Evidence map›Paper›PMID 39174027›Full record

ArticleEmerging infectious diseases2024

Optimizing Disease Outbreak Forecast Ensembles.

Spencer J Fox, Minsu Kim, Lauren Ancel Meyers, Nicholas G Reich, Evan L Ray

Abstract read
In one paragraph

Article in Emerging infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Coordinating collaborative infectious disease modeling projects with the hubverse.medRxiv : the preprint server for health sciences · 2025
    Article
  8. Article
  9. Article
  10. 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

5 authors.

Spencer J Fox
Minsu Kim
Lauren Ancel Meyers
Nicholas G Reich
Evan L Ray

Funding

Influenza Forecasting Center of Excellence at University of Massachusetts AmherstU01IP001122 · IP · UNIVERSITY OF MASSACHUSETTS AMHERST · PI REICH, NICHOLAS G · 2019 to 2023
$5.4M
Statistical methods for real-time forecasts of infectious disease: expanding dynamic time-series and machine learning approaches for pandemic scenariosR35GM119582 · NIGMS · UNIVERSITY OF MASSACHUSETTS AMHERST · PI Nicholas G Reich · 2016 to 2026
$4.8M
NCIRD CDC HHS U01 IP001122NIGMS NIH HHS R35 GM119582
6 · The paper itself

Abstract

On the basis of historical influenza and COVID-19 forecasts, we found that more than 3 forecast models are needed to ensure robust ensemble accuracy. Additional models can improve ensemble performance, but with diminishing accuracy returns. This understanding will assist with the design of current and future collaborative infectious disease forecasting efforts.

Indexed as

COVID-19Disease OutbreaksForecastingInfluenza, HumanSARS-CoV-2Epidemiological ModelsHumansModels, StatisticalCOVID-19disease modelsensemble forecastinghospitalizationsInfectious disease forecastinginfluenzaoutbreakUnited Statesviruses

Identifiers

PMID39174027
PMCPMC11347000

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.