The many definitions of multiplicity of infection

Kristan Alexander Schneider1, Henri Christian Junior Tsoungui Obama1, George Kamanga1

  • 1Department of Applied Computer- and Biosciences, University of Applied Sciences, Mittweida, Germany.

PubMed

Insights

This study introduces a new statistical framework to precisely define and measure the multiplicity of infection (MOI), crucial for understanding infectious disease dynamics and drug resistance in malaria. The framework clarifies MOI's impact on variant prevalence and frequency, improving molecular surveillance accuracy.

Area of Science:

  • Epidemiology
  • Infectious Diseases
  • Malariology
  • Statistical Genetics

Background:

  • Multiple genetically distinct pathogen strains within a single host (multiplicity of infection, MOI) are common in infectious diseases, notably malaria.
  • MOI complicates molecular surveillance, especially in high-transmission areas, by creating discrepancies between observed variant prevalence and actual pathogen frequency.
  • Existing definitions of MOI are inconsistent, ranging from verbal descriptions to statistical methods, leading to potential misinterpretations.

Purpose of the Study:

  • To introduce a formal statistical framework for defining and quantifying MOI and its population-level distribution.
  • To clarify the relationship between MOI, variant prevalence, and frequency, particularly in the context of malaria transmission seasonality.
  • To provide a unified approach for interpreting MOI data and comparing results across studies using different analytical methods.

Main Methods:

  • Development of a formal statistical framework for MOI definition and estimation.
  • Exploration of relationships between the proposed MOI definition and alternative measures (e.g., number of distinct haplotypes, maximum detectable alleles).
  • Discussion of statistical methods for estimating MOI distribution and pathogenic variants at the population level.

Main Results:

  • A concise definition of MOI and its population-level distribution is proposed.
  • The framework demonstrates how alternative MOI definitions can be derived from the general model.
  • Methods for estimating MOI and pathogenic variants are presented, enabling reconstruction of infection composition.

Conclusions:

  • The introduced framework standardizes MOI definition and estimation, enhancing the accuracy of molecular surveillance for infectious diseases like malaria.
  • It clarifies the impact of MOI on variant prevalence versus frequency, crucial for tracking drug resistance and disease dynamics.
  • This approach facilitates robust comparisons between studies employing diverse MOI analytical methods.

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