Compositional modelling of immune response and virus transmission dynamics
W Waites1,2, M Cavaliere3, V Danos4
1Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK.
Summary
This study introduces a new, modular modeling approach for infectious disease transmission. It integrates biological immune responses with transmission dynamics, improving epidemic forecasting and understanding individual heterogeneity in disease spread.
Area of Science:
- Epidemiology
- Computational Biology
- Immunology
Background:
- Traditional infectious disease models lack biological detail and flexibility.
- Existing models are often monolithic, hindering modification and integration of different scales.
Purpose of the Study:
- To develop a modifiable modeling framework for infectious diseases.
- To integrate immune response dynamics with disease transmission models.
- To enhance the realism and adaptability of epidemic modeling.
Main Methods:
- Constructed a rule-based immune response model for respiratory viruses.
- Developed a transmission model incorporating the immune response.
- Combined and refined models at different scales.
Main Results:
- The immune response model accurately predicts COVID-19 PCR test results.
- The model reveals a long-tailed infectiousness distribution due to individual differences.
- Integrated model reproduces population viral load shifts during epidemics.
Conclusions:
- A modular, rule-based approach enhances infectious disease modeling flexibility.
- Integrating biological processes improves the accuracy of epidemic simulations.
- This framework facilitates the merging of models across scales for comprehensive analysis.
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