Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: May 14, 2020
Date Accepted: Jul 24, 2020
Date Submitted to PubMed: Sep 22, 2020
A machine learning method to forecast in real-time the COVID-19 outbreak in Chinese provinces using novel digital data and estimates from mechanistic models.
ABSTRACT
Background:
The inherent difficulty to identify and monitor emerging outbreaks caused by novel pathogens can lead to their rapid spread; and if left unchecked, they may become major public health threats to the planet. The ongoing COVID-19 outbreak, which has infected over 2,300,000 individuals and caused over 150,000 deaths, is an example of one of these catastrophic events.
Methods:
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 uses as inputs (a) official health reports (b) COVID-19-related internet search activity (c) news media activity and (d) daily forecasts of COVID-19 activity from a metapopulation 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 1 of historical disease observations, characteristic of emerging outbreaks.
Results:
Our model is able to produce stable and accurate forecasts two days ahead of current time, and outperforms a collection of baseline models in 27 out of the 32 Chinese provinces
Conclusions:
Our methodology could be easily extended to other geographies currently affected by the COVID-19 outbreak to help decision makers.
Citation
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