ArticleScientific reports2023
Forecasting virus outbreaks with social media data via neural ordinary differential equations.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Toward Drift-Aware Multimodal Early Warning for Acute Exacerbations of Chronic Obstructive Pulmonary Disease.JMIR medical informatics · 2026Article
- Artificial intelligence in infection surveillance: Data integration, applications and future directions.Biomedical journal · 2026Review
- Automated model discovery based on COVID-19 epidemiological data from Thuringia, Germany.PloS one · 2026Article
- Current state and open problems in universal differential equations for systems biology.NPJ systems biology and applications · 2025Article
- Socio-economic pandemic modelling: case of Spain.Scientific reports · 2024Article
- Plant science in the age of simulation intelligence.Frontiers in plant science · 2023Article
- Hybrid deep learning of social media big data for predicting the evolution of COVID-19 transmission.Knowledge-based systems · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During the Covid-19 pandemic, real-time social media data could in principle be used as an early predictor of a new epidemic wave. This possibility is examined here by employing a neural ordinary differential equation (neural ODE) trained to forecast viral outbreaks in a specific geographic region. It learns from multivariate time series of signals derived from a novel set of large online polls regarding COVID-19 symptoms. Once trained, the neural ODE can capture the dynamics of interconnected local signals and effectively estimate the number of new infections up to two months in advance. In addition, it may predict the future consequences of changes in the number of infected at a certain period, which might be related with the flow of individuals entering or exiting a region. This study provides persuasive evidence for the predictive ability of widely disseminated social media surveys for public health applications.
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Registered trials
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