Evidence map›Paper›PMID 35718471›Full record

ArticleStatistics in medicine2022

Dynamic downscaling and daily nowcasting from influenza surveillance data.

Rajib Paul, Dan Han, Elise DeDoncker, Diana Prieto

Abstract read
In one paragraph

Article in Statistics in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Rajib PaulDepartment of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, North Carolina, USA.ORCID 0000-0002-5120-7525
Dan HanDepartment of Mathematics, University of Louisville, Louisville, Kentucky, USA.
Elise DeDonckerDepartment of Computer Science, Western Michigan University, Kalamazoo, Michigan, USA.
Diana PrietoCarey School of Business, Johns Hopkins University, Baltimore, Maryland, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Real-time trends from surveillance data are important to assess and develop preparedness for influenza outbreaks. The overwhelming testing demand and limited capacity of testing laboratories for viral positivity render daily confirmed case data inaccurate and delay its availability in preparedness. Using Bayesian dynamic downscaling models, we obtained posterior estimates for daily influenza incidences from weekly estimates of the Centers for Disease Control and Prevention and daily reported constitutional and respiratory complaints during emergency department (ED) visits obtained from the state health departments. Our model provides one-day and seven-day lead forecasts along with 95

Indexed as

Influenza, HumanBayes TheoremDisease OutbreaksEmergency Service, HospitalForecastingHumansBayesian methodsinfluenzaKalman filtersemiparametric modelingtemporal analysis

Identifiers

PMID35718471
PMCPMC9544787

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

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.