Fast Mining and Forecasting of Co-evolving Epidemiological Data Streams
28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022
; : 3157-3167, 2022.
Article
in English
| Scopus | ID: covidwho-2020394
ABSTRACT
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, we propose a new streaming algorithm, EPICAST, 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 EPICAST outperforms the best existing state-of-the-art methods as regards accuracy and execution speed. © 2022 ACM.
data streams; epidemics; non-linear dynamical systems; real-time forecasting; tensor data analysis; time series; Data mining; Differential equations; Dynamical systems; Forecasting; Linear control systems; Time series analysis; 'current; Data stream; Dynamical pattern; Epidemic; Infectious disease; Streaming algorithm; Tensor data analyse; Times series; Real time systems
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Observational study
Language:
English
Journal:
28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022
Year:
2022
Document Type:
Article
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