ArticlePloS one2011
Estimating incidence curves of several infections using symptom surveillance data.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Article
- Correlation-Based Discovery of Disease Patterns for Syndromic Surveillance.Frontiers in big data · 2021Article
- Unsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptoms.PLoS computational biology · 2019Article
- The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison.Journal of biomedical informatics · 2018Article
- Utilizing syndromic surveillance data for estimating levels of influenza circulation.American journal of epidemiology · 2014Article
- A Simulation Optimization Approach to Epidemic Forecasting.PloS one · 2013Article
- Vaccine allocation in a declining epidemic.Journal of the Royal Society, Interface · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
We introduce a method for estimating incidence curves of several co-circulating infectious pathogens, where each infection has its own probabilities of particular symptom profiles. Our deconvolution method utilizes weekly surveillance data on symptoms from a defined population as well as additional data on symptoms from a sample of virologically confirmed infectious episodes. We illustrate this method by numerical simulations and by using data from a survey conducted on the University of Michigan campus. Last, we describe the data needs to make such estimates accurate.
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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.