Evidence map›Paper›PMID 31477710›Full record

ArticleNature communications2019

Pareto rules for malaria super-spreaders and super-spreading.

Laura Cooper, Su Yun Kang, Donal Bisanzio, Kilama Maxwell, Isabel Rodriguez-Barraquer, Bryan Greenhouse, Chris Drakeley, Emmanuel Arinaitwe, Sarah G Staedke, Peter W Gething and 8 more

Abstract read
In one paragraph

Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed.

  1. Population heterogeneity in Plasmodium vivax relapse risk.PLoS neglected tropical diseases · 2022
    Trial
  2. Trial
  3. Article
  4. The exact mathematical form of the Pareto principle.Theory in biosciences = Theorie in den Biowissenschaften · 2026
    Article
  5. Article
  6. Rare variation in malaria parasites biases population-genetic inference.bioRxiv : the preprint server for biology · 2026
    Article
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  12. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Laura CooperDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
Su Yun KangOxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Donal BisanzioOxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Kilama MaxwellInfectious Diseases Research Collaboration, Kampala, Uganda.
Isabel Rodriguez-BarraquerDepartment of Epidemiology, Johns Hopkins University, Baltimore, MD, USA.ORCID http://orcid.org/0000-0001-6784-1021
Bryan GreenhouseDepartment of Medicine, University of California, San Francisco, CA, USA.
Chris DrakeleyLondon School of Hygiene & Tropical Medicine, London, UK.ORCID http://orcid.org/0000-0003-4863-075X
Emmanuel ArinaitweInfectious Diseases Research Collaboration, Kampala, Uganda.
Sarah G StaedkeLondon School of Hygiene & Tropical Medicine, London, UK.
Peter W GethingOxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
Philip EckhoffInstitute for Disease Modeling, Bellevue, WA, USA.
Robert C ReinerDepartment of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0003-1056-7919
Simon I HayDepartment of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0002-0611-7272
Grant DorseyDepartment of Medicine, University of California, San Francisco, CA, USA.
Moses R KamyaSchool of Medicine, Makerere University College of Health Sciences, Kampala, Uganda.
Steven W LindsaySchool of Biological and Biomedical Sciences, Durham University, Durham, UK.
Bryan T GrenfellDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
David L SmithDepartment of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA. smitdave@uw.edu.ORCID http://orcid.org/0000-0003-4367-3849

Funding

Transmission ProjectU19AI089674 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MATTHEW G DORSEY, MOSES Robert KAMYA · 2010 to 2026
$29.3M
Scientific and Technical Computing CoreP2CHD047879 · NICHD · PRINCETON UNIVERSITY · PI SALGANIK, MATTHEW J. · 2014 to 2018
$2.0M
NIAID NIH HHS U19 AI089674NICHD NIH HHS P2C HD047879Wellcome Trust
6 · The paper itself

Abstract

Heterogeneity in transmission is a challenge for infectious disease dynamics and control. An 80-20 "Pareto" rule has been proposed to describe this heterogeneity whereby 80% of transmission is accounted for by 20% of individuals, herein called super-spreaders. It is unclear, however, whether super-spreading can be attributed to certain individuals or whether it is an unpredictable and unavoidable feature of epidemics. Here, we investigate heterogeneous malaria transmission at three sites in Uganda and find that super-spreading is negatively correlated with overall malaria transmission intensity. Mosquito biting among humans is 90-10 at the lowest transmission intensities declining to less than 70-30 at the highest intensities. For super-spreaders, biting ranges from 70-30 down to 60-40. The difference, approximately half the total variance, is due to environmental stochasticity. Super-spreading is thus partly due to super-spreaders, but modest gains are expected from targeting super-spreaders.

Indexed as

AlgorithmsModels, TheoreticalAnimalsAnophelesCommunicable DiseasesHumansMalariaMosquito VectorsPlasmodiumStochastic ProcessesUganda

Identifiers

PMID31477710
PMCPMC6718398

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.