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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.
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.
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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