Using parasite genetic and human mobility data to infer local and cross-border malaria connectivity in Southern Africa

  1. Sofonias K Tessema  Is a corresponding author
  2. Amy Wesolowski
  3. Anna Chen
  4. Maxwell Murphy
  5. Jordan Wilheim
  6. Anna-Rosa Mupiri
  7. Nick W Ruktanonchai
  8. Victor A Alegana
  9. Andrew J Tatem
  10. Munyaradzi Tambo
  11. Bradley Didier
  12. Justin M Cohen
  13. Adam Bennett
  14. Hugh JW Sturrock
  15. Roland Gosling
  16. Michelle S Hsiang
  17. David L Smith
  18. Davis R Mumbengegwi
  19. Jennifer L Smith
  20. Bryan Greenhouse
  1. University of California, San Francisco, United States
  2. Johns Hopkins Bloomberg School of Public Health, United States
  3. University of Namibia, Namibia
  4. University of Southampton, United Kingdom
  5. Clinton Health Access Initiative, United States
  6. University of Washington, United States
  7. University of Texas Southwestern Medical Center, United States

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.

Article and author information

Author details

  1. Sofonias K Tessema

    Department of Medicine, University of California, San Francisco, San Francisco, United States
    For correspondence
    SofoniasK.Tessema@ucsf.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1057-5310
  2. Amy Wesolowski

    Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6320-3575
  3. Anna Chen

    Department of Medicine, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Maxwell Murphy

    Department of Medicine, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0332-4388
  5. Jordan Wilheim

    Department of Medicine, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Anna-Rosa Mupiri

    Multidisciplinary Research Center, University of Namibia, Windhoek, Namibia
    Competing interests
    The authors declare that no competing interests exist.
  7. Nick W Ruktanonchai

    Geography and Environment, University of Southampton, Southampton, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  8. Victor A Alegana

    Geography and Environment, University of Southampton, Southampton, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  9. Andrew J Tatem

    Geography and Environment, University of Southampton, Southampton, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  10. Munyaradzi Tambo

    Multidisciplinary Research Center, University of Namibia, Windhoek, Namibia
    Competing interests
    The authors declare that no competing interests exist.
  11. Bradley Didier

    Clinton Health Access Initiative, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  12. Justin M Cohen

    Clinton Health Access Initiative, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  13. Adam Bennett

    Institute of Global Health Sciences, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  14. Hugh JW Sturrock

    Institute of Global Health Sciences, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  15. Roland Gosling

    Global Health Group, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  16. Michelle S Hsiang

    Institute of Global Health Sciences, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  17. David L Smith

    Institute of Health Metrics and Evaluation, University of Washington, Seattle, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4367-3849
  18. Davis R Mumbengegwi

    Multidisciplinary Research Center, University of Namibia, Windhoek, Namibia
    Competing interests
    The authors declare that no competing interests exist.
  19. Jennifer L Smith

    Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, United States
    Competing interests
    The authors declare that no competing interests exist.
  20. Bryan Greenhouse

    Department of Medicine, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.

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.

Metrics

  • 3,840
    views
  • 463
    downloads
  • 90
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Sofonias K Tessema
  2. Amy Wesolowski
  3. Anna Chen
  4. Maxwell Murphy
  5. Jordan Wilheim
  6. Anna-Rosa Mupiri
  7. Nick W Ruktanonchai
  8. Victor A Alegana
  9. Andrew J Tatem
  10. Munyaradzi Tambo
  11. Bradley Didier
  12. Justin M Cohen
  13. Adam Bennett
  14. Hugh JW Sturrock
  15. Roland Gosling
  16. Michelle S Hsiang
  17. David L Smith
  18. Davis R Mumbengegwi
  19. Jennifer L Smith
  20. Bryan Greenhouse
(2019)
Using parasite genetic and human mobility data to infer local and cross-border malaria connectivity in Southern Africa
eLife 8:e43510.
https://doi.org/10.7554/eLife.43510

Share this article

https://doi.org/10.7554/eLife.43510

Further reading

  1. Genetic analyses help to pinpoint where people got infected with malaria, enabling better interventions on the ground.

    1. Epidemiology and Global Health
    2. Microbiology and Infectious Disease
    Felix Lankester, Tito J Kibona ... Sarah Cleaveland
    Research Article

    Lack of data on the aetiology of livestock diseases constrains effective interventions to improve livelihoods, food security and public health. Livestock abortion is an important disease syndrome affecting productivity and public health. Several pathogens are associated with livestock abortions but across Africa surveillance data rarely include information from abortions, little is known about aetiology and impacts, and data are not available to inform interventions. This paper describes outcomes from a surveillance platform established in Tanzania spanning pastoral, agropastoral and smallholder systems to investigate causes and impacts of livestock abortion. Abortion events were reported by farmers to livestock field officers (LFO) and on to investigation teams. Events were included if the research team or LFO could attend within 72 hr. If so, samples and questionnaire data were collected to investigate (a) determinants of attribution; (b) patterns of events, including species and breed, previous abortion history, and seasonality; (c) determinants of reporting, investigation and attribution; (d) cases involving zoonotic pathogens. Between 2017–2019, 215 events in cattle (n=71), sheep (n=44), and goats (n=100) were investigated. Attribution, achieved for 19.5% of cases, was significantly affected by delays in obtaining samples. Histopathology proved less useful than PCR due to rapid deterioration of samples. Vaginal swabs provided practical and sensitive material for pathogen detection. Livestock abortion surveillance, even at a small scale, can generate valuable information on causes of disease outbreaks, reproductive losses and can identify pathogens not easily captured through other forms of livestock disease surveillance. This study demonstrated the feasibility of establishing a surveillance system, achieved through engagement of community-based field officers, establishment of practical sample collection and application of molecular diagnostic platforms.