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Hitting a moving target: a model for malaria elimination in the presence of population movement

Abstract:
Abstract South Africa is committed to eliminating malaria with a goal of zero local transmission by 2018. Malaria elimination strategies may be unsuccessful if they focus only on vector biology, and ignore the mobility patterns of humans, particularly where the majority of infections are imported. In the first study in Mpumalanga Province in South Africa designed for this purpose, a metapopulation model is developed to assess the impact of their proposed elimination-focused policy interventions. A stochastic, non-linear, ordinary-differential equation model is fitted to malaria data from Mpumalanga and neighbouring Maputo Province in Mozambique. Further scaling-up of vector control is predicted to lead to a minimal reduction in local infections, while mass drug administration and focal screening and treatment at the Mpumalanga-Maputo border are predicted to have only a short-lived impact. Source reduction in Maputo Province is predicted to generate large reductions in local infections through stemming imported infections. The mathematical model predicts malaria elimination to be possible only when imported infections are treated before entry or eliminated at the source suggesting that a regionally focused strategy appears needed, for achieving malaria elimination in Mpumalanga and South Africa.
Publication status:
Published
Peer review status:
Peer reviewed

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Role:
Author
ORCID:
0000-0002-5547-820X
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Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
Tropical Medicine
Oxford college:
Wadham College
Role:
Author
ORCID:
0000-0002-6523-185X


Publisher:
Public Library of Science (PLoS)
Journal:
PloS one More from this journal
Issue:
12
Pages:
e0144990
Publication date:
2015-01-01
Acceptance date:
2015-11-25
DOI:
ISSN:
1932-6203
Pmid:
26689547


Language:
English
Keywords:
Pubs id:
pubs:584188
UUID:
uuid:5578a20c-621c-4425-9582-02cdcb0df53a
Local pid:
pubs:584188
Source identifiers:
584188
Deposit date:
2019-02-05
ARK identifier:

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