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Forecasting the spatial transmission of influenza in the United States

https://doi.org/10.1073/pnas.1708856115
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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inferredcites →https://doi.org/10.1126/science.1134695openalex-snapshot
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An Ensemble Adjustment Kalman Filter for Data Assimilation
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Results from the centers for disease control and prevention’s predict the 2013–2014 Influenza Season Challenge
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Absolute Humidity and the Seasonal Onset of Influenza in the Continental United States
inferredcites →https://doi.org/10.1371/journal.pcbi.1003064openalex-snapshot
Multiple Estimates of Transmissibility for the 2009 Influenza Pandemic Based on Influenza-like-Illness Data from Small US Military Populations
inferredcites →https://doi.org/10.1371/journal.pcbi.1003583openalex-snapshot
Comparison of Filtering Methods for the Modeling and Retrospective Forecasting of Influenza Epidemics
inferredcites →https://doi.org/10.1371/journal.pcbi.1003635openalex-snapshot
Spatial Transmission of 2009 Pandemic Influenza in the US
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Retrospective Parameter Estimation and Forecast of Respiratory Syncytial Virus in the United States
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Forecasting Influenza Outbreaks in Boroughs and Neighborhoods of New York City
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A human judgment approach to epidemiological forecasting
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Human mobility and the spatial transmission of influenza in the United States
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Natural human mobility patterns and spatial spread of infectious diseases

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Modelling and forecasting the spread tendency of the COVID-19 in China
inferred← citeshttps://doi.org/10.21203/rs.3.rs-4557374/v1openalex-snapshot
The ensemble Kalman filter for dual estimation of hand-foot-mouth disease prediction and forecasting in Korea
inferred← citeshttps://doi.org/10.21203/rs.3.rs-50050/v1openalex-snapshot
Epidemic Dynamics of COVID-19 Based on SEAIUHR Model Considering Asymptomatic Cases in Henan Province, China
inferred← citeshttps://doi.org/10.21203/rs.3.rs-9223330/v1openalex-snapshot
Machine Learning-Based Forecasting of Tuberculosis Incidence in Taiwan: A Comprehensive Comparison of Traditional and Deep Learning Approaches with Projections to 2035
inferred← citeshttps://doi.org/10.2196/100492openalex-snapshot
Multisource Early Warning Framework Integrating Regional Pathogen Surveillance and Baidu Index Data for Respiratory Infectious Disease Prediction in Beijing: Retrospective Time-Series Modeling Study
inferred← citeshttps://doi.org/10.2196/41435openalex-snapshot
Effect of Rapid Urbanization in Mainland China on the Seasonal Influenza Epidemic: Spatiotemporal Analysis of Surveillance Data From 2010 to 2017
inferred← citeshttps://doi.org/10.23919/csms.2021.0025openalex-snapshot
State-of-the-Art Development of Complex Systems and Their Simulation Methods
inferred← citeshttps://doi.org/10.3389/ffutr.2021.646751openalex-snapshot
Empowering Compartmental Modeling With Mobility and Shelter-in-Place Analysis
inferred← citeshttps://doi.org/10.3389/fmed.2021.591372openalex-snapshot
Estimating the Prevalence of Asymptomatic COVID-19 Cases and Their Contribution in Transmission - Using Henan Province, China, as an Example
inferred← citeshttps://doi.org/10.3389/fpubh.2023.1154944openalex-snapshot
Estimation of influenza incidence and analysis of epidemic characteristics from 2009 to 2022 in Zhejiang Province, China
inferred← citeshttps://doi.org/10.3389/fpubh.2024.1379481openalex-snapshot
A study of the attenuation stage of a global infectious disease
inferred← citeshttps://doi.org/10.3389/fpubh.2026.1746529openalex-snapshot
FaXNet: a frequency-adaptive, explainable, and uncertainty-aware network for influenza forecasting
inferred← citeshttps://doi.org/10.3390/app122211849openalex-snapshot
Influence and Control of SARS-CoV-2 Transmission under Two Different Models
inferred← citeshttps://doi.org/10.3390/app16157586openalex-snapshot
MeteoGST: Meteorology-Driven Spatiotemporal Graph Learning for Epidemic Influenza Forecasting
inferred← citeshttps://doi.org/10.3390/e22020133openalex-snapshot
Phase Transitions in Spatial Connectivity during Influenza Pandemics
inferred← citeshttps://doi.org/10.3390/ijerph16142477openalex-snapshot
The Effects of Imitation Dynamics on Vaccination Behaviours in SIR-Network Model
inferred← citeshttps://doi.org/10.3390/ijerph17103510openalex-snapshot
Optimized Forecasting Method for Weekly Influenza Confirmed Cases
inferred← citeshttps://doi.org/10.3390/ijerph17134743openalex-snapshot
Forecasting Weekly Influenza Outpatient Visits Using a Two-Dimensional Hierarchical Decision Tree Scheme
inferred← citeshttps://doi.org/10.3390/ijerph18137120openalex-snapshot
Spatio-Temporal Analysis of Influenza-Like Illness and Prediction of Incidence in High-Risk Regions in the United States from 2011 to 2020
inferred← citeshttps://doi.org/10.3390/ijerph20105865openalex-snapshot
Who Gets the Flu? Individualized Validation of Influenza-like Illness in Urban Spaces
inferred← citeshttps://doi.org/10.3390/pathogens13080685openalex-snapshot
Advancing Public Health Surveillance: Integrating Modeling and GIS in the Wastewater-Based Epidemiology of Viruses, a Narrative Review
inferred← citeshttps://doi.org/10.3390/sym11030430openalex-snapshot
On a SIR Model in a Patchy Environment Under Constant and Feedback Decentralized Controls with Asymmetric Parameterizations
inferred← citeshttps://doi.org/10.3390/v14092063openalex-snapshot
Transmission Patterns of Seasonal Influenza in China between 2010 and 2018
inferred← citeshttps://doi.org/10.3390/v14122795openalex-snapshot
The Structural Identifiability of a Humidity-Driven Epidemiological Model of Influenza Transmission
inferred← citeshttps://doi.org/10.3390/v15071527openalex-snapshot
The Impact of Spring Festival Travel on Epidemic Spreading in China
inferred← citeshttps://doi.org/10.3934/mbe.2024036openalex-snapshot
Quantifying the presymptomatic transmission of COVID-19 in the USA
inferred← citeshttps://doi.org/10.4018/979-8-3693-3807-0.ch005openalex-snapshot
Harnessing the Power of Artificial Intelligence and the Internet of Things for Improved Epidemic Forecasting
inferred← citeshttps://doi.org/10.48550/arxiv.2002.04482openalex-snapshot
Network-Based Prediction of the 2019-nCoV Epidemic Outbreak in the Chinese Province Hubei
inferred← citeshttps://doi.org/10.48550/arxiv.2002.04663openalex-snapshot
TDEFSI: Theory Guided Deep Learning Based Epidemic Forecasting with Synthetic Information
inferred← citeshttps://doi.org/10.48550/arxiv.2003.11511openalex-snapshot
Contact Tracing Mobile Apps for COVID-19: Privacy Considerations and Related Trade-offs
inferred← citeshttps://doi.org/10.48550/arxiv.2011.03528openalex-snapshot
Optimal Resource and Demand Redistribution for Healthcare Systems Under Stress from COVID-19
inferred← citeshttps://doi.org/10.48550/arxiv.2012.15161openalex-snapshot
Universal Urban Spreading Pattern of COVID-19 and Its Underlying Mechanism
inferred← citeshttps://doi.org/10.48550/arxiv.2106.04420openalex-snapshot
Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future
inferred← citeshttps://doi.org/10.52396/justc-2025-0014openalex-snapshot
Theoretical foundations and application of the varying coefficient SEIR model
inferred← citeshttps://doi.org/10.53029/2786-4529-2020-1-2openalex-snapshot
FORMATION OF CRITERIA OF DESIGN AND SYSTEM MANAGEMENT OF EMERGENCY SITUATION OF MEDICAL AND BIOLOGICAL CHARACTER
inferred← citeshttps://doi.org/10.58664/mustjournal.2023.06.001openalex-snapshot
Review of Influenza Prediction
inferred← citeshttps://doi.org/10.6084/m9.figshare.12629125openalex-snapshot
Additional file 1 of Network-inference-based prediction of the COVID-19 epidemic outbreak in the Chinese province Hubei
inferred← citeshttps://doi.org/10.6084/m9.figshare.12629128openalex-snapshot
Additional file 2 of Network-inference-based prediction of the COVID-19 epidemic outbreak in the Chinese province Hubei
inferred← citeshttps://doi.org/10.6084/m9.figshare.13889232openalex-snapshot
Additional file 1 of Spatial transmission network construction of influenza-like illness using dynamic Bayesian network and vector-autoregressive moving average model
inferred← citeshttps://doi.org/10.6084/m9.figshare.13889235openalex-snapshot
Additional file 2 of Spatial transmission network construction of influenza-like illness using dynamic Bayesian network and vector-autoregressive moving average model
inferred← citeshttps://doi.org/10.6084/m9.figshare.16851223openalex-snapshot
Additional file 1 of Increased urbanization reduced the effectiveness of school closures on seasonal influenza epidemics in China
inferred← citeshttps://doi.org/10.6084/m9.figshare.20390918openalex-snapshot
Additional file 1 of Evaluating the impact of stay-at-home and quarantine measures on COVID-19 spread
inferred← citeshttps://doi.org/10.6084/m9.figshare.20423873openalex-snapshot
Additional file 1 of Understanding the romanization spreading on historical interregional networks in Northern Tunisia
inferred← citeshttps://doi.org/10.6084/m9.figshare.23354389openalex-snapshot
Additional file 1 of Quantifying the spatial spillover effects of non-pharmaceutical interventions on pandemic risk
inferred← citeshttps://doi.org/10.6339/21-jds1013openalex-snapshot
Methods, Challenges, and Practical Issues of COVID-19 Projection: A Data Science Perspective
inferred← citeshttps://doi.org/10.64898/2026.07.20.26358508openalex-snapshot
Learning shared forecast-error structure to improve ensemble forecasts of seasonal respiratory outbreaks
inferred← citeshttps://doi.org/10.7554/elife.40977openalex-snapshot
Inference and control of the nosocomial transmission of methicillin-resistant Staphylococcus aureus
inferred← citeshttps://doi.org/10.7554/elife.91849openalex-snapshot
Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States
inferred← citeshttps://doi.org/10.7554/elife.91849.1openalex-snapshot
Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States
inferred← citeshttps://doi.org/10.7554/elife.91849.1.sa3openalex-snapshot
eLife Assessment: Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States
inferred← citeshttps://doi.org/10.7554/elife.91849.2openalex-snapshot
Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States
inferred← citeshttps://doi.org/10.7554/elife.91849.2.sa0openalex-snapshot
Author response: Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States
inferred← citeshttps://doi.org/10.7554/elife.91849.2.sa1openalex-snapshot
Reviewer #1 (Public Review): Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States
inferred← citeshttps://doi.org/10.7554/elife.91849.3openalex-snapshot
Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.