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PLoS Comput Biol. 2016 Apr 4;12(4):e1004846. doi: 10.1371/journal.pcbi.1004846. eCollection 2016 Apr.

Identifying Malaria Transmission Foci for Elimination Using Human Mobility Data.

Author information

1
WorldPop Project, Geography and Environment, University of Southampton, Southampton, United Kingdom.
2
Flowminder Foundation, Stockholm, Sweden.
3
Department of Mathematics, Oregon State University, Corvallis, Oregon, United States of America.
4
School of Forest Resources and Conservation, Gainesville, Florida, United States of America.
5
Clinton Health Access Initiative, Boston, Massachusetts, United States of America.
6
Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America.
7
Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America.
8
Sanaria Institute for Global Health and Tropical Medicine, Rockville, Maryland, United States of America.
9
Spatial Epidemiology and Evolution Group, Department of Zoology, University of Oxford, Oxford, United Kingdom.

Abstract

Humans move frequently and tend to carry parasites among areas with endemic malaria and into areas where local transmission is unsustainable. Human-mediated parasite mobility can thus sustain parasite populations in areas where they would otherwise be absent. Data describing human mobility and malaria epidemiology can help classify landscapes into parasite demographic sources and sinks, ecological concepts that have parallels in malaria control discussions of transmission foci. By linking transmission to parasite flow, it is possible to stratify landscapes for malaria control and elimination, as sources are disproportionately important to the regional persistence of malaria parasites. Here, we identify putative malaria sources and sinks for pre-elimination Namibia using malaria parasite rate (PR) maps and call data records from mobile phones, using a steady-state analysis of a malaria transmission model to infer where infections most likely occurred. We also examined how the landscape of transmission and burden changed from the pre-elimination setting by comparing the location and extent of predicted pre-elimination transmission foci with modeled incidence for 2009. This comparison suggests that while transmission was spatially focal pre-elimination, the spatial distribution of cases changed as burden declined. The changing spatial distribution of burden could be due to importation, with cases focused around importation hotspots, or due to heterogeneous application of elimination effort. While this framework is an important step towards understanding progressive changes in malaria distribution and the role of subnational transmission dynamics in a policy-relevant way, future work should account for international parasite movement, utilize real time surveillance data, and relax the steady state assumption required by the presented model.

PMID:
27043913
PMCID:
PMC4820264
DOI:
10.1371/journal.pcbi.1004846
[Indexed for MEDLINE]
Free PMC Article

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