Oscillations in an epidemiological model based on asynchronous probabilistic cellular automaton

Consider a contagious disease affecting a host population composed of two groups with distinct habits. At each time step, each individual of this population can be in one of two states: susceptible (S) or infective (I). Here, a SIS epidemic model based on cellular automaton (CA) is proposed to study the disease spreading in such a population. In this model, the state transitions are described by probabilistic rules and each group has its own schedule to update the states of its individuals. We also propose a set of difference equations (DE) to analyze this population dynamics and we show how these two approaches (CA and DE) can be equivalent. We noticed that oscillations can be found in the composition of the group with more active social life, but not in the composition of the other group.     

Self-sustained oscillations in epidemic models with infective immigrants

A relevant issue related to eco-epidemiological studies concerns the demographic mechanisms that can lead to self-sustained oscillations in the composition of a host population subject to infection. In particular, why does the prevalence of some contagious diseases oscillate over time? Here, we address this question by using susceptible-infective-recovered-empty models including migration of infective foreigners and variable population size. These models are described in terms of ordinary differential equations (ODE) and also in terms of probabilistic cellular automaton (PCA), in which each cell is connected to others either by a regular lattice or by a random graph favoring local contacts. Each cell in the PCA model can be either empty or occupied by a single individual. The amount of neighbors per cell affects the value of the basic reproduction number R₀, which is, in fact, a bifurcation parameter. We show that, by varying the amount of neighbors per cell (and consequently R₀), the number of infective individuals can start to exhibit periodic behavior, which corresponds to a Hopf bifurcation in the ODE model. This bifurcation gives rise to a self-sustained oscillation and it can only occur if the immigration rate of infective individuals is above a critical value. We also investigate how the sum of new infections, within the considered time window, depends on the number of neighbors per cell.     

Spatial heterogeneity and the design of immunization programs

Conventional epidemiological models usually assume homogeneous mixing, with susceptible and infected individuals mingling like the molecules in an ideal gas. It has recently been noted, however, that variability in transmission rates—arising, for example, from some hosts being in dense aggregates while others are in small or remote groups—can result in the intrinsic reproductive rate, R₀, of a microparasitic infection being greater than would be estimated under the usual assumption of homogeneous mixing; this implies the infection may be harder to eradicate under a homogeneously applied immunization programme (that is, a larger proportion of the population must be vaccinated) than simple estimates might suggest. In this paper we consider a spatially heterogeneous population arbitrarily subdivided into n groups, with one transmission rate among individuals within any one group, and another, lower transmission rate between groups. We define an optimum eradication program as that which—treating different groups differently—achieves its aim by immunizing the smallest overall number in each cohort of newborns, and we show this optimum program requires fewer immunizations than would be estimated under the (false) assumption that the population is homogeneously mixed. We prove this result in general form, and illustrate it for some special examples (in particular, for a population subdivided into one large ”city“ and several small ”villages“).     

Preventing the Tragedy of the Commons Through Punishment of Over-Consumers and Encouragement of Under-Consumers

The conditions that can lead to the exploitative depletion of a shared resource, i.e., the tragedy of the commons, can be reformulated as a game of prisoner’s dilemma: while preserving the common resource is in the best interest of the group, over-consumption is in the interest of each particular individual at any given point in time. One way to try and prevent the tragedy of the commons is through infliction of punishment for over-consumption and/or encouraging under-consumption, thus selecting against over-consumers. Here, the effectiveness of various punishment functions in an evolving consumer-resource system is evaluated within a framework of a parametrically heterogeneous system of ordinary differential equations (ODEs). Conditions leading to the possibility of sustainable coexistence with the common resource for a subset of cases are identified analytically using adaptive dynamics; the effects of punishment on heterogeneous populations with different initial composition are evaluated using the reduction theorem for replicator equations. Obtained results suggest that one cannot prevent the tragedy of the commons through rewarding of under-consumers alone—there must also be an implementation of some degree of punishment that increases in a nonlinear fashion with respect to over-consumption and which may vary depending on the initial distribution of clones in the population.     

Modeling the effects of vasculature evolution on early brain tumor growth

Mathematical modeling of both tumor growth and angiogenesis have been active areas of research for the past several decades. Such models can be classified into one of two categories: those that analyze the remodeling of the vasculature while ignoring changes in the tumor mass, and those that predict tumor expansion in the presence of a non-evolving vasculature. However, it is well accepted that vasculature remodeling and tumor growth strongly depend on one another. For this reason, we have developed a two-dimensional hybrid cellular automaton model of early brain tumor growth that couples the remodeling of the microvasculature with the evolution of the tumor mass. A system of reaction-diffusion equations has been developed to track the concentration of vascular endothelial growth factor (VEGF), Ang-1, Ang-2, their receptors and their complexes in space and time. The properties of the vasculature and hence of each cell are determined by the relative concentrations of these key angiogenic factors. The model exhibits an angiogenic switch consistent with experimental observations on the upregulation of angiogenesis. Particularly, we show that if the pathways that produce and respond to VEGF and the angiopoietins are properly functioning, angiogenesis is initiated and a tumor can grow to a macroscopic size. However, if the VEGF pathway is inhibited, angiogenesis does not occur and tumor growth is thwarted beyond 1-2mm in size. Furthermore, we show that tumor expansion can occur in well-vascularized environments even when angiogenesis is inhibited, suggesting that anti-angiogenic therapies may not be sufficient to eliminate a population of actively dividing malignant cells.     

Spatiotemporal Dynamics of the Epidemic Transmission in a Predator-Prey System

Epidemic transmission is one of the critical density-dependent mechanisms that affect species viability and dynamics. In a predator-prey system, epidemic transmission can strongly affect the success probability of hunting, especially for social animals. Predators, therefore, will suffer from the positive density-dependence, i.e., Allee effect, due to epidemic transmission in the population. The rate of species contacting the epidemic, especially for those endangered or invasive, has largely increased due to the habitat destruction caused by anthropogenic disturbance. Using ordinary differential equations and cellular automata, we here explored the epidemic transmission in a predator-prey system. Results show that a moderate Allee effect will destabilize the dynamics, but it is not true for the extreme Allee effect (weak or strong). The predator-prey dynamics amazingly stabilize by the extreme Allee effect. Predators suffer the most from the epidemic disease at moderate transmission probability. Counter-intuitively, habitat destruction will benefit the control of the epidemic disease. The demographic stochasticity dramatically influences the spatial distribution of the system. The spatial distribution changes from oil-bubble-like (due to local interaction) to aggregated spatially scattered points (due to local interaction and demographic stochasticity). It indicates the possibility of using human disturbance in habitat as a potential epidemic-control method in conservation.     

A random-walk model of human mortality and aging

Consideration is made of the roles of certain types of state space and time scales for a random-walk model of individual physiological status change and death. Because the actual measurement of physiological variables omits many variables relevant to survival, we are forced to view this model as operating in a stochastic state space for a population of individuals where only the frequency distributions are deterministic. In this stochastic state space, under the assumption that the “history” of prior movement contains no additional information, the forward partial differential equation is obtained for the distribution of a population whose movement in the selected space is determined by the randomwalk equations. If the initial distribution of the population in the state space is normal, then certain assumptions about movement and mortality will operate to preserve normality thereafter. Under the assumption of normality, simultaneous ordinary differential equations can be derived from the forward partial differential equation defining the distribution function. Examination of the ordinary simultaneous differential equations shows how parameters for certain models of aging and mortality can be obtained.     

The ideal free distribution as an evolutionarily stable strategy

We examine the evolutionary stability of strategies for dispersal in heterogeneous patchy environments or for switching between discrete states (e.g. defended and undefended) in the context of models for population dynamics or species interactions in either continuous or discrete time. There have been a number of theoretical studies that support the view that in spatially heterogeneous but temporally constant environments there will be selection against unconditional, i.e. random, dispersal, but there may be selection for certain types of dispersal that are conditional in the sense that dispersal rates depend on environmental factors. A particular type of dispersal strategy that has been shown to be evolutionarily stable in some settings is balanced dispersal, in which the equilibrium densities of organisms on each patch are the same whether there is dispersal or not. Balanced dispersal leads to a population distribution that is ideal free in the sense that at equilibrium all individuals have the same fitness and there is no net movement of individuals between patches or states. We find that under rather general assumptions about the underlying population dynamics or species interactions, only such ideal free strategies can be evolutionarily stable. Under somewhat more restrictive assumptions (but still in considerable generality), we show that ideal free strategies are indeed evolutionarily stable. Our main mathematical approach is invasibility analysis using methods from the theory of ordinary differential equations and nonnegative matrices. Our analysis unifies and extends previous results on the evolutionary stability of dispersal or state-switching strategies.     

Spatial Dynamics and the Evolution of Enzyme Production

We re-examine the problem of the evolution of protein synthesis or enzyme production using a stochastic cellular automaton model, where the replicators are fixed in the sites of a two-dimensional square lattice. In contrast with the classical chemical kinetics or mean-field predictions, we show that a small colony of mutant, protein-mediated (enzymatic) replicators has an appreciable probability to take over a resident population of simpler, direct-template replicators. In addition, we argue that the threshold phenomenon corresponding to the onset of invasion can be described quantitatively within the physics framework of nonequilibrium phase transitions. We study also the invasion of a resident population of enzymatic replicators by more efficient replicators of the same kind, and show that although slightly more efficient mutants cannot invade, invasion is a likely event if the productivity advantage of the mutants is large. In this sense, the establishment of a population of enzymatic replicators is not a `once-forever' evolutionary decision.     

A comparative evaluation of modelling strategies for the effect of treatment and host interactions on the spread of drug resistance

The evolutionary responses of infectious pathogens often have ruinous consequences for the control of disease spread in the population. Drug resistance is a well-documented instance that is generally driven by the selective pressure of drugs on both the replication of the pathogen within hosts and its transmission between hosts. Management of drug resistance therefore requires the development of treatment strategies that can impede the emergence and spread of resistance in the population. This study evaluates various treatment strategies for influenza infection as a case study by comparing the long-term epidemiological outcomes predicted by deterministic and stochastic versions of a homogeneously mixing (mean-field) model and those predicted by a heterogeneous model that incorporates spatial pair-wise correlation. We discuss the importance of three major parameters in our evaluation: the basic reproduction number, the population level of treatment, and the degree of clustering as a key parameter determining the structure of heterogeneous interactions. The results show that, as a common feature in all models, high treatment levels during the early stages of disease outset can result in large resistant outbreaks, with the possibility of a second wave of infection appearing in the pair-approximation model. Our simulations demonstrate that, if the basic reproduction number exceeds a threshold value, the population-wide spread of the resistant pathogen emerges more rapidly in the pair-approximation model with significantly lower treatment levels than in the homogeneous models. We tested an antiviral strategy that delays the onset of aggressive treatment for a certain amount of time after the onset of the outbreak. The findings indicate that the overall disease incidence is reduced as the degree of clustering increases, and a longer delay should be considered for implementing the large-scale treatment.     

Effects of heterogeneous and clustered contact patterns on infectious disease dynamics

The spread of infectious diseases fundamentally depends on the pattern of contacts between individuals. Although studies of contact networks have shown that heterogeneity in the number of contacts and the duration of contacts can have far-reaching epidemiological consequences, models often assume that contacts are chosen at random and thereby ignore the sociological, temporal and/or spatial clustering of contacts. Here we investigate the simultaneous effects of heterogeneous and clustered contact patterns on epidemic dynamics. To model population structure, we generalize the configuration model which has a tunable degree distribution (number of contacts per node) and level of clustering (number of three cliques). To model epidemic dynamics for this class of random graph, we derive a tractable, low-dimensional system of ordinary differential equations that accounts for the effects of network structure on the course of the epidemic. We find that the interaction between clustering and the degree distribution is complex. Clustering always slows an epidemic, but simultaneously increasing clustering and the variance of the degree distribution can increase final epidemic size. We also show that bond percolation-based approximations can be highly biased if one incorrectly assumes that infectious periods are homogeneous, and the magnitude of this bias increases with the amount of clustering in the network. We apply this approach to model the high clustering of contacts within households, using contact parameters estimated from survey data of social interactions, and we identify conditions under which network models that do not account for household structure will be biased.     

Dispersal in heterogeneous environments drives population dynamics and control of tsetse flies

Spatio-temporally heterogeneous environments may lead to unexpected population dynamics. Knowledge is needed on local properties favouring population resilience at large scale. For pathogen vectors, such as tsetse flies transmitting human and animal African trypanosomosis, this is crucial to target management strategies. We developed a mechanistic spatio-temporal model of the age-structured population dynamics of tsetse flies, parametrized with field and laboratory data. It accounts for density- and temperature-dependence. The studied environment is heterogeneous, fragmented and dispersal is suitability-driven. We confirmed that temperature and adult mortality have a strong impact on tsetse populations. When homogeneously increasing adult mortality, control was less effective and induced faster population recovery in the coldest and temperature-stable locations, creating refuges. To optimally select locations to control, we assessed the potential impact of treating them and their contribution to the whole population. This heterogeneous control induced a similar population decrease, with more dispersed individuals. Control efficacy was no longer related to temperature. Dispersal was responsible for refuges at the interface between controlled and uncontrolled zones, where resurgence after control was very high. The early identification of refuges, which could jeopardize control efforts, is crucial. We recommend baseline data collection to characterize the ecosystem before implementing any measures.     

利用元胞自动机研究一类捕食食饵模型中的斑块扩散现象

利用概率元胞自动机模型对空间隐式的、食饵具Allee效应的一类捕食食饵模型进行模拟,发现随着相关参数的变化,种群的空间扩散前沿由连续的扩散波逐渐转变为一种相互隔离的斑块向外扩散,这种斑块扩散现象与以往的扩散模式有所不同。研究结果表明：(1)在斑块扩散的情况下,相关参数的微小变化会导致种群灭绝或者形成连续的扩散波,即斑块扩散发生在种群趋于灭绝和连续扩散之间;(2)当种群的空间扩散方式为斑块扩散时,种群的扩散速度会变慢,与其他扩散方式下的速度有着明显的区别。该研究结果对生物入侵控制和外来物种监测有重要的启发和指导作用。     

Modelling contact spread of infection in host-parasitoid systems: Vertical transmission of pathogens can cause chaos

All animals and plants are, to some extent, susceptible to disease caused by varying combinations of parasites, viruses and bacteria. In this paper, we develop a mathematical model of contact spread infection to investigate the effect of introducing a parasitoid-vectored infection into a one-host-two-parasitoid competition model. We use a system of ordinary differential equations to investigate the separate influences of horizontal and vertical pathogen transmission on a model system appropriate for a variety of competitive situations. Computational simulations and steady-state analysis show that the transient and long-term dynamics exhibited under contact spread infection are highly complex. Horizontal pathogen transmission has a stabilising effect on the system whilst vertical transmission can destabilise it to the point of chaotic fluctuations in population levels. This has implications when considering the introduction of host pathogens for the control of insect vectored diseases such as bovine tuberculosis or yellow fever.     

集合种群空间混沌的模拟研究以及Allee效应、拥挤效应与捕食效应对空间模式的影响

集合种群的空间模式研究是当今生态学的核心问题之一。本研究利用常微分动力系统以及基于网格模型的元胞自动机模型对Allee效应、拥挤效应以及捕食作用集合种群的空间分布模式做了全面的模拟研究。Allee效应描述当种群水平低于某一阈值时会发生由生殖成功几率下降造成的种群负增长率,而拥挤效应是指当种群密度过高时引起的个体性为异常从而达到调节种群增长率的作用。文章组建了3个空间确定性模型：局部作用模型(CIM)、距离敏感模型(DSM)和集合种群捕食模型(MMP)。局部作用模型显示在一维生境中空斑块形成金字塔状,二维模型显示出明显的动态拟周期性以及由空间混沌所形成的异质性。距离敏感模型可导致由迁移个体中密度制约强度决定的集合种群大小复杂动态与种群密度的双峰分布。这些结果说明动态行为的复杂性,不仅可用于表征研究物种的特性,而且可以表明该物种的续存能力与灭绝风险。集合种群捕食模型是概率转换空间模型,利用该模型得出了依赖于模型参数和生境尺度的白组织种群概率空间分布模式。模拟的结果表明,系统的内在机制和这种白组织模式导致捕食者形成集团型不明显的“捕食小组”或“杀手小组”,并具有较高扩散力．但却包括侵占率低、灭绝率高的特点。而使猎物种群形成高集团性、高侵占率、低灭绝率、低扩散力的种群集团。这种特点又使捕食者种群在生境中处于中心地带,而使猎物种群形成在捕食者和生境边缘间的环状分布。这些结果还说明了尺度对于生态学的研究是至关重要的,不同的尺度将产生不同的系统模式。     

Proliferative heterogeneity of vascular smooth muscle cells and its alteration by injury

This study was designed to explore the question of whether the population of morphologically similar smooth muscle cells (SMC) in the vessel wall is functionally homogeneous or heterogeneous with respect to their proliferative response to injury. Using time-lapse video recording we measured interdivision times (IDT) of primary SMC clones, sibling pairs, and mother/daughter pairs. SMC from in vivo undisturbed vessels displayed an interclonal and intraclonal heterogeneity with a wide range in IDT. In vivo balloon injury resulted in a population with homogeneously short IDT. While 80% of IDT of SMC from injured vessels were shorter than 14 h, only slightly more than half of IDT of cells from undisturbed vessels fell into this category. Longitudinal analysis of mother/daughter pairs confirmed the presence of a heterogeneous population of SMC in the undisturbed vessel wall. In vivo balloon injury not only shortened the IDT of the majority of cells, but the shorter IDT persisted much longer than in the case of the undisturbed vessel. We suggest that a morphologically homogeneous SMC population in the aorta can now be subdivided into several groups of functionally different SMC with respect to their proliferative response to injury.     

The evolution of juvenile-adult interactions in populations structured in age and space

We study the evolution of a spatially structured population with two age classes using spatial moment equations. In the model, adults can either help juveniles by increasing their survival, or adopt a cannibalistic behaviour and consume juveniles. While cannibalism is the sole evolutionary outcome when the population is well-mixed, both cannibalism and parental care can be evolutionarily stable if the population is viscous. Our analysis allows us to make two main technical points. First, we present a method to define invasion fitness in class-structured viscous populations, which allows us to apply adaptive dynamics methodology. Second, we show that ordinary pair approximation introduces an important quantitative bias in the evolutionary model, even on random networks. We propose a correction to the ordinary pair approximation that yields quantitative accuracy, and discuss how the bias associated with this approach is precisely what allows us to identify subtle aspects associated with the evolutionary dynamics of spatially structured populations.     

Host ecotype generates evolutionary and epidemiological divergence across a pathogen metapopulation

The extent and speed at which pathogens adapt to host resistance varies considerably. This presents a challenge for predicting when—and where—pathogen evolution may occur. While gene flow and spatially heterogeneous environments are recognized to be critical for the evolutionary potential of pathogen populations, we lack an understanding of how the two jointly shape coevolutionary trajectories between hosts and pathogens. The rust pathogen Melampsora lini infects two ecotypes of its host plant Linum marginale that occur in close proximity yet in distinct populations and habitats. In this study, we found that within-population epidemics were different between the two habitats. We then tested for pathogen local adaptation at host population and ecotype level in a reciprocal inoculation study. Even after controlling for the effect of spatial structure on infection outcome, we found strong evidence of pathogen adaptation at the host ecotype level. Moreover, sequence analysis of two pathogen infectivity loci revealed strong genetic differentiation by host ecotype but not by distance. Hence, environmental variation can be a key determinant of pathogen population genetic structure and coevolutionary dynamics and can generate strong asymmetry in infection risks through space.     

The ideal free distribution as an evolutionarily stable strategy

We examine the evolutionary stability of strategies for dispersal in heterogeneous patchy environments or for switching between discrete states (e.g. defended and undefended) in the context of models for population dynamics or species interactions in either continuous or discrete time. There have been a number of theoretical studies that support the view that in spatially heterogeneous but temporally constant environments there will be selection against unconditional, i.e. random, dispersal, but there may be selection for certain types of dispersal that are conditional in the sense that dispersal rates depend on environmental factors. A particular type of dispersal strategy that has been shown to be evolutionarily stable in some settings is balanced dispersal, in which the equilibrium densities of organisms on each patch are the same whether there is dispersal or not. Balanced dispersal leads to a population distribution that is ideal free in the sense that at equilibrium all individuals have the same fitness and there is no net movement of individuals between patches or states. We find that under rather general assumptions about the underlying population dynamics or species interactions, only such ideal free strategies can be evolutionarily stable. Under somewhat more restrictive assumptions (but still in considerable generality), we show that ideal free strategies are indeed evolutionarily stable. Our main mathematical approach is invasibility analysis using methods from the theory of ordinary differential equations and nonnegative matrices. Our analysis unifies and extends previous results on the evolutionary stability of dispersal or state-switching strategies.     

Automata simulation of the selection process

A complete simulation of the selection process can be constructed using a population of self-replicating finite-state automata. The entire population is challenged with a repeating sequence of inputs, and those individuals that are best able to recognize the input sequence are allowed to replicate most rapidly. Replication proceeds with imperfect fidelity, so that under the constraint of constant total population size, a quasispecies distribution of error copies is obtained. The operation of this simulation provides an essential representation of an evolving system. When the input sequence is altered, the structure of the existing population is destabilized, and a new quasispecies distribution emerges. The ability of the system to respond to changes in the input and the structure of the quasispecies distribution are shown to be critically dependent on the fidelity of replication.