Evidence map›Paper›PMID 24516718›Full record

ReviewInterface focus2013

Co-infection and super-infection models in evolutionary epidemiology.

Samuel Alizon

Abstract readReview
In one paragraph

Review in Interface focus, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing 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

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

  1. Pooled it
  2. Pooled it
  3. Article
  4. Concurrent infections byParasitology · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. How do fluctuating ecological dynamics impact the evolution of hosts and parasites?Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023
    Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. A Scoping Review of Drug Epidemic Models.International journal of environmental research and public health · 2022
    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

1 author.

Samuel AlizonLaboratoire MIVEGEC (UMR CNRS 5290, UR IRD 224, UM1, UM2) , 911 avenue Agropolis, BP 64501, 34394 Montpellier Cedex 5 , France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multiple infections are intensively studied because of their consequences for the health of the host but also because they can radically alter the selective pressures acting on parasites. I discuss how multiple infections have been modelled in evolutionary epidemiology. First, I briefly mention within-host models, which are at the root of these epidemiological models. Then, I present the super-infection framework, with an original focus on how the definition of the super-infection function can lead to evolutionary branching. There are several co-infection models and, for each of them, I briefly go through the underlying mathematics (especially the invasion fitness of a mutant strain) and I discuss the biological assumptions they make and the questions they consequently may ask. In particular, I show that a widely used co-infection model should not be invoked for invasion analyses because it confers a frequency-dependent advantage to rare neutral mutants. Finally, I present more recent frameworks, such as the Price equation framework in epidemiology, that can account for increased parasite diversity. To conclude, I discuss some perspectives for the study of multiple infections in evolutionary epidemiology.

Indexed as

adaptive dynamicsepidemiologyevolutionary biologymultiple infectionstrade-offsvirulence

Identifiers

PMID24516718
PMCPMC3915848

What OpenQuestion holds

Textmetadata
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.