Impact of seasonal variations in Plasmodium falciparum malaria transmission on the surveillance of pfhrp2 gene deletions

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

Abstract

Ten countries have reported pfhrp2/pfhrp3 gene deletions since the first observation of pfhrp2-deleted parasites in 2012. In a previous study (Watson et al., 2017) we characterised the drivers selecting for pfhrp2/3 deletions, and mapped the regions in Africa with the greatest selection pressure. In February 2018, the World Health Organization issued guidance on investigating suspected false-negative rapid diagnostic tests (RDTs) due to pfhrp2/3 deletions. However, no guidance is provided regarding the timing of investigations. Failure to consider seasonal variation could cause premature decisions to switch to alternative RDTs. In response, we have extended our methods and predict that the prevalence of false-negative RDTs due to pfhrp2/3 deletions is highest when sampling from younger individuals during the beginning of the rainy season. We conclude by producing a map of the regions impacted by seasonal fluctuations in pfhrp2/3 deletions and a database identifying optimum sampling intervals to support malaria control programmes.

Data availability

All data generated are provided within the online database, hosted through a shiny application at https://ojwatson.shinyapps.io/seasonal_hrp2/. The raw data for the application is available within the github repository at https://github.com/OJWatson/hrp2malaRia.

The following previously published data sets were used

Article and author information

Author details

  1. Oliver John Watson

    MRC Center for Outbreak Analysis and Modelling, 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. Robert Verity

    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.
  3. 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.
  4. Tini Garske

    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.
  5. Jane Cunningham

    Global Malaria Programme, World Health Organisation, Geneva, Switzerland
    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. 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.
  8. Steven R Meshnick

    4.Department of Epidemiology, Gillings School for Global Public Health, University of North Carolina, Chapel Hill, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. 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.
  10. Hannah C Slater

    1.MRC Centre for Global Infectious Disease Analysis, 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

Medical Research Council (MR/N01507X/1)

  • Robert Verity

Department for International Development

  • Azra C Ghani

Medical Research Council

  • Tini Garske

National Institute of Allergy and Infectious Diseases (R01AI132547)

  • Steven R Meshnick
  • Jonathan B Parr

American Society for Tropical Medicine and Hygiene-Burroughs Wellcome Fund

  • Jonathan B Parr

Imperial College London

  • Hannah C Slater

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

Copyright

© 2019, 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

  • 1,143
    views
  • 219
    downloads
  • 25
    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. Robert Verity
  3. Azra C Ghani
  4. Tini Garske
  5. Jane Cunningham
  6. Antoinette Tshefu
  7. Melchior K Mwandagalirwa
  8. Steven R Meshnick
  9. Jonathan B Parr
  10. Hannah C Slater
(2019)
Impact of seasonal variations in Plasmodium falciparum malaria transmission on the surveillance of pfhrp2 gene deletions
eLife 8:e40339.
https://doi.org/10.7554/eLife.40339

Share this article

https://doi.org/10.7554/eLife.40339

Further reading

    1. Epidemiology and Global Health
    2. Evolutionary Biology
    Renan Maestri, Benoît Perez-Lamarque ... Hélène Morlon
    Research Article

    Several coronaviruses infect humans, with three, including the SARS-CoV2, causing diseases. While coronaviruses are especially prone to induce pandemics, we know little about their evolutionary history, host-to-host transmissions, and biogeography. One of the difficulties lies in dating the origination of the family, a particularly challenging task for RNA viruses in general. Previous cophylogenetic tests of virus-host associations, including in the Coronaviridae family, have suggested a virus-host codiversification history stretching many millions of years. Here, we establish a framework for robustly testing scenarios of ancient origination and codiversification versus recent origination and diversification by host switches. Applied to coronaviruses and their mammalian hosts, our results support a scenario of recent origination of coronaviruses in bats and diversification by host switches, with preferential host switches within mammalian orders. Hotspots of coronavirus diversity, concentrated in East Asia and Europe, are consistent with this scenario of relatively recent origination and localized host switches. Spillovers from bats to other species are rare, but have the highest probability to be towards humans than to any other mammal species, implicating humans as the evolutionary intermediate host. The high host-switching rates within orders, as well as between humans, domesticated mammals, and non-flying wild mammals, indicates the potential for rapid additional spreading of coronaviruses across the world. Our results suggest that the evolutionary history of extant mammalian coronaviruses is recent, and that cases of long-term virus–host codiversification have been largely over-estimated.

    1. Cancer Biology
    2. Epidemiology and Global Health
    Chelsea L Hansen, Cécile Viboud, Lone Simonsen
    Research Article

    Cancer is considered a risk factor for COVID-19 mortality, yet several countries have reported that deaths with a primary code of cancer remained within historic levels during the COVID-19 pandemic. Here, we further elucidate the relationship between cancer mortality and COVID-19 on a population level in the US. We compared pandemic-related mortality patterns from underlying and multiple cause (MC) death data for six types of cancer, diabetes, and Alzheimer’s. Any pandemic-related changes in coding practices should be eliminated by study of MC data. Nationally in 2020, MC cancer mortality rose by only 3% over a pre-pandemic baseline, corresponding to ~13,600 excess deaths. Mortality elevation was measurably higher for less deadly cancers (breast, colorectal, and hematological, 2–7%) than cancers with a poor survival rate (lung and pancreatic, 0–1%). In comparison, there was substantial elevation in MC deaths from diabetes (37%) and Alzheimer’s (19%). To understand these differences, we simulated the expected excess mortality for each condition using COVID-19 attack rates, life expectancy, population size, and mean age of individuals living with each condition. We find that the observed mortality differences are primarily explained by differences in life expectancy, with the risk of death from deadly cancers outcompeting the risk of death from COVID-19.