A runtime alterable epidemic model with genetic drift, waning immunity and vaccinations

Area of Science:

• Computational Biology
• Epidemiology
• Software Engineering

Background:

• Complex dynamical systems modeling is crucial for understanding phenomena like disease spread.
• Existing modeling platforms often lack flexibility for real-time adaptation during simulations.
• The COVID-19 pandemic highlighted the need for dynamic models that can incorporate evolving pathogen characteristics and interventions.

Purpose of the Study:

• To present methods for building a Java Runtime-Alterable-Model Platform (RAMP) for complex dynamical systems.
• To illustrate the RAMP's utility by developing a multivariant SEIR (Susceptible-Exposed-Infectious-Recovered) epidemic model.
• To enable easy modification of parameters, process descriptions, and runtime drivers during simulations.

Main Methods:

• Developed an individual-based SEIR model within the RAMP framework.
• Incorporated adaptive contact rates, pathogen genetic drift, waning and cross-immunity.
• Enabled runtime alteration of pathogen shedding, environmental persistence, transmission, within-host mutation, social distancing, and vaccination strategies.

Main Results:

• Simulations using COVID-19 pandemic data suggest waning immunity can outpace vaccination rates.
• Vaccination rollouts may fail to contain highly transmissible variants if vaccine valency is not adapted to escape mutations.
• The RAMP allows for dynamic adjustments to model parameters and processes.

Conclusions:

• The SEIR RAMP provides a flexible tool for exploring epidemic dynamics under various scenarios.
• The RAMP concept facilitates the creation and sharing of adaptable complex systems models.
• Dynamic modeling is essential for effective pandemic response, especially concerning variant evolution and vaccination strategies.