ArticleNature communications2021
Forecasting influenza activity using machine-learned mobility map.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Learning shared forecast-error structure to improve ensemble forecasts of seasonal respiratory outbreaks.medRxiv : the preprint server for health sciences · 2026Article
- AI-Driven Approaches for the Detection, Classification, and Surveillance of Viral Pathogens: Current Advances, Challenges, and Future Directions.Pathogens (Basel, Switzerland) · 2026Review
- Efficient reporting delay calibration in spatial metapopulation models for reconstructing cross-regional epidemic dynamics.Biosafety and health · 2026Article
- Forecasting Influenza Epidemics and Pandemics in the Age of AI and Machine Learning.Reviews in medical virology · 2026Review
- A systems approach for anticipating post-pandemic forecast challenges for pediatric influenza and hospital burden.Scientific reports · 2025Article
- Improving outbreak forecasts through model augmentation.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Probability-Based Early Warning for Seasonal Influenza in China: Model Development Study.JMIR medical informatics · 2025Article
- Competition between transmission lineages mediated by human mobility shapes seasonal influenza epidemics in the US.Nature communications · 2025Article
- Article
- Advanced Artificial Intelligence Technologies Transforming Contemporary Pharmaceutical Research.Bioengineering (Basel, Switzerland) · 2025Review
- Modelling COVID-19 in the North American region with a metapopulation network and Kalman filter.Epidemics · 2025Article
- A prospective real-time transfer learning approach to estimate influenza hospitalizations with limited data.Epidemics · 2025Article
- Harnessing artificial intelligence for enhanced public health surveillance: a narrative review.Frontiers in public health · 2025Review
- The Constrained Disorder Principle Overcomes the Challenges of Methods for Assessing Uncertainty in Biological Systems.Journal of personalized medicine · 2024Review
- Novel multi-cluster workflow system to support real-time HPC-enabled epidemic science: Investigating the impact of vaccine acceptance on COVID-19 spread.Journal of parallel and distributed computing · 2024Article
- Increased Risk of Influenza Infection During Cold Spells in China: National Time Series Study.JMIR public health and surveillance · 2024Article
- Commuting-driven competition between transmission chains shapes seasonal influenza virus epidemics in the United States.medRxiv : the preprint server for health sciences · 2024Article
- Theoretical Epidemiology Needs Urgent Attention in China.China CDC weekly · 2024Article
- A simplicial epidemic model for COVID-19 spread analysis.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Forecasting influenza hospital admissions within English sub-regions using hierarchical generalised additive models.Communications medicine · 2023Article
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
Human mobility is a primary driver of infectious disease spread. However, existing data is limited in availability, coverage, granularity, and timeliness. Data-driven forecasts of disease dynamics are crucial for decision-making by health officials and private citizens alike. In this work, we focus on a machine-learned anonymized mobility map (hereon referred to as AMM) aggregated over hundreds of millions of smartphones and evaluate its utility in forecasting epidemics. We factor AMM into a metapopulation model to retrospectively forecast influenza in the USA and Australia. We show that the AMM model performs on-par with those based on commuter surveys, which are sparsely available and expensive. We also compare it with gravity and radiation based models of mobility, and find that the radiation model's performance is quite similar to AMM and commuter flows. Additionally, we demonstrate our model's ability to predict disease spread even across state boundaries. Our work contributes towards developing timely infectious disease forecasting at a global scale using human mobility datasets expanding their applications in the area of infectious disease epidemiology.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.