ArticlePLoS computational biology2018
Deploying digital health data to optimize influenza surveillance at national and local scales.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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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.
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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.
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Who cites it
18 citing papers in PubMed, 39 citations in OpenAlex.
- Learning shared forecast-error structure to improve ensemble forecasts of seasonal respiratory outbreaks.medRxiv : the preprint server for health sciences · 2026Article
- Beyond influenza-like-illness: data-driven syndromic surveillance for fine-scale monitoring of seasonal respiratory viruses.medRxiv : the preprint server for health sciences · 2026Article
- Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States.eLife · 2024Article
- Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States.medRxiv : the preprint server for health sciences · 2024Article
- Molecular characterization of influenza virus circulating in Nepal in the year 2019.Scientific reports · 2024Article
- Spatial distribution and determinants of childhood vaccination refusal in the United States.Vaccine · 2023Article
- Spatial aggregation choice in the era of digital and administrative surveillance data.PLOS digital health · 2022Article
- Addressing the socioeconomic divide in computational modeling for infectious diseases.Nature communications · 2022Article
- Network effects in influenza spread: The impact of mobility and socio-economic factors.Socio-economic planning sciences · 2021Article
- Toward the use of neural networks for influenza prediction at multiple spatial resolutions.Science advances · 2021Article
- Health inequities in influenza transmission and surveillance.PLoS computational biology · 2021Article
- Optimizing respiratory virus surveillance networks using uncertainty propagation.Nature communications · 2021Article
- Infectious or Recovered? Optimizing the Infectious Disease Detection Process for Epidemic Control and Prevention Based on Social Media.International journal of environmental research and public health · 2020Article
- Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation.JMIR public health and surveillance · 2019Article
- Incidence estimation from sentinel surveillance data; a simulation study and application to data from the Belgian laboratory sentinel surveillance.BMC public health · 2019Article
- Article
- Evaluation of the 2017/18 Influenza Epidemic Season in Poland Based on the SENTINEL Surveillance System.Advances in experimental medicine and biology · 2019Article
- Urbanization and humidity shape the intensity of influenza epidemics in U.S. cities.Science (New York, N.Y.) · 2018Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
The surveillance of influenza activity is critical to early detection of epidemics and pandemics and the design of disease control strategies. Case reporting through a voluntary network of sentinel physicians is a commonly used method of passive surveillance for monitoring rates of influenza-like illness (ILI) worldwide. Despite its ubiquity, little attention has been given to the processes underlying the observation, collection, and spatial aggregation of sentinel surveillance data, and its subsequent effects on epidemiological understanding. We harnessed the high specificity of diagnosis codes in medical claims from a database that represented 2.5 billion visits from upwards of 120,000 United States healthcare providers each year. Among influenza seasons from 2002-2009 and the 2009 pandemic, we simulated limitations of sentinel surveillance systems such as low coverage and coarse spatial resolution, and performed Bayesian inference to probe the robustness of ecological inference and spatial prediction of disease burden. Our models suggest that a number of socio-environmental factors, in addition to local population interactions, state-specific health policies, as well as sampling effort may be responsible for the spatial patterns in U.S. sentinel ILI surveillance. In addition, we find that biases related to spatial aggregation were accentuated among areas with more heterogeneous disease risk, and sentinel systems designed with fixed reporting locations across seasons provided robust inference and prediction. With the growing availability of health-associated big data worldwide, our results suggest mechanisms for optimizing digital data streams to complement traditional surveillance in developed settings and enhance surveillance opportunities in developing countries.
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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.