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Reproduction Factor Based Latent Epidemic Model Inference: A Data-Driven Approach Using COVID-19 Datasets.

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    Area of Science:

    • Epidemiology
    • Mathematical Biology
    • Public Health

    Background:

    • Mathematical modeling is crucial for understanding infectious disease transmission and informing public health interventions.
    • Existing transmissibility indicators like reproduction numbers have limitations, especially during advanced stages of an epidemic or when control measures are in place.

    Purpose of the Study:

    • To propose a novel transmissibility indicator, the reproduction factor, for near real-time assessment of infectious disease spread.
    • To develop a data-driven inference model using a Markov chain to estimate disease parameters and latent information.
    • To validate the proposed reproduction factor and inference model using COVID-19 datasets.

    Main Methods:

    • Development of a new transmissibility indicator, the reproduction factor, considering susceptible individuals and the non-isolated population.
    • Application of a Markov chain for data-driven inference of epidemiological parameters, including undetected infections and daily new infections.
    • Extensive simulations and comparative analysis using COVID-19 datasets from Germany, Italy, South Korea, and California.

    Main Results:

    • The proposed reproduction factor effectively evaluates near real-time transmissibility, outperforming classic reproduction numbers in complex scenarios.
    • The Markov chain-based inference model successfully estimated latent epidemiological information from COVID-19 data.
    • The model demonstrated strong performance in explaining the dynamics of COVID-19 spread across diverse geographical locations.

    Conclusions:

    • The reproduction factor offers a more accurate and timely measure of infectious disease transmissibility.
    • The data-driven inference model provides valuable insights into hidden epidemiological data, aiding in disease surveillance.
    • The validated model and indicator can significantly enhance the effectiveness of public health control strategies and interventions.