A novel framework for modeling quarantinable disease transmission.

Area of Science:

• Epidemiology
• Mathematical Biology
• Infectious Disease Modeling

Background:

• Traditional epidemiological models struggle with COVID-19 complexities like asymptomatic spread and underreported data.
• Existing models often assume uniform mixing and static transmission rates, which do not reflect real-world dynamics.

Purpose of the Study:

• To introduce a novel epidemiological model, CURNDS (Compartmental Understanding of Realistic Novel Disease Spread), designed to overcome limitations of traditional models.
• To accurately estimate undetected infections and undocumented mortality during the COVID-19 pandemic.
• To provide a more nuanced understanding of disease transmission dynamics, particularly for highly contagious diseases.

Main Methods:

• Developed the CURNDS model, which stratifies compartments and transmission pathways based on contact levels.
• Incorporated adaptive power laws and dynamic transmission rates to move beyond static assumptions.
• Utilized spline-based smoothing techniques for robust data analysis.
• Challenged the assumption of homogeneous mixing in epidemiological modeling.

Main Results:

• The CURNDS model accurately estimates the number of undetected COVID-19 infections and undocumented deaths.
• Analysis revealed significant deviations from homogeneous mixing assumptions, highlighting complex transmission patterns.
• The model provides insights into the transmission dynamics of various COVID-19 strains.

Conclusions:

• The CURNDS model offers a robust framework for understanding and modeling the spread of highly contagious diseases like COVID-19.
• This approach improves the estimation of a disease's true impact by accounting for unobserved cases and deaths.
• The findings underscore the need for dynamic and contact-level-based models in infectious disease epidemiology.