Modelling the drivers of the spread of Plasmodium falciparum hrp2 gene deletions in sub-Saharan Africa

  1. Oliver John Watson  Is a corresponding author
  2. Hannah C Slater
  3. Robert Verity
  4. Jonathan B Parr
  5. Melchior K Mwandagalirwa
  6. Antoinette Tshefu
  7. Steven R Meshnick
  8. Azra C Ghani
  1. Imperial College London, United Kingdom
  2. University of North Carolina, United States
  3. University of Kinshasa, Democratic Republic of the Congo

Abstract

Rapid diagnostic tests (RDTs) have transformed malaria diagnosis. The most prevalent P. falciparum RDTs detect histidine-rich protein 2 (PfHRP2). However, pfhrp2 gene deletions yielding false-negative RDTs, first reported in South America in 2010, have been confirmed in Africa and Asia. We developed a mathematical model to explore the potential for RDT-led diagnosis to drive selection of pfhrp2-deleted parasites. Low malaria prevalence and high frequencies of people seeking treatment resulted in the greatest selection pressure. Calibrating our model against confirmed pfhrp2-deletions in the Democratic Republic of Congo, we estimate a starting frequency of 6% pfhrp2-deletion prior to RDT introduction. Furthermore, the patterns observed necessitate a degree of selection driven by the introduction of PfHRP2-based RDT-guided treatment. Combining this with parasite prevalence and treatment coverage estimates, we map the model-predicted spread of pfhrp2-deletion, and identify the geographic regions in which surveillance for pfhrp2-deletion should be prioritised.

Data availability

The following previously published data sets were used

Article and author information

Author details

  1. Oliver John Watson

    Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
    For correspondence
    o.watson15@imperial.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2374-0741
  2. Hannah C Slater

    Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Robert Verity

    Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Jonathan B Parr

    Division of Infectious Diseases, University of North Carolina, Chapel Hill, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Melchior K Mwandagalirwa

    School of Public Health, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
    Competing interests
    The authors declare that no competing interests exist.
  6. Antoinette Tshefu

    School of Public Health, University of Kinshasa, Kinshasa, Democratic Republic of the Congo
    Competing interests
    The authors declare that no competing interests exist.
  7. Steven R Meshnick

    Division of Infectious Diseases, University of North Carolina, Chapel Hill, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Azra C Ghani

    MRC Center for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

Funding

Wellcome (109312/Z/15/Z)

  • Oliver John Watson

National Institute of Allergy and Infectious Diseases (5R01AI107949)

  • Steven R Meshnick

Imperial College London

  • Hannah C Slater

Medical Research Council (MR/N01507X/1)

  • Robert Verity

Department for International Development

  • Azra C Ghani

National Institute of Allergy and Infectious Diseases (5T32AI007151)

  • Jonathan B Parr

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

Copyright

© 2017, Watson 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

  • 2,579
    views
  • 464
    downloads
  • 77
    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. Oliver John Watson
  2. Hannah C Slater
  3. Robert Verity
  4. Jonathan B Parr
  5. Melchior K Mwandagalirwa
  6. Antoinette Tshefu
  7. Steven R Meshnick
  8. Azra C Ghani
(2017)
Modelling the drivers of the spread of Plasmodium falciparum hrp2 gene deletions in sub-Saharan Africa
eLife 6:e25008.
https://doi.org/10.7554/eLife.25008

Share this article

https://doi.org/10.7554/eLife.25008

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.