Learning transmission dynamics modelling of COVID-19 using comomodels.

Area of Science:

• Epidemiology
• Mathematical Biology
• Computational Science

Background:

• The COVID-19 pandemic necessitates sophisticated mathematical models for policy guidance.
• A gap exists between basic epidemiological models and complex, real-world COVID-19 transmission models.
• Existing models present challenges for newcomers to epidemiological modeling.

Purpose of the Study:

• To introduce "comomodels," an open-source R package designed to simplify the understanding of COVID-19 transmission dynamics.
• To provide accessible learning resources for epidemiological modeling of COVID-19.
• To facilitate the practical application of differential equation models in COVID-19 research.

Main Methods:

• Development of an open-source R package (comomodels) for epidemiological modeling.
• Creation of tutorials and an interactive web-based interface for dynamic model investigation.
• Application of the package within R Markdown vignettes to illustrate key COVID-19 transmission concepts.

Main Results:

• The "comomodels" package effectively demonstrates complex differential equation models for COVID-19.
• Learning resources enhance accessibility for users new to epidemiological modeling.
• The package facilitates practical insights into COVID-19 transmission dynamics.

Conclusions:

• "comomodels" bridges the gap between theoretical and applied epidemiological modeling for COVID-19.
• Accessible tools and resources are crucial for advancing the understanding and application of infectious disease models.
• This package supports evidence-based policymaking through enhanced modeling capabilities.