Evidence map›Paper›PMID 38455332›Full record

ArticleFrontiers in epidemiology2022

The many definitions of multiplicity of infection.

Kristan Alexander Schneider, Henri Christian Junior Tsoungui Obama, George Kamanga, Loyce Kayanula, Nessma Adil Mahmoud Yousif

Open access · diamondAbstract read
In one paragraph

Article in Frontiers in epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
3.2field-weighted citation impact, top 8% of its field
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

13 citing papers in PubMed, 17 citations in OpenAlex.

  1. Article
  2. A Novel Lytic Podovirus AP-20-A InfectingInternational journal of molecular sciences · 2026
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  6. Observational
  7. Pathogens (Basel, Switzerland) · 2025
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  8. Article
  9. Review
  10. Soil Giant Phage: Genome and Biological Characteristics ofInternational journal of molecular sciences · 2024
    Article
  11. Article
  12. Article
  13. Evolutionary genetics of malaria.Frontiers in genetics · 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

5 authors at 1 institution in 1 country.

Kristan Alexander SchneiderDepartment of Applied Computer- and Biosciences, University of Applied Sciences, Mittweida, Germany.
Henri Christian Junior Tsoungui ObamaDepartment of Applied Computer- and Biosciences, University of Applied Sciences, Mittweida, Germany.
George KamangaDepartment of Applied Computer- and Biosciences, University of Applied Sciences, Mittweida, Germany.
Loyce KayanulaDepartment of Applied Computer- and Biosciences, University of Applied Sciences, Mittweida, Germany.
Nessma Adil Mahmoud YousifDepartment of Applied Computer- and Biosciences, University of Applied Sciences, Mittweida, Germany.
Hochschule Mittweida · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The presence of multiple genetically different pathogenic variants within the same individual host is common in infectious diseases. Although this is neglected in some diseases, it is well recognized in others like malaria, where it is typically referred to as multiplicity of infection (MOI) or complexity of infection (COI). In malaria, with the advent of molecular surveillance, data is increasingly being available with enough resolution to capture MOI and integrate it into molecular surveillance strategies. The distribution of MOI on the population level scales with transmission intensities, while MOI on the individual level is a confounding factor when monitoring haplotypes of particular interests, e.g., those associated with drug-resistance. Particularly, in high-transmission areas, MOI leads to a discrepancy between the likelihood of a haplotype being observed in an infection (prevalence) and its abundance in the pathogen population (frequency). Despite its importance, MOI is not universally defined. Competing definitions vary from verbal ones to those based on concise statistical frameworks. Heuristic approaches to MOI are popular, although they do not mine the full potential of available data and are typically biased, potentially leading to misinferences. We introduce a formal statistical framework and suggest a concise definition of MOI and its distribution on the host-population level. We show how it relates to alternative definitions such as the number of distinct haplotypes within an infection or the maximum number of alleles detectable across a set of genetic markers. It is shown how alternatives can be derived from the general framework. Different statistical methods to estimate the distribution of MOI and pathogenic variants at the population level are discussed. The estimates can be used as plug-ins to reconstruct the most probable MOI of an infection and set of infecting haplotypes in individual infections. Furthermore, the relation between prevalence of pathogenic variants and their frequency (relative abundance) in the pathogen population in the context of MOI is clarified, with particular regard to seasonality in transmission intensities. The framework introduced here helps to guide the correct interpretation of results emerging from different definitions of MOI. Especially, it excels comparisons between studies based on different analytical methods.

Indexed as

co-infectioncomplexity of infection (COI)haplotype phasingmixed-species infectionMOIprevalencesuper-infectiontransmission intensities

Identifiers

PMID38455332
PMCPMC10910904
OpenAlexW4302283904

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.