In:
eLife, eLife Sciences Publications, Ltd, Vol. 4 ( 2015-12-08)
Abstract:
The outbreak of Ebola that started in West Africa in late 2013 has caused at least 28,000 illnesses and 11,000 deaths. As the outbreak progressed, global and local public health authorities scrambled to contain the spread of the disease by isolating those who were ill, putting in place infection control processes in health care settings, and encouraging the public to take steps to prevent the spread of the illness in the community. It took a massive investment of resources and personnel from many countries to eventually bring the outbreak under control. To determine where to allocate people and resources during the outbreak, public health authorities often turned to mathematical models created by scientists to predict the course of the outbreak and identify interventions that could be effective. Many groups of scientists created models of the epidemic using publically available data or data they obtained from government officials or field studies. In some instances, the models yielded valuable insights. But with various groups using different methods and data, the models didn’t always agree on what would happen next or how best to contain the epidemic. Now, Chretien et al. provide an overview of Ebola mathematical modeling during the epidemic and suggest how future efforts may be improved. The overview included 66 published studies about Ebola outbreak models. Although most forecasts predicted many more cases than actually occurred, some modeling approaches produced more accurate predictions, and several models yielded valuable insights. For example, one study found that focusing efforts on isolating patients with the most severe cases of Ebola would help end the epidemic by substantially reducing the number of new infections. Another study used real-time airline data to predict which traveler screening strategies would be most efficient at preventing international spread of Ebola. Furthermore, studies that obtained genomic data showed how specific virus strains were transmitted across geographic areas. Chretien et al. argue that mathematical modeling efforts could be more useful in future pubic health emergencies if modelers cooperated more, and suggest the collaborative approach of weather forecasters as a good example to follow. Greater data sharing and the creation of standards for epidemic modeling would aid better collaboration.
Type of Medium:
Online Resource
ISSN:
2050-084X
DOI:
10.7554/eLife.09186.001
DOI:
10.7554/eLife.09186.002
DOI:
10.7554/eLife.09186.003
DOI:
10.7554/eLife.09186.004
DOI:
10.7554/eLife.09186.005
DOI:
10.7554/eLife.09186.006
DOI:
10.7554/eLife.09186.007
DOI:
10.7554/eLife.09186.008
DOI:
10.7554/eLife.09186.009
DOI:
10.7554/eLife.09186.010
DOI:
10.7554/eLife.09186.011
DOI:
10.7554/eLife.09186.012
DOI:
10.7554/eLife.09186.013
DOI:
10.7554/eLife.09186.014
Language:
English
Publisher:
eLife Sciences Publications, Ltd
Publication Date:
2015
detail.hit.zdb_id:
2687154-3
