Mapping imported malaria in Bangladesh using parasite genetic and human mobility data

  1. Hsiao-Han Chang
  2. Amy Wesolowski
  3. Ipsita Sinha
  4. Christopher G Jacob
  5. Ayesha Mahmud
  6. Didar Uddin
  7. Sazid Ibna Zaman
  8. Md Amir Hossain
  9. M Abul Faiz
  10. Aniruddha Ghose
  11. Abdullah Abu Sayeed
  12. M Ridwanur Rahman
  13. Akramul Islam
  14. Mohammad Jahirul Karim
  15. M Kamar Rezwan
  16. Abul Khair Mohammad Shamsuzzaman
  17. Sanya Tahmina Jhora
  18. M M Aktaruzzaman
  19. Eleanor Drury
  20. Sonia Gonçalves
  21. Mihir Kekre
  22. Mehul Dhorda
  23. Ranitha Vongpromek
  24. Olivo Miotto
  25. Kenth Engø-Monsen
  26. Dominic Kwiatkowski
  27. Richard J Maude
  28. Caroline Buckee  Is a corresponding author
  1. Harvard T H Chan School of Public Health, United States
  2. Johns Hopkins Bloomberg School of Public Health, United States
  3. Mahidol University, Thailand
  4. Wellcome Sanger Institute, United Kingdom
  5. Chittagong Medical College, Bangladesh
  6. Chittagong Medical College Hospital, Bangladesh
  7. Shaheed Suhrawardy Medical College, Bangladesh
  8. BRAC Centre, Bangladesh
  9. National Malaria Elimination Programme, Bangladesh
  10. World Health Organization, Bangladesh
  11. Directorate General of Health Services, Bangladesh
  12. The WorldWide Antimalarial Resistance Network (WWARN), Thailand
  13. Telenor Group, Norway

Abstract

For countries aiming for malaria elimination, travel of infected individuals between endemic areas undermines local interventions. Quantifying parasite importation has therefore become a priority for national control programs. We analyzed epidemiological surveillance data, travel surveys, parasite genetic data, and anonymized mobile phone data to measure the spatial spread of malaria parasites in southeast Bangladesh. We developed a genetic mixing index to estimate the likelihood of samples being local or imported from parasite genetic data and inferred the direction and intensity of parasite flow between locations using an epidemiological model integrating the travel survey and mobile phone calling data. Our approach indicates that, contrary to dogma, frequent mixing occurs in low transmission regions in the southwest, and elimination will require interventions in addition to reducing imported infections from forested regions. Unlike risk maps generated from clinical case counts alone, therefore, our approach distinguishes areas of frequent importation as well as high transmission.

Data availability

All genetic data are included in Supplementary file 4 and all travel matrices are included in Supplementary file 5.

Article and author information

Author details

  1. Hsiao-Han Chang

    Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  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. Ipsita Sinha

    Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6574-310X
  4. Christopher G Jacob

    Malaria Programme, Wellcome Sanger Institute, Hinxton, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  5. Ayesha Mahmud

    Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Didar Uddin

    Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    Competing interests
    The authors declare that no competing interests exist.
  7. Sazid Ibna Zaman

    Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    Competing interests
    The authors declare that no competing interests exist.
  8. Md Amir Hossain

    Department of Medicine, Chittagong Medical College, Chittagong, Bangladesh
    Competing interests
    The authors declare that no competing interests exist.
  9. M Abul Faiz

    Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    Competing interests
    The authors declare that no competing interests exist.
  10. Aniruddha Ghose

    Chittagong Medical College Hospital, Chittagong, Bangladesh
    Competing interests
    The authors declare that no competing interests exist.
  11. Abdullah Abu Sayeed

    Chittagong Medical College Hospital, Chittagong, Bangladesh
    Competing interests
    The authors declare that no competing interests exist.
  12. M Ridwanur Rahman

    Shaheed Suhrawardy Medical College, Dhaka, Bangladesh
    Competing interests
    The authors declare that no competing interests exist.
  13. Akramul Islam

    BRAC Centre, Dhaka, Bangladesh
    Competing interests
    The authors declare that no competing interests exist.
  14. Mohammad Jahirul Karim

    National Malaria Elimination Programme, Dhaka, Bangladesh
    Competing interests
    The authors declare that no competing interests exist.
  15. M Kamar Rezwan

    Vector-Borne Disease Control, World Health Organization, Dhaka, Bangladesh
    Competing interests
    The authors declare that no competing interests exist.
  16. Abul Khair Mohammad Shamsuzzaman

    Communicable Disease Control, Directorate General of Health Services, Dhaka, Bangladesh
    Competing interests
    The authors declare that no competing interests exist.
  17. Sanya Tahmina Jhora

    Communicable Disease Control, Directorate General of Health Services, Dhaka, Bangladesh
    Competing interests
    The authors declare that no competing interests exist.
  18. M M Aktaruzzaman

    National Malaria Elimination Programme, Dhaka, Bangladesh
    Competing interests
    The authors declare that no competing interests exist.
  19. Eleanor Drury

    Malaria Programme, Wellcome Sanger Institute, Hinxton, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  20. Sonia Gonçalves

    Malaria Programme, Wellcome Sanger Institute, Hinxton, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  21. Mihir Kekre

    Malaria Programme, Wellcome Sanger Institute, Hinxton, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  22. Mehul Dhorda

    Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    Competing interests
    The authors declare that no competing interests exist.
  23. Ranitha Vongpromek

    The WorldWide Antimalarial Resistance Network (WWARN), Bangkok, Thailand
    Competing interests
    The authors declare that no competing interests exist.
  24. Olivo Miotto

    Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
    Competing interests
    The authors declare that no competing interests exist.
  25. Kenth Engø-Monsen

    Telenor Research, Telenor Group, Fornebu, Norway
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1618-7597
  26. Dominic Kwiatkowski

    Malaria Programme, Wellcome Sanger Institute, Hinxton, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  27. Richard J Maude

    Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  28. Caroline Buckee

    Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, United States
    For correspondence
    cbuckee@hsph.harvard.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8386-5899

Funding

National Institute of General Medical Sciences (U54GM088558)

  • Hsiao-Han Chang

Burroughs Wellcome Fund

  • Amy Wesolowski

Bill and Melinda Gates Foundation (CPT000390)

  • Ipsita Sinha
  • Sazid Ibna Zaman
  • Richard J Maude

Medical Research Council (G0600718)

  • Christopher G Jacob
  • Eleanor Drury
  • Sonia Gonçalves
  • Mihir Kekre
  • Dominic Kwiatkowski

National Institute of General Medical Sciences (R35GM124715-02)

  • Caroline Buckee

Bill and Melinda Gates Foundation (OPP1118166)

  • Christopher G Jacob
  • Olivo Miotto
  • Caroline Buckee

Bill and Melinda Gates Foundation (OPP1129596)

  • Ipsita Sinha
  • Sazid Ibna Zaman
  • Richard J Maude

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2019, Chang 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

  • 4,145
    views
  • 603
    downloads
  • 85
    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. Hsiao-Han Chang
  2. Amy Wesolowski
  3. Ipsita Sinha
  4. Christopher G Jacob
  5. Ayesha Mahmud
  6. Didar Uddin
  7. Sazid Ibna Zaman
  8. Md Amir Hossain
  9. M Abul Faiz
  10. Aniruddha Ghose
  11. Abdullah Abu Sayeed
  12. M Ridwanur Rahman
  13. Akramul Islam
  14. Mohammad Jahirul Karim
  15. M Kamar Rezwan
  16. Abul Khair Mohammad Shamsuzzaman
  17. Sanya Tahmina Jhora
  18. M M Aktaruzzaman
  19. Eleanor Drury
  20. Sonia Gonçalves
  21. Mihir Kekre
  22. Mehul Dhorda
  23. Ranitha Vongpromek
  24. Olivo Miotto
  25. Kenth Engø-Monsen
  26. Dominic Kwiatkowski
  27. Richard J Maude
  28. Caroline Buckee
(2019)
Mapping imported malaria in Bangladesh using parasite genetic and human mobility data
eLife 8:e43481.
https://doi.org/10.7554/eLife.43481

Share this article

https://doi.org/10.7554/eLife.43481

Further reading

    1. Epidemiology and Global Health
    Riccardo Spott, Mathias W Pletz ... Christian Brandt
    Research Article

    Given the rapid cross-country spread of SARS-CoV-2 and the resulting difficulty in tracking lineage spread, we investigated the potential of combining mobile service data and fine-granular metadata (such as postal codes and genomic data) to advance integrated genomic surveillance of the pandemic in the federal state of Thuringia, Germany. We sequenced over 6500 SARS-CoV-2 Alpha genomes (B.1.1.7) across 7 months within Thuringia while collecting patients’ isolation dates and postal codes. Our dataset is complemented by over 66,000 publicly available German Alpha genomes and mobile service data for Thuringia. We identified the existence and spread of nine persistent mutation variants within the Alpha lineage, seven of which formed separate phylogenetic clusters with different spreading patterns in Thuringia. The remaining two are subclusters. Mobile service data can indicate these clusters’ spread and highlight a potential sampling bias, especially of low-prevalence variants. Thereby, mobile service data can be used either retrospectively to assess surveillance coverage and efficiency from already collected data or to actively guide part of a surveillance sampling process to districts where these variants are expected to emerge. The latter concept was successfully implemented as a proof-of-concept for a mobility-guided sampling strategy in response to the surveillance of Omicron sublineage BQ.1.1. The combination of mobile service data and SARS-CoV-2 surveillance by genome sequencing is a valuable tool for more targeted and responsive surveillance.

    1. Epidemiology and Global Health
    Marina Padilha, Victor Nahuel Keller ... Gilberto Kac
    Research Article

    Background: The role of circulating metabolites on child development is understudied. We investigated associations between children's serum metabolome and early childhood development (ECD).

    Methods: Untargeted metabolomics was performed on serum samples of 5,004 children aged 6-59 months, a subset of participants from the Brazilian National Survey on Child Nutrition (ENANI-2019). ECD was assessed using the Survey of Well-being of Young Children's milestones questionnaire. The graded response model was used to estimate developmental age. Developmental quotient (DQ) was calculated as the developmental age divided by chronological age. Partial least square regression selected metabolites with a variable importance projection ≥ 1. The interaction between significant metabolites and the child's age was tested.

    Results: Twenty-eight top-ranked metabolites were included in linear regression models adjusted for the child's nutritional status, diet quality, and infant age. Cresol sulfate (β = -0.07; adjusted-p < 0.001), hippuric acid (β = -0.06; adjusted-p < 0.001), phenylacetylglutamine (β = -0.06; adjusted-p < 0.001), and trimethylamine-N-oxide (β = -0.05; adjusted-p = 0.002) showed inverse associations with DQ. We observed opposite directions in the association of DQ for creatinine (for children aged -1 SD: β = -0.05; p =0.01; +1 SD: β = 0.05; p =0.02) and methylhistidine (-1 SD: β = - 0.04; p =0.04; +1 SD: β = 0.04; p =0.03).

    Conclusion: Serum biomarkers, including dietary and microbial-derived metabolites involved in the gut-brain axis, may potentially be used to track children at risk for developmental delays.

    Funding: Supported by the Brazilian Ministry of Health and the Brazilian National Research Council.