Literature DB >> 27387097

Modeling in Real Time During the Ebola Response.

Martin I Meltzer1, 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.   

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).

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Year:  2016        PMID: 27387097     DOI: 10.15585/mmwr.su6503a12

Source DB:  PubMed          Journal:  MMWR Suppl        ISSN: 2380-8942


  5 in total

1.  The RAPIDD ebola forecasting challenge: Synthesis and lessons learnt.

Authors:  Cécile Viboud; Kaiyuan Sun; Robert Gaffey; Marco Ajelli; Laura Fumanelli; Stefano Merler; Qian Zhang; Gerardo Chowell; Lone Simonsen; Alessandro Vespignani
Journal:  Epidemics       Date:  2017-08-26       Impact factor: 4.396

Review 2.  Development and dissemination of infectious disease dynamic transmission models during the COVID-19 pandemic: what can we learn from other pathogens and how can we move forward?

Authors:  Alexander D Becker; Kyra H Grantz; Sonia T Hegde; Sophie Bérubé; Derek A T Cummings; Amy Wesolowski
Journal:  Lancet Digit Health       Date:  2020-12-07

3.  Improving early epidemiological assessment of emerging Aedes-transmitted epidemics using historical data.

Authors:  Julien Riou; Chiara Poletto; Pierre-Yves Boëlle
Journal:  PLoS Negl Trop Dis       Date:  2018-06-04

4.  How decision makers can use quantitative approaches to guide outbreak responses.

Authors:  Oliver Morgan
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2019-07-08       Impact factor: 6.237

5.  Comparative performance study of three Ebola rapid diagnostic tests in Guinea.

Authors:  Zelda Moran; William Rodriguez; Doré Ahmadou; Barré Soropogui; N' Faly Magassouba; Cassandra Kelly-Cirino; Yanis Ben Amor
Journal:  BMC Infect Dis       Date:  2020-09-15       Impact factor: 3.090

  5 in total

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