed as a dynamic and flexible system, and is based on a unified non-linear differential equation. Our method has the following properties: (a) Effective: it operates on large co-evolving epidemiological data streams, and captures important world-wide trends, as well as location-specific patterns. It also performs real-time and long-term forecasting; (b) Adaptive: it incrementally monitors current dynamical patterns, and also identifies any abrupt changes in streams; (c) Scalable: our algorithm does not depend on data size, and thus is applicable to very large data streams. In extensive experiments on real datasets, we demonstrate that E C outperforms the best existing stateof-the-art methods as regards accuracy and execution speed. CCS CONCEPTS • Information systems → Data stream mining; • Mathematics of computing → Nonlinear equations.
doi:10.1145/3534678.3539078 fatcat:g65ndlkvxnhnjpuqxrfldn2zne