Forecasting virus outbreaks with social media data via neural ordinary differential equations

MATIAS NUÑEZ; NADIA BARREIRO ; RAFAEL BARRIO; CHRISTOPHER RACKAUCKAS

Nature Scientific Reports

Nature Springer

Año: 2021

2666-3899

In the midst of the covid-19 pandemic, social media data collected in real time has the potential of being an early indicator of a new epidemic wave. This possibility is explored here by using a neural ordinary differential equation (neural ODE) that is trained to predict virus outbreaks for a geographic region. It learns from multivariate time series of signals obtained from a novel set of massive online surveys about COVID-19 symptoms. Once trained, the neural ODE is able to capture the dynamics of the interlinked local signals and accurately predict the number of new infections up to two months in advance. Moreover, it can estimate the future effects of changes in the number of infected at a given time, which can be associated with the flow of people entering or leaving a given region or, for instance, with a local vaccination campaign. This work gives compelling preliminary evidence for the predictive power of widely distributed social media surveys for public health application