Evidence map›Paper›PMID 27983501›Full record

ArticleEmerging infectious diseases2017

A Framework for Modeling Emerging Diseases to Inform Management.

Robin E Russell, Rachel A Katz, Katherine L D Richgels, Daniel P Walsh, Evan H C Grant

Abstract read
In one paragraph

Article in Emerging infectious diseases, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
–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

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Quantitative support for the benefits of proactive management for wildlife disease control.Conservation biology : the journal of the Society for Conservation Biology · 2025
    Article
  5. Review
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Applied and environmental microbiology · 2021
    Article
  12. Article
  13. Article
  14. Disease-structuredEcology and evolution · 2019
    Article
  15. Article
  16. Review
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.

Robin E Russell
Rachel A Katz
Katherine L D Richgels
Daniel P Walsh
Evan H C Grant

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid emergence and reemergence of zoonotic diseases requires the ability to rapidly evaluate and implement optimal management decisions. Actions to control or mitigate the effects of emerging pathogens are commonly delayed because of uncertainty in the estimates and the predicted outcomes of the control tactics. The development of models that describe the best-known information regarding the disease system at the early stages of disease emergence is an essential step for optimal decision-making. Models can predict the potential effects of the pathogen, provide guidance for assessing the likelihood of success of different proposed management actions, quantify the uncertainty surrounding the choice of the optimal decision, and highlight critical areas for immediate research. We demonstrate how to develop models that can be used as a part of a decision-making framework to determine the likelihood of success of different management actions given current knowledge.

Indexed as

Decision Support TechniquesDisease ManagementAnimalsClinical Decision-MakingCommunicable Diseases, EmergingHumansUncertaintyUnited StatesZoonosesdecision analysisemerging diseasesinfectious diseasesmathematical modellingmodel developmentone healthpredictive modellingzoonoseszoonotic diseases

Identifiers

PMID27983501
PMCPMC5176225

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.