1. Epidemiology and Global Health
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Determinants of MDA impact and designing MDAs towards malaria elimination

  1. Bo Gao
  2. Sompob Saralamba
  3. Yoel Lubell
  4. Lisa J White
  5. Arjen M Dondorp
  6. Ricardo Aguas  Is a corresponding author
  1. University of Oxford, United Kingdom
  2. Mahidol University, Thailand
Research Article
  • Cited 2
  • Views 767
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Cite this article as: eLife 2020;9:e51773 doi: 10.7554/eLife.51773

Abstract

Malaria remains at the forefront of scientific research and global political and funding agendas. Malaria models have consistently oversimplified how mass interventions are implemented. Here, we present an individual based, spatially explicit model of P. falciparum malaria transmission that includes all the programmatic implementation details of mass drug administration (MDA) campaigns. We uncover how the impact of MDA campaigns is determined by the interaction between implementation logistics, patterns of human mobility and how transmission risk is distributed over space. Our results indicate that malaria elimination is only realistically achievable in settings with very low prevalence and can be hindered by spatial heterogeneities in risk. In highly mobile populations, accelerating MDA implementation increases likelihood of elimination; if populations are more static, deploying less teams would be cost optimal. We conclude that mass drug interventions can be an invaluable tool towards malaria elimination in low endemicity areas, specifically when paired with effective vector control.

Data availability

The study presented here is purely theoretical and no data has been used apart from previously (and publicly available) data

Article and author information

Author details

  1. Bo Gao

    Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  2. Sompob Saralamba

    Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
    Competing interests
    The authors declare that no competing interests exist.
  3. Yoel Lubell

    Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
    Competing interests
    The authors declare that no competing interests exist.
  4. Lisa J White

    Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  5. Arjen M Dondorp

    Mahidol Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, 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:" 0000-0001-5190-2395
  6. Ricardo Aguas

    Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    For correspondence
    ricardo@tropmedres.ac
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6507-6597

Funding

Bill and Melinda Gates Foundation (OPP1110500)

  • Sompob Saralamba
  • Yoel Lubell
  • Lisa J White
  • Ricardo Aguas

Bill and Melinda Gates Foundation (OPP1193472)

  • Bo Gao
  • Ricardo Aguas

Wellcome

  • Yoel Lubell
  • Lisa J White
  • Arjen M Dondorp

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

Reviewing Editor

  1. Eduardo Franco, McGill University, Canada

Publication history

  1. Received: September 10, 2019
  2. Accepted: April 12, 2020
  3. Accepted Manuscript published: April 15, 2020 (version 1)
  4. Version of Record published: April 27, 2020 (version 2)

Copyright

© 2020, Gao 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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    Nightingale Health UK Biobank Initiative et al.
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    Biomarkers of low-grade inflammation have been associated with susceptibility to a severe infectious disease course, even when measured prior to disease onset. We investigated whether metabolic biomarkers measured by nuclear magnetic resonance (NMR) spectroscopy could be associated with susceptibility to severe pneumonia (2507 hospitalised or fatal cases) and severe COVID-19 (652 hospitalised cases) in 105,146 generally healthy individuals from UK Biobank, with blood samples collected 2007–2010. The overall signature of metabolic biomarker associations was similar for the risk of severe pneumonia and severe COVID-19. A multi-biomarker score, comprised of 25 proteins, fatty acids, amino acids and lipids, was associated equally strongly with enhanced susceptibility to severe COVID-19 (odds ratio 2.9 [95%CI 2.1–3.8] for highest vs lowest quintile) and severe pneumonia events occurring 7–11 years after blood sampling (2.6 [1.7–3.9]). However, the risk for severe pneumonia occurring during the first 2 years after blood sampling for people with elevated levels of the multi-biomarker score was over four times higher than for long-term risk (8.0 [4.1–15.6]). If these hypothesis generating findings on increased susceptibility to severe pneumonia during the first few years after blood sampling extend to severe COVID-19, metabolic biomarker profiling could potentially complement existing tools for identifying individuals at high risk. These results provide novel molecular understanding on how metabolic biomarkers reflect the susceptibility to severe COVID-19 and other infections in the general population.

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    Background There are multiple known associations between the ABO and RhD blood groups and disease. No systematic population-based studies elucidating associations between a large number of disease categories and blood group have been conducted.

    Methods Using SCANDAT3-S, a comprehensive nationwide blood donation-transfusion database, we modelled outcomes for 1,217 disease categories including 70 million person-years of follow-up, accruing from 5.1 million individuals.

    Results We discovered 49 and 1 associations between a disease and ABO and RhD blood group, respectively, after adjustment for multiple testing. We identified new associations such as kidney stones and blood group B as compared to O. We also expanded previous knowledge on other associations such as pregnancy-induced hypertension and blood group A and AB as compared to O and RhD positive as compared to negative.

    Conclusion Our findings generate strong further support for previously known associations, but also indicate new interesting relations.

    Funding Swedish Research Council.