Compositional modelling of immune response and virus transmission dynamics

TL;DR

This paper introduces a modular approach to modeling immune response and virus transmission, enabling flexible combination and refinement of models to better reflect biological processes and heterogeneity in infectiousness.

Contribution

It presents a simple, rule-based framework for constructing and merging immune response and transmission models, improving adaptability over traditional monolithic models.

Findings

Reproduces COVID-19 PCR response curve

Shows long-tailed infectiousness distribution

Replicates viral load shifts during epidemics

Abstract

Transmission models for infectious diseases are typically formulated in terms of dynamics between individuals or groups with processes such as disease progression or recovery for each individual captured phenomenologically, without reference to underlying biological processes. Furthermore, the construction of these models is often monolithic: they don't allow one to readily modify the processes involved or include the new ones, or to combine models at different scales. We show how to construct a simple model of immune response to a respiratory virus and a model of transmission using an easily modifiable set of rules allowing further refining and merging the two models together. The immune response model reproduces the expected response curve of PCR testing for COVID-19 and implies a long-tailed distribution of infectiousness reflective of individual heterogeneity. This immune response model, when combined with a transmission model, reproduces the previously reported shift in the population distribution of viral loads along an epidemic trajectory.