Evidence map›Paper›PMID 27387097›Full record

ArticleMMWR supplements2016

Modeling in Real Time During the Ebola Response.

Martin I Meltzer, Scott Santibanez, Leah S Fischer, Toby L Merlin, Bishwa B Adhikari, Charisma Y Atkins, Caresse Campbell, Isaac Chun-Hai Fung, Manoj Gambhir, Thomas Gift and 10 more

Open access · diamondAbstract read
PubMed Publisher
In one paragraph

Article in MMWR supplements, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.2field-weighted citation impact, top 13% of its field
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

11 citing papers in PubMed, 23 citations in OpenAlex.

  1. Article
  2. Coordinating collaborative infectious disease modeling projects with the hubverse.medRxiv : the preprint server for health sciences · 2025
    Article
  3. Article
  4. Article
  5. Stabilizing the return to normal behavior in an epidemic.medRxiv : the preprint server for health sciences · 2023
    Article
  6. Review
  7. Article
  8. Article
  9. How decision makers can use quantitative approaches to guide outbreak responses.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019
    Article
  10. Article
  11. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

20 authors at 2 institutions in 1 country.

Martin I MeltzerDivision of Preparedness and Emerging Infections, National Center for Emerging and Zoonotic Infectious Diseases, CDC.
Scott Santibanez
Leah S Fischer
Toby L Merlin
Bishwa B Adhikari
Charisma Y Atkins
Caresse Campbell
Isaac Chun-Hai Fung
Manoj Gambhir
Thomas Gift
Bradford Greening
Weidong Gu
Evin U Jacobson
Emily B Kahn
Cristina Carias
Lina Nerlander
Gabriel Rainisch
Manjunath Shankar
Karen Wong
Michael L Washington
National Center for Emerging and Zoonotic Infectious Diseases · USNational Center for HIV/AIDS Viral Hepatitis STD and TB Prevention · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To aid decision-making during CDC's response to the 2014-2016 Ebola virus disease (Ebola) epidemic in West Africa, CDC activated a Modeling Task Force to generate estimates on various topics related to the response in West Africa and the risk for importation of cases into the United States. Analysis of eight Ebola response modeling projects conducted during August 2014-July 2015 provided insight into the types of questions addressed by modeling, the impact of the estimates generated, and the difficulties encountered during the modeling. This time frame was selected to cover the three phases of the West African epidemic curve. Questions posed to the Modeling Task Force changed as the epidemic progressed. Initially, the task force was asked to estimate the number of cases that might occur if no interventions were implemented compared with cases that might occur if interventions were implemented; however, at the peak of the epidemic, the focus shifted to estimating resource needs for Ebola treatment units. Then, as the epidemic decelerated, requests for modeling changed to generating estimates of the potential number of sexually transmitted Ebola cases. Modeling to provide information for decision-making during the CDC Ebola response involved limited data, a short turnaround time, and difficulty communicating the modeling process, including assumptions and interpretation of results. Despite these challenges, modeling yielded estimates and projections that public health officials used to make key decisions regarding response strategy and resources required. The impact of modeling during the Ebola response demonstrates the usefulness of modeling in future responses, particularly in the early stages and when data are scarce. Future modeling can be enhanced by planning ahead for data needs and data sharing, and by open communication among modelers, scientists, and others to ensure that modeling and its limitations are more clearly understood. The activities summarized in this report would not have been possible without collaboration with many U.S. and international partners (http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/partners.html).

Indexed as

Models, TheoreticalAfrica, WesternCenters for Disease Control and Prevention, U.S.EpidemicsForecastingHemorrhagic Fever, EbolaHumansInternational CooperationProfessional RoleUnited States

Identifiers

PMID27387097
OpenAlexW2470910144

What OpenQuestion holds

Textmetadata
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.