8 research outputs found
Investigating mathematical models of immuno-interactions with early-stage cancer under an agent-based modelling perspective
Many advances in research regarding immuno-interactions with cancer were developed with the help of ordinary
differential equation (ODE) models. These models, however, are not effectively capable of representing problems
involving individual localisation, memory and emerging properties, which are common characteristics of cells and
molecules of the immune system. Agent-based modelling and simulation is an alternative paradigm to ODE
models that overcomes these limitations. In this paper we investigate the potential contribution of agent-based
modelling and simulation when compared to ODE modelling and simulation. We seek answers to the following
questions: Is it possible to obtain an equivalent agent-based model from the ODE formulation? Do the outcomes
differ? Are there any benefits of using one method compared to the other? To answer these questions, we have
considered three case studies using established mathematical models of immune interactions with early-stage
cancer. These case studies were re-conceptualised under an agent-based perspective and the simulation results
were then compared with those from the ODE models. Our results show that it is possible to obtain equivalent
agent-based models (i.e. implementing the same mechanisms); the simulation output of both types of models
however might differ depending on the attributes of the system to be modelled. In some cases, additional insight
from using agent-based modelling was obtained. Overall, we can confirm that agent-based modelling is a useful
addition to the tool set of immunologists, as it has extra features that allow for simulations with characteristics
that are closer to the biological phenomena
Juxtaposition of System Dynamics and Agent-Based Simulation for a Case Study in Immunosenescence
A hybrid simulation framework for modelling construction projects using agent-based modelling and system dynamics: an application to model construction workers' safety behavior
Methods of modelling viral disease dynamics across the within- and between-host scales: the impact of virus dose on host population immunity
We study the epidemiology of a viral disease with dose-dependent replication and transmission by nesting a differential-equation model of the within-host viral dynamics inside a between-host epidemiological model. We use two complementary approaches for nesting the models: an agent-based (AB) simulation and a mean-field approximation called the growth-matrix (GM) model. We find that although infection rates and predicted case loads are somewhat different between the AB and GM models, several epidemiological parameters, e.g. mean immunity in the population and mean dose received, behave similarly across the methods. Further, through a comparison of our dose-dependent replication model against two control models that uncouple dose-dependent replication from transmission, we find that host immunity in a population after an epidemic is qualitatively different than when transmission depends on time-varying viral abundances within hosts. These results show that within-host dynamics and viral dose should not be neglected in epidemiological models, and that the simpler GM approach to model nesting provides a reasonable tradeoff between model complexity and accuracy of results