Journal
PLOS BIOLOGY
Volume 13, Issue 1, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pbio.1002057
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Categories
Funding
- NSF [1414374, 1318788 III]
- NSF-NIH-USDA Ecology and Evolution of Infectious Diseases program
- UK Biotechnology and Biological Sciences Research Council [BB/M008894/1]
- NSF-IIS RAPID [1518939]
- Fogarty International Center, US National Institutes of Health
- Japan Science and Technology Agency CREST project
- Japanese Society for the Promotion of Science (JSPS) KAKENHI [26700028]
- St Luke'
- s Life Science Institute Research Grant for Clinical Epidemiology Research
- Commissioned Research program of the Ministry of Health, Labour and Welfare, Japan [H26-ShinkoJitsuyoka-General-016]
- BBSRC [BB/M008894/1] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/M008894/1] Funding Source: researchfish
- Grants-in-Aid for Scientific Research [26700028] Funding Source: KAKEN
- Direct For Biological Sciences
- Division Of Environmental Biology [1414374] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1318788] Funding Source: National Science Foundation
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Carefully calibrated transmission models have the potential to guide public health officials on the nature and scale of the interventions required to control epidemics. In the context of the ongoing Ebola virus disease (EVD) epidemic in Liberia, Drake and colleagues, in this issue of PLOS Biology, employed an elegant modeling approach to capture the distributions of the number of secondary cases that arise in the community and health care settings in the context of changing population behaviors and increasing hospital capacity. Their findings underscore the role of increasing the rate of safe burials and the fractions of infectious individuals who seek hospitalization together with hospital capacity to achieve epidemic control. However, further modeling efforts of EVD transmission and control in West Africa should utilize the spatial-temporal patterns of spread in the region by incorporating spatial heterogeneity in the transmission process. Detailed datasets are urgently needed to characterize temporal changes in population behaviors, contact networks at different spatial scales, population mobility patterns, adherence to infection control measures in hospital settings, and hospitalization and reporting rates.
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