Mathematical Biosciences & Engineering
2010 , Volume 7 , Issue 1
A special issue on
Tribute to Horst R. Thieme on the Occasion of his 60th Birthday
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I met Horst at the University of Münster, Germany, in July 1972. While I was at the university, one of my goals was to find a husband. I wanted my husband to be Catholic, so I decided to join a bible study group, where I thought I would find many suitable “candidates.” Unfortunately though, I was wrong. There were only two Catholics. One of them was definitely not an option, and then there was … Horst. He was a challenge, and I guess all participants of this conference know what I mean. But some people just grow on you, which happened in Horst’s case.
As our relationship was getting more serious, he said to me one day: “I need to tell you something. I am already engaged, and no matter what is going to happen between the two of us, I’m not going to give up my fiancée. If you manage to get along well with her, the three of us can have a good life together. I would like you to give the three of us a chance.”
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It is a great pleasure to congratulate Horst on his 60th birthday and to dedicate this volume to him in honor of his many contributions to the field of mathematical biology.
Horst Thieme has been, over a period of thirty years, one of the most productive scientists in Mathematical Biology, with numerous works on subjects centered on population biology and transmission and control of infectious disease. He has provided a number of rigorous mathematical tools for the study of structured populations, the problem of biological invasion, and the limiting behavior of epidemic and other population models. Horst Thieme's work can be characterized by deep insight into the underlying structure of biological phenomena and mechanisms, great skills in entering these insights into feasible models, and by rigorous mathematics validating or sometimes rejecting modeling approaches.
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The mathematical theory of single outbreak epidemic models really began with the work of Kermack and Mackendrick about $8$ decades ago. This gave a simple answer to the long-standing question of why epidemics woould appear suddenly and then disappear just as suddenly without having infected an entire population. Therefore it seemed natural to expect that theoreticians would immediately proceed to expand this mathematical framework both because the need to handle recurrent single infectious disease outbreaks has always been a priority for public health officials and because theoreticians often try to push the limits of exiting theories. However, the expansion of the theory via the inclusion of refined epidemiological classifications or through the incorporation of categories that are essential for the evaluation of intervention strategies, in the context of ongoing epidemic outbreaks, did not materialize. It was the global threat posed by SARS in $2003$ that caused theoreticians to expand the Kermack-McKendrick single-outbreak framework. Most recently, efforts to connect theoretical work to data have exploded as attempts to deal with the threat of emergent and re-emergent diseases including the most recent H1N1 influenza pandemic, have marched to the forefront of our global priorities. Since data are collected and/or reported over discrete units of time, developing single outbreak models that fit collected data naturally is relevant. In this note, we introduce a discrete-epidemic framework and highlight, through our analyses, the similarities between single-outbreak comparable classical continuous-time epidemic models and the discrete-time models introduced in this note. The emphasis is on comparisons driven by expressions for the final epidemic size.
A general question in the study of the evolution of dispersal is what kind of dispersal strategies can convey competitive advantages and thus will evolve. We consider a two species competition model in which the species are assumed to have the same population dynamics but different dispersal strategies. Both species disperse by random diffusion and advection along certain gradients, with the same random dispersal rates but different advection coefficients. We found a conditional dispersal strategy which results in the ideal free distribution of species, and show that it is a local evolutionarily stable strategy. We further show that this strategy is also a global convergent stable strategy under suitable assumptions, and our results illustrate how the evolution of conditional dispersal can lead to an ideal free distribution. The underlying biological reason is that the species with this particular dispersal strategy can perfectly match the environmental resource, which leads to its fitness being equilibrated across the habitats.
The classical models for populations structured by size have two features which may cause problems in biologically realistic modeling approaches: the structure variable always increases, and individuals in an age cohort that are identical initially stay identical throughout their lives. Here a diffusion term is introduced in the partial differential equation which mathematically amounts to adding viscosity. This approach solves both problems but it requires to identify appropriate boundary (recruitment) conditions. The method is applied to size-structured populations, metapopulations, infectious diseases, and vector-transmitted diseases.
Recently an SIS epidemic reaction-diffusion model with Neumann (or no-flux) boundary condition has been proposed and studied by several authors to understand the dynamics of disease transmission in a spatially heterogeneous environment in which the individuals are subject to a random movement. Many important and interesting properties have been obtained: such as the role of diffusion coefficients in defining the reproductive number; the global stability of disease-free equilibrium; the existence and uniqueness of a positive endemic steady; global stability of endemic steady for some particular cases; and the asymptotical profiles of the endemic steady states as the diffusion coefficient for susceptible individuals is sufficiently small. In this research we will study two modified SIS diffusion models with the Dirichlet boundary condition that reflects a hostile environment in the boundary. The reproductive number is defined which plays an essential role in determining whether the disease will extinct or persist. We have showed that the disease will die out when the reproductive number is less than one and that the endemic equilibrium occurs when the reproductive number is exceeds one. Partial result on the global stability of the endemic equilibrium is also obtained.
We study a stage-structured single species population model with Allee effects. The asymptotic dynamics of the model depend on the maximal growth rate of the population as well as on its initial population size. We also investigate two models of host-parasitoid interaction with stage-structure and Allee effects in the host. The parasitoid population may drive the host population to extinction in both models even if the initial host population is beyond the Allee threshold.
Feedback loops are found to be important network structures in regulatory networks of biological signaling systems because they are responsible for maintaining normal cellular activity. Recently, the functions of feedback loops have received extensive attention. The existing results in the literature mainly focus on verifying that negative feedback loops are responsible for oscillations, positive feedback loops for multistability, and coupled feedback loops for the combined dynamics observed in their individual loops. In this work, we develop a general framework for studying systematically functions of feedback loops networks. We investigate the general dynamics of all networks with one to three nodes and one to two feedback loops. Interestingly, our results are consistent with Thomas' conjectures although we assume each node in the network undergoes a decay, which corresponds to a negative loop in Thomas' setting. Besides studying how network structures influence dynamics at the linear level, we explore the possibility of network structures having impact on the nonlinear dynamical behavior by using Lyapunov-Schmidt reduction and singularity theory.
To study the impact of releasing transgenic mosquitoes on malaria transmission, we formulate discrete-time models for interacting wild and transgenic mosquitoes populations, based on systems of difference equations. We start with models including all homozygous and heterozygous mosquitoes. We then consider either dominant or recessive transgenes to reduce the 3-dimensional model systems to 2-dimensional systems. We include density-dependent vital rates and incorporate Allee effects in the functional mating rates. Dynamics of these models are explored by investigating the existence and stability of boundary and positive fixed points. Numerical simulations are provided and brief discussions are given.
This article focuses on the study of an age-structured two-strain model with super-infection. The explicit expression of basic reproduction numbers and the invasion reproduction numbers corresponding to strain one and strain two are obtained. It is shown that the infection-free steady state is globally stable if the basic reproductive number $ R_0 $ is below one. Existence of strain one and strain two exclusive equilibria is established. Conditions for local stability or instability of the exclusive equilibria of the strain one and strain two are established. Existence of coexistence equilibrium is also obtained under the condition that both invasion reproduction numbers are larger than one.
Traditional functional responses for plant-herbivore interactions do not take into account explicitly the effect of plant toxin. However, considerable evidence suggests that toxins set upper limits on food intake for many species of herbivorous vertebrates. In this paper, a mathematical model for plant-herbivore interactions mediated by toxin-determined functional response is studied. The model consists of three ordinary differential equations describing one herbivore population and two plant species with different toxicity levels. The effect of plant toxicity on herbivore's intake rate is incorporated explicitly in the model by assuming an increased handling time. The dynamical behaviors of the model are analyzed and the results are used to examine the influence of toxin-determined intake in the community composition of plant species. The bifurcation analysis presented in this paper suggests that the toxin-mediated functional response may have dramatic effects on plant-herbivore interactions.
The aim of this work is to investigate the mechanisms involved in the clearance of viral infection of the influenza virus at the epithelium level by modeling and analyzing the interaction of the influenza virus specific cytotoxic T Lymphocytes (CTL cells) and the influenza virus infected epithelial cells. Since detailed and definite mechanisms that trigger CTL production and cell death are still debatable, we utilize two plausible mathematical models for the CTLs response to influenza infection (i) logistic growth and (ii) threshold growth. These models incorporate the simulating effect of the production of CTLs during the infection. The systematical analysis of these models show that the behaviors of the models are similar when CTL density is high and in which case both generate reasonable dynamics. However, both models failed to produce the desirable and natural clearance dynamic. Nevertheless, at lower CTL density, the threshold model shows the possibility of existence of a "lower" equilibrium. This sub-threshold equilibrium may represent dose-dependent immune response to low level infection.
A well-known formula for the spreading speed of a discrete-time recursion model is extended to a class of problems for which its validity was previously unknown. These include migration models with moderately fat tails or fat tails. Examples of such models are given.
Hantavirus, a zoonotic disease carried by wild rodents, is spread among rodents via direct contact and indirectly via infected rodent excreta in the soil. Spillover to humans is primarily via the indirect route through inhalation of aerosolized viral particles. Rodent-hantavirus models that include direct and indirect transmission and periodically varying demographic and epidemiological parameters are studied in this investigation. Two models are analyzed, a nonautonomous system of differential equations with time-periodic coefficients and an autonomous system, where the coefficients are taken to be the time-average. In the nonautonomous system, births, deaths, transmission rates and viral decay rates are assumed to be periodic. For both models, the basic reproduction numbers are calculated. The models are applied to two rodent populations, reservoirs for a New World and for an Old World hantavirus. The numerical examples show that periodically varying demographic and epidemiological parameters may substantially increase the basic reproduction number. Also, large variations in the viral decay rate in the environment coupled with an outbreak in rodent populations may lead to spillover infection in humans.
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