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Modeling spatial invasion of Ebola in West Africa.

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Title: Modeling spatial invasion of Ebola in West Africa.
Authors: D'silva, Jeremy P.1, Eisenberg, Marisa C.1 marisae@umich.edu
Source: Journal of Theoretical Biology. Sep2017, Vol. 428, p65-75. 11p.
Subjects: Ebola virus disease, Epidemiology methodology, Spatiotemporal processes, Gravity model (Social sciences), Health policy, Diagnosis, Methodology
Abstract: The 2014–2016 Ebola Virus Disease (EVD) epidemic in West Africa was the largest ever recorded, representing a fundamental shift in Ebola epidemiology with unprecedented spatiotemporal complexity. To understand the spatiotemporal dynamics of EVD in West Africa, we developed spatial transmission models using a gravity-model framework at both the national and district-level scales, which we used to compare effectiveness of local interventions (e.g. local quarantine) and long-range interventions (e.g. border-closures). The country-level gravity model captures the epidemic data, including multiple waves of initial epidemic growth observed in Guinea. We found that local-transmission reductions were most effective in Liberia, while long-range transmission was dominant in Sierra Leone. Both models illustrated that interventions in one region result in an amplified protective effect on other regions by preventing spatial transmission. In the district-level model, interventions in the strongest of these amplifying regions reduced total cases in all three countries by over 20%, in spite of the region itself generating only ∼0.1% of total cases. This model structure and associated intervention analysis provide information that can be used by public health policymakers to assist planning and response efforts for future epidemics. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Theoretical Biology is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Theoretical+Biology%22">Journal of Theoretical Biology</searchLink>. Sep2017, Vol. 428, p65-75. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Ebola+virus+disease%22">Ebola virus disease</searchLink><br /><searchLink fieldCode="DE" term="%22Epidemiology+methodology%22">Epidemiology methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Spatiotemporal+processes%22">Spatiotemporal processes</searchLink><br /><searchLink fieldCode="DE" term="%22Gravity+model+%28Social+sciences%29%22">Gravity model (Social sciences)</searchLink><br /><searchLink fieldCode="DE" term="%22Health+policy%22">Health policy</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnosis%22">Diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Methodology%22">Methodology</searchLink>
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  Data: The 2014–2016 Ebola Virus Disease (EVD) epidemic in West Africa was the largest ever recorded, representing a fundamental shift in Ebola epidemiology with unprecedented spatiotemporal complexity. To understand the spatiotemporal dynamics of EVD in West Africa, we developed spatial transmission models using a gravity-model framework at both the national and district-level scales, which we used to compare effectiveness of local interventions (e.g. local quarantine) and long-range interventions (e.g. border-closures). The country-level gravity model captures the epidemic data, including multiple waves of initial epidemic growth observed in Guinea. We found that local-transmission reductions were most effective in Liberia, while long-range transmission was dominant in Sierra Leone. Both models illustrated that interventions in one region result in an amplified protective effect on other regions by preventing spatial transmission. In the district-level model, interventions in the strongest of these amplifying regions reduced total cases in all three countries by over 20%, in spite of the region itself generating only ∼0.1% of total cases. This model structure and associated intervention analysis provide information that can be used by public health policymakers to assist planning and response efforts for future epidemics. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Theoretical Biology is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.jtbi.2017.05.034
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        Text: English
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        PageCount: 11
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      – SubjectFull: Ebola virus disease
        Type: general
      – SubjectFull: Epidemiology methodology
        Type: general
      – SubjectFull: Spatiotemporal processes
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      – SubjectFull: Gravity model (Social sciences)
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      – SubjectFull: Health policy
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      – SubjectFull: Diagnosis
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      – SubjectFull: Methodology
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              Text: Sep2017
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