ArticleScience advances2021
Toward the use of neural networks for influenza prediction at multiple spatial resolutions.
Article in Science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 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
- Multi-source machine learning for short-term influenza forecasting in Hunan Province, China: a spatiotemporal analysis using surveillance, search, meteorological and air-quality data.BMC infectious diseases · 2026Article
- AI Meets Attitudes: Cross-Sectional Quantitative Study of COVID-19 Vaccine Hesitancy in Alaska's Diverse Communities.Journal of medical Internet research · 2026Article
- A kernelized data-driven approach for analyzing and predicting brucellosis in Inner Mongolia.Innovation (Cambridge (Mass.)) · 2026Article
- Development of a predictive model using automated machine learning for Carbapenem-resistant Organisms (CRO) infections in hospitalized patients.Frontiers in microbiology · 2026Article
- Ensemble approaches for short-term dengue fever forecasts: A global evaluation study.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Competition between transmission lineages mediated by human mobility shapes seasonal influenza epidemics in the US.Nature communications · 2025Article
- Using Surveillance Data to Estimate Infectious Disease Burden: Opportunities and Challenges.American journal of public health · 2025Article
- A prospective real-time transfer learning approach to estimate influenza hospitalizations with limited data.Epidemics · 2025Article
- Deep learning architectures for influenza dynamics and treatment optimization: a comprehensive review.Frontiers in artificial intelligence · 2025Review
- A Novel Grammar-Based Approach for Patients' Symptom and Disease Diagnosis Information Dissemination to Maintain Confidentiality and Information Integrity.Bioengineering (Basel, Switzerland) · 2024Article
- Forecasting severe respiratory disease hospitalizations using machine learning algorithms.BMC medical informatics and decision making · 2024Article
- An adaptive weight ensemble approach to forecast influenza activity in an irregular seasonality context.Nature communications · 2024Article
- Psychological AI: Designing Algorithms Informed by Human Psychology.Perspectives on psychological science : a journal of the Association for Psychological Science · 2024Article
- Commuting-driven competition between transmission chains shapes seasonal influenza virus epidemics in the United States.medRxiv : the preprint server for health sciences · 2024Article
- Spatial analysis and risk mapping ofParasite epidemiology and control · 2023Article
- Neural network models for influenza forecasting with associated uncertainty using Web search activity trends.PLoS computational biology · 2023Article
- Graph Neural Network Modeling of Web Search Activity for Real-time Pandemic Forecasting.Proceedings. IEEE International Conference on Healthcare Informatics · 2023Article
- Influenza's Plummeting During the COVID-19 Pandemic: The Roles of Mask-Wearing, Mobility Change, and SARS-CoV-2 Interference.Engineering (Beijing, China) · 2023Article
- Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States.Science advances · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Mitigating the effects of disease outbreaks with timely and effective interventions requires accurate real-time surveillance and forecasting of disease activity, but traditional health care-based surveillance systems are limited by inherent reporting delays. Machine learning methods have the potential to fill this temporal "data gap," but work to date in this area has focused on relatively simple methods and coarse geographic resolutions (state level and above). We evaluate the predictive performance of a gated recurrent unit neural network approach in comparison with baseline machine learning methods for estimating influenza activity in the United States at the state and city levels and experiment with the inclusion of real-time Internet search data. We find that the neural network approach improves upon baseline models for long time horizons of prediction but is not improved by real-time internet search data. We conduct a thorough analysis of feature importances in all considered models for interpretability purposes.
Identifiers
What OpenQuestion holds
Registered trials
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.