A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models

TL;DR

This paper introduces a machine learning approach that combines mechanistic model estimates with digital data sources to forecast COVID-19 activity in Chinese provinces two days in advance, demonstrating high accuracy and potential for broader application.

Contribution

The study presents a novel, interpretable machine learning methodology that integrates diverse data sources and geo-spatial clustering to improve real-time COVID-19 forecasting accuracy.

Findings

Model outperforms baseline models in 27 of 32 provinces

Uses diverse data sources including health reports, internet searches, news, and mechanistic forecasts

Enables reliable two-day-ahead COVID-19 activity predictions

Abstract

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.