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Updated: Nov 5, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Quantifying superspreading for COVID-19 using Poisson mixture distributions.

Cécile Kremer, Andrea Torneri, Sien Boesmans

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    This summary is machine-generated.

    The negative binomial distribution may inaccurately model infectious disease transmission, especially for COVID-19. Alternative distributions, like the Poisson-lognormal, offer a better fit for secondary case data, improving transmission heterogeneity estimates.

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    Area of Science:

    • Epidemiology
    • Mathematical Biology
    • Infectious Disease Modeling

    Background:

    • Accurate modeling of secondary cases is crucial for infectious disease control.
    • Individual variation in transmission (heterogeneity) is common, as seen in COVID-19.
    • The negative binomial distribution is frequently used but may not fully capture transmission dynamics.

    Approach:

    • Proposed and evaluated three alternative offspring distributions to model transmission heterogeneity.
    • Assessed potential bias in estimating the mean and overdispersion when the assumed distribution differs from the data-generating one.
    • Re-analyzed three COVID-19 datasets using these distributions.

    Key Points:

    • Overdispersion estimates can be biased in the presence of substantial transmission heterogeneity.
    • The choice of offspring distribution significantly impacts estimates of transmission parameters.
    • The Poisson-lognormal distribution provided a better fit for secondary case data in analyzed COVID-19 datasets.

    Conclusions:

    • The negative binomial distribution may not be optimal for modeling infectious disease transmission heterogeneity.
    • Considering alternative distributions like the Poisson-lognormal is recommended for more accurate epidemiological assessments.
    • This research highlights the importance of selecting appropriate statistical distributions for understanding disease spread.