ArticleAmerican journal of epidemiology2009
Optimizing influenza sentinel surveillance at the state level.
Article in American journal of epidemiology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed, 45 citations in OpenAlex.
- An integrated framework for modelling respiratory disease transmission and designing surveillance networks using a sentinel index.Royal Society open science · 2025Article
- Information-guided adaptive learning approach for active surveillance of infectious diseases.Infectious Disease Modelling · 2025Article
- Pandemic monitoring with global aircraft-based wastewater surveillance networks.Nature medicine · 2025Article
- Optimization and performance analytics of global aircraft-based wastewater surveillance networks.medRxiv : the preprint server for health sciences · 2024Article
- Evaluation of the design of the influenza-like illness sentinel surveillance system in Brazil.Cadernos de saude publica · 2024Article
- A comparative study of influenza surveillance systems and administrative data in England during the 2022-2023 season.PLOS global public health · 2024Article
- Optimizing respiratory virus surveillance networks using uncertainty propagation.Nature communications · 2021Article
- The DIOS framework for optimizing infectious disease surveillance: Numerical methods for simulation and multi-objective optimization of surveillance network architectures.PLoS computational biology · 2020Article
- Applying Machine Learning Models with An Ensemble Approach for Accurate Real-Time Influenza Forecasting in Taiwan: Development and Validation Study.Journal of medical Internet research · 2020Article
- Socioeconomic bias in influenza surveillance.PLoS computational biology · 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
- Optimal multi-source forecasting of seasonal influenza.PLoS computational biology · 2018Article
- Deploying digital health data to optimize influenza surveillance at national and local scales.PLoS computational biology · 2018Article
- Design Strategies for Efficient Arbovirus Surveillance.Emerging infectious diseases · 2017Article
- Improving incidence estimation in practice-based sentinel surveillance networks using spatial variation in general practitioner density.BMC medical research methodology · 2016Article
- Optimizing the precision of case fatality ratio estimates under the surveillance pyramid approach.American journal of epidemiology · 2014Article
- Optimal sampling strategies for detecting zoonotic disease epidemics.PLoS computational biology · 2014Article
- Improving disease incidence estimates in primary care surveillance systems.Population health metrics · 2014Article
- Commuter mobility and the spread of infectious diseases: application to influenza in France.PloS one · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Influenza-like illness data are collected via an Influenza Sentinel Provider Surveillance Network at the state level. Because participation is voluntary, locations of the sentinel providers may not reflect optimal geographic placement. The purpose of this study was to determine the "best" locations for sentinel providers in Iowa by using a maximal coverage model (MCM) and to compare the population coverage obtained with that of the current sentinel network. The authors used an MCM to maximize the Iowa population located within 20 miles (32.2 km) of 1-143 candidate sites and calculated the coverage provided by each additional site. The first MCM location covered 15% of the population; adding a second increased coverage to 25%. Additional locations provided more coverage but with diminishing marginal returns. In contrast, the existing 22 Iowa sentinel locations covered 56% of the population, the same coverage achieved with just 10 MCM sites. Using 22 MCM sites covered more than 75% of the population, an improvement over the current site placement, adding nearly 600,000 Iowa residents. Given scarce public health resources, MCMs can help surveillance efforts by prioritizing recruitment of sentinel locations.
Indexed as
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.