Evidence map›Paper›PMID 27711149›Full record

ArticlePloS one2016

Spatial Effects on the Multiplicity of Plasmodium falciparum Infections.

Stephan Karl, Michael T White, George J Milne, David Gurarie, Simon I Hay, Alyssa E Barry, Ingrid Felger, Ivo Mueller

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 2 pooled it
–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

20 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Population genetics ofFrontiers in genetics · 2025
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  7. Measurably recombining malaria parasites.Trends in parasitology · 2023
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  9. PNAS nexus · 2022
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  15. Review
  16. Critical transitions in malaria transmission models are consistently generated by superinfection.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019
    Article
  17. Molecular Surveillance ofThe American journal of tropical medicine and hygiene · 2018
    Article
  18. Article
  19. Article
  20. Article
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

8 authors.

Stephan KarlPopulation-Based Biology Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
Michael T WhitePopulation-Based Biology Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
George J MilneSchool of Computer Science and Software Engineering, The University of Western Australia, Perth, WA, Australia.
David GurarieDepartment of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, Ohio, United States of America.
Simon I HayInstitute for Health Metrics and Evaluation, Seattle, Washington, United States of America.
Alyssa E BarryPopulation-Based Biology Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
Ingrid FelgerDepartment of Medical Parasitology and Infection Biology Swiss Tropical and Public Health Institute, Basel, Switzerland.
Ivo MuellerPopulation-Based Biology Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As malaria is being pushed back on many frontiers and global case numbers are declining, accurate measurement and prediction of transmission becomes increasingly difficult. Low transmission settings are characterised by high levels of spatial heterogeneity, which stands in stark contrast to the widely used assumption of spatially homogeneous transmission used in mathematical transmission models for malaria. In the present study an individual-based mathematical malaria transmission model that incorporates multiple parasite clones, variable human exposure and duration of infection, limited mosquito flight distance and most importantly geographically heterogeneous human and mosquito population densities was used to illustrate the differences between homogeneous and heterogeneous transmission assumptions when aiming to predict surrogate indicators of transmission intensity such as population parasite prevalence or multiplicity of infection (MOI). In traditionally highly malaria endemic regions where most of the population harbours malaria parasites, humans are often infected with multiple parasite clones. However, studies have shown also in areas with low overall parasite prevalence, infection with multiple parasite clones is a common occurrence. Mathematical models assuming homogeneous transmission between humans and mosquitoes cannot explain these observations. Heterogeneity of transmission can arise from many factors including acquired immunity, body size and occupational exposure. In this study, we show that spatial heterogeneity has a profound effect on predictions of MOI and parasite prevalence. We illustrate, that models assuming homogeneous transmission underestimate average MOI in low transmission settings when compared to field data and that spatially heterogeneous models predict stable transmission at much lower overall parasite prevalence. Therefore it is very important that models used to guide malaria surveillance and control strategies in low transmission and elimination settings take into account the spatial features of the specific target area, including human and mosquito vector distribution.

Indexed as

Spatial AnalysisAllelesAnimalsAntigens, ProtozoanCulicidaeHumansInsect VectorsMalaria, FalciparumModels, StatisticalPlasmodium falciparumProtozoan ProteinsAntigens, Protozoanmerozoite surface protein 2, PlasmodiumProtozoan Proteins

Identifiers

PMID27711149
PMCPMC5053403

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.