2020/04/08 by Dianbo Liu, Leonardo Clemente, Liu, Dianbo +13
Computer Science · Mathematics · Medicine · #Anomaly Detection Techniques and Applications #COVID-19 epidemiological studies #Data-Driven Disease Surveillance #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Other Statistics (stat.OT) #Populations and Evolution (q-bio.PE)
paper · doi:10.48550/arxiv.2004.04019
openalex publication_date 2020/04/08 · openalex created_date 2020/04/17 · openalex updated_date 2026/07/28
We present a timely and novel methodology that combines disease estimates from mechanistic models with digital traces, via interpretable machine-learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real-time. Specifically, our method is able to produce stable and accurate forecasts 2 days ahead of current time, and uses as inputs (a) official health reports from Chinese Center Disease for Control and Prevention (China CDC), (b) COVID-19-related internet search activity from Baidu, (c) news media activity reported by Media Cloud, and (d) daily forecasts of COVID-19 activity from GLEAM, an agent-based mechanistic model. Our machine-learning methodology uses a clustering technique that enables the exploitation of geo-spatial synchronicities of COVID-19 activity across Chinese provinces, and a data augmentation technique to deal with the small number of historical disease activity observations, characteristic of emerging outbreaks. Our model's predictive power outperforms a collection of baseline models in 27 out of the 32 Chinese provinces, and could be easily extended to other geographies currently affected by the COVID-19 outbreak to help decision makers.