Chris McCaig

Process Algebra Models of Population Dynamics

It is well understood that populations cannot grow without bound and that it is competition between individuals for resources which restricts growth. Despite centuries of interest, the question of how best to model density dependent population growth still has no definitive answer. We address this question here through a number of individual based models of populations expressed using the process algebra WSCCS. The advantage of these models is that they can be explicitly based on observations of individual interactions. From our probabilistic models we derive equations expressing overall population dynamics, using a formal and rigorous rewriting based method. These equations are easily compared with the traditionally used deterministic Ordinary Differential Equation models and allow evaluation of those ODE models, challenging their assumptions about system dynamics. Further, the approach is applied to epidemiology, combining population growth with disease spread.

From individuals to populations: A mean field semantics for process algebra

A new semantics in terms of mean field equations is presented for WSCCS (Weighted Synchronous Calculus of Communicating Systems). The semantics captures the average behaviour of the system over time, but without computing the entire state space, therefore avoiding the state space explosion problem. This allows easy investigation of models with large numbers of components. The new semantics is shown to be equivalent to the standard Discrete Time Markov Chain semantics of WSCCS as the number of processes tends to infinity. The method of deriving the semantics is illustrated with examples drawn from biology and from computing.

Improved Continuous Approximation of PEPA Models through Epidemiological Examples

We present two individual based models of disease systems using PEPA (Performance Evaluation Process Algebra). The models explore contrasting mechanisms of disease transmission: direct transmission (e.g. measles) and indirect transmission (e.g. malaria, via mosquitos). We extract ordinary differential equations (ODEs) as a continuous approximation to the PEPA models using the Hillston method and compare these with the traditionally used ODE disease models and with the results of stochastic simulation. Improvements to the Hillston method of ODE extraction for this context are proposed, and the new results compare favourably with stochastic simulation results and to ODEs derived for equivalent models in WSCCS (Weighted Synchronous Calculus of Communicating Systems).

A rigorous approach to investigating common assumptions about disease transmission

Changing scale, for example, the ability to move seamlessly from an individual-based model to a population-based model, is an important problem in many fields. In this paper, we introduce process algebra as a novel solution to this problem in the context of models of infectious disease spread. Process algebra allows us to describe a system in terms of the stochastic behaviour of individuals, and is a technique from computer science. We review the use of process algebra in biological systems, and the variety of quantitative and qualitative analysis techniques available. The analysis illustrated here solves the changing scale problem: from the individual behaviour we can rigorously derive equations to describe the mean behaviour of the system at the level of the population. The biological problem investigated is the transmission of infection, and how this relates to individual interactions.

A symbolic investigation of superspreaders

Superspreaders are an important phenomenon in the spread of infectious disease, accounting for a higher than average number of new infections in the population. We use mathematical models to compare the impact of supershedders and supercontacters on population dynamics. The stochastic, individual based models are investigated by conversion to deterministic, population level Mean Field Equations, using process algebra. The mean emergent population dynamics of the models are shown to be equivalent with and without superspreaders; however, simulations confirm expectations of differences in variability, having implications for individual epidemics.

Modelling wave–current interactions off the east coast of Scotland

Densely populated coastal areas of the North Sea are particularly vulnerable to severe wave conditions, which overtop or damage sea defences leading to dangerous flooding. Around the shallow southern North Sea, where the coastal margin is lying low and population density is high, oceanographic modelling has helped to develop forecasting systems to predict flood risk. However, coastal areas of the deeper northern North Sea are also subject to regular storm damage, but there has been little or no effort to develop coastal wave models for these waters. Here, we present a high spatial resolution model of northeast Scottish coastal waters, simulating waves and the effect of tidal currents on wave propagation, driven by global ocean tides, far-field wave conditions, and local air pressure and wind stress. We show that the wave–current interactions and wave–wave interactions are particularly important for simulating the wave conditions close to the coast at various locations. The model can simulate the extreme conditions experienced when high (spring) tides are combined with sea-level surges and large Atlantic swell. Such a combination of extremes represents a high risk for damaging conditions along the Scottish coast.

Using process algebra to develop predator-prey models of within-host parasite dynamics

As a first approximation of immune-mediated within-host parasite dynamics we can consider the immune response as a predator, with the parasite as its prey. In the ecological literature of predator-prey interactions there are a number of different functional responses used to describe how a predator reproduces in response to consuming prey. Until recently most of the models of the immune system that have taken a predator-prey approach have used simple mass action dynamics to capture the interaction between the immune response and the parasite. More recently Fenton and Perkins (2010) employed three of the most commonly used prey-dependent functional response terms from the ecological literature. In this paper we make use of a technique from computing science, process algebra, to develop mathematical models. The novelty of the process algebra approach is to allow stochastic models of the population (parasite and immune cells) to be developed from rules of individual cell behaviour. By using this approach in which individual cellular behaviour is captured we have derived a ratio-dependent response similar to that seen in the previous models of immune-mediated parasite dynamics, confirming that, whilst this type of term is controversial in ecological predator-prey models, it is appropriate for models of the immune system.