我校“东师学者讲座教授”、美国Purdue University的冯芷兰教授将于7月5日至8月9日在我校进行为期一个月的学术访问。访问期间将为高年级本科生、研究生、青年教授讲授短期课程《Epidemiological Modeling and Applications》并作系列学术报告，同时与范猛教授研究组及相关方向的教师进行合作研究。

附1：冯芷兰教授简介
    冯芷兰，美国Purdue University教授。1978-1985年就读于吉林大学数学系，先后获学士和硕士学位。1994年于美国Arizona State University获博士学位, 1994-1996年在美国Cornell University从事博士后研究。1996年至今在美国Purdue University任教，2005年任教授。冯芷兰教授是数学生物学领域知名学者，主要从事微分方程和动力学系统及其在生物学、生态学、流行病学中的应用，公开发表论文80余篇，是Journal of Theoretical Biology, Mathematical Biosciences, Mathematical Biosciences and Engineering 以及 SIAM J. Applications Mathematics等杂志的编委。多次到我校进行学术交流与访问，与范猛教授研究组有着长期的而良好的合作关系。2015年被聘为我校“东师学者讲座教授”。
    冯芷兰教授个人主页： http://www.math.purdue.edu/~zfeng/site

附2：授课时间、地点

附3：授课内容

★Short Course:
Title：Epidemiological modeling and applications
Syllabus: The course will start with simple epidemic and endemic models for the spread and control of infectious diseases. Base methods and mathematical tools will be introduced for analyzing these models. Models with various complexities will then be considered which are motivated by specific questions that are of interests to biologists, epidemiologists, and public health policymakers. The complexities are related to heterogeneities such as age-structure, spatial-structure, multiple pathogen strains, coupling within-host and between-host systems. Models will also be considered to investigate the effects of various disease control strategies including quarantine, isolation, drug treatment, vaccination, etc.

★Talks：
☆Talk 1：
Title：Influence of heterogeneity in model predictions for public health policymaking
Abstract：Mathematical modeling of infectious diseases has affected disease control policy throughout the developed world. Policy goals vary with disease and setting, but preventing outbreaks is common. We use epidemiological models that incorporate various spatial and temporal heterogeneities to demonstrate how these heterogeneities may influence model predictions, particularly their implications for public health policymaking.
☆Talk 2：
Title：Emerging disease dynamics in a model coupling within-host and between-host systems
Abstract：Epidemiological models and immunological models have been studied largely independently. However, the two processes (within- and between-host interactions) occur jointly and models that couple the two processes may generate new biological insights. Particularly, the threshold conditions for disease control may be dramatically different when compared with those generated from the epidemiological or immunological models separately. We developed and analyzed an ODE model, which links an SI epidemiological model and an immunological model for pathogen-cell dynamics. When the two sub-systems are considered in isolation, the dynamics are standard and simple. That is, either the infection-free equilibrium is stable or a unique positive equilibrium is stable depending on the relevant reproduction number being less or greater than 1. However, when the two sub-systems are explicitly coupled, the full system exhibits more complex dynamics including backward bifurcations; that is, multiple positive equilibria exist with one of which being stable even if the reproduction number is less than 1. The biological implications of such bifurcations are illustrated using an example concerning the spread and control of toxoplasmosis.
☆Talk 3：
Title：Epidemiological models with arbitrarily distributed disease stages
Abstract：SEIR epidemiological models with the inclusion of quarantine and isolation are considered to study the control and intervention of infectious diseases. A simple ordinary differential equation (ODE) model that assumes exponential distribution for the latent and infectious stages is shown to be inadequate for assessing disease control strategies. By assuming arbitrarily distributed disease stages, a general
integral equation model is developed, of which the simple ODE model is a special case. Analysis of the general model shows that the qualitative disease dynamics are determined by the reproductive number Rc, which is a function of control measures. The integral equation model is shown to reduce to an ODE model when the disease stages are assumed to have a gamma distribution, which is more realistic than the exponential distribution. Outcomes of these models are compared regarding the effectiveness of various intervention policies. Numerical simulations suggest that models that assume exponential and non-exponential stage distribution can produce inconsistent predictions.
☆Talk 4：
Title：Modeling evolutionary dynamics of pathogen
Abstract：The evolutionary strategies that emerge within populations can be dictated by numerous factors, including interactions with other species. In this paper, we explore the consequences of such a scenario using a host–parasite system of human concern. By analyzing the dynamical behaviors of a mathematical model we investigate the evolutionary outcomes resulting from interactions between Schistosoma mansoni and its snail and human hosts. The model includes two types of snail hosts representing resident and mutant types. Using this approach, we focus on establishing evolutionary stable strategies under conditions where snail hosts express different life-histories and when drug treatment is applied to an age-structured population of human hosts. Results from this work demonstrate that the evolutionary trajectories of host–parasite interactions can be varied, and at times, counter-intuitive, based on parasite virulence, host resistance, and drug treatment.