An agent-based nested model integrating within-host and between-host mechanisms to predict an epidemic.

Area of Science:

• Epidemiology
• Computational Biology
• Infectious Disease Modeling

Background:

• The COVID-19 pandemic highlighted the need for accurate communicable disease spread prediction.
• Traditional Susceptible-Infected-Recovered (SIR) models often use constant transmission probabilities, which may not fully capture disease dynamics.

Purpose of the Study:

• To develop an innovative agent-based model (ABM) that incorporates dynamic, within-host viral load variations to predict between-host disease transmission.
• To compare the predictive capabilities of this new model against the classical SIR model regarding epidemic size, peak infection numbers, and timing.

Main Methods:

• An agent-based modeling approach was developed, integrating "within-host" viral load dynamics with "between-host" transmission processes.
• Transmission probability was made dynamic and time-dependent, directly influenced by the agent's viral load, diverging from the constant probability in standard SIR models.

Main Results:

• The agent-based model demonstrated that while overall epidemic size predictions align with the standard SIR model, significant differences were observed in the peak number of infected individuals and the timing of this peak.
• These discrepancies are attributed to the direct correlation between time-evolving transmission probability and host viral load.

Conclusions:

• The developed agent-based model provides a more nuanced understanding of disease spread dynamics by linking within-host and between-host factors.
• This model's insights are crucial for informing targeted public health interventions and policies for effective epidemic management.