Using parasite genetic and human mobility data to infer local and cross-border malaria connectivity in Southern Africa
Abstract
Local and cross-border importation remain major challenges to malaria elimination and are difficult to measure using traditional surveillance data. To address this challenge, we systematically collected parasite genetic data and travel history from thousands of malaria cases across northeastern Namibia and estimated human mobility from mobile phone data. We observed strong fine-scale spatial structure in local parasite populations, providing positive evidence that the majority of cases were due to local transmission. This result was largely consistent with estimates from mobile phone and travel history data. However, genetic data identified more detailed and extensive evidence of parasite connectivity over hundreds of kilometers than the other data, within Namibia and across the Angolan and Zambian borders. Our results provide a framework for incorporating genetic data into malaria surveillance and provide evidence that both strengthening of local interventions and regional coordination are likely necessary to eliminate malaria in this region of Southern Africa.
Data availability
All data generated or analyzed during this study are included in the manuscript and supplementary files.
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Funding
Bill and Melinda Gates Foundation
- Sofonias K Tessema
- Bryan Greenhouse
Burroughs Wellcome Fund
- Amy Wesolowski
National Institutes of Health
- Amy Wesolowski
Chan Zuckerberg Biohub
- Bryan Greenhouse
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: Ethical approval for the study was obtained from the Institutional Review Boards of the University of Namibia and the University of California, San Francisco (Identification numbers 15-17422 and 14-14576). Informed consent was obtained from all participants or the parents of all children participated in the Zambezi study. For the Kavango study, IRB approval was obtained but no informed consent was collected as all samples (used RDTs) and de-identified data were collected during routine surveillance.
Copyright
© 2019, Tessema et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Further reading
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Genetic analyses help to pinpoint where people got infected with malaria, enabling better interventions on the ground.
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- Epidemiology and Global Health
- Microbiology and Infectious Disease
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