一类具有状态依赖时滞的阶段结构捕食-食饵模型

建立并分析了一类具有修正因子的状态依赖时滞捕食-食饵模型,其成熟时滞是依赖于食饵数量的单调递减有界函数.首先,证明了解的非负性和一致最终有界性.其次,讨论了模型所有平衡态的存在性及正平衡态的唯一性.最后,证明了三个平衡态的线性稳定性.     

一类具有非单调发生率SIS流行病模型的分析

该文讨论了具有非单调发生率SIS流行病模型,分别建立了带有分布时滞和离散时滞形式的感染个体的恢复时滞模型,同时分析了系统平衡态的稳定性．     

具有阈值策略的非光滑阶段结构害虫增长模型的分析(英文)

为了控制害虫数量不超过一个确定的水平,引入具有阈值控制策略的非光滑的阶段结构的害虫增长模型,称之为Filippov系统.本文主要介绍和讨论了以成年种群数量作为指标的阈值控制策略.在此种情况下,分析了Filippov系统的滑动区域,滑线动力学以及真,假平衡态和伪平衡态的存在性.文章通过理论和数值方法讨论和证明了上述平衡态的稳定性.引入了具有生物应用意义的害虫控制,而得到的结论证明成年种群的数量通过阈值策略控制能够成功的稳定在经济阈值之下.     

一类具连续时滞的三种群互助模型正平衡态的渐近稳定性

本文研究了一类具连续时滞的三种群互助模型,利用上、下解方法及相应的单调迭代方法,获得了该系统存在唯一正常数平衡态及该平衡态是全局渐近稳定的结论,为讨论时滞三种群模型提供了一种有效方法,所得结果也适用于二种群互助模型及不含时滞和扩散项的互助模型,因而推广了已有的一些结论．     

一类具分段常数变量的捕食-食饵系统的Neimark-Sacker分支

讨论了具时滞与分段常数变量的捕食-食饵生态模型的稳定性及Neimark-Sacker分支;通过计算得到连续模型对应的差分模型,基于特征值理论和Schur-Cohn判据得到正平衡态局部渐进稳定的充分条件;以食饵的内禀增长率为分支参数,运用分支理论和中心流形定理分析了Neimark-Sacker分支的存在性与稳定性条件;通过举例和数值模拟验证了理论的正确性。     

一类带有接种和年龄结构的SVIR传染病模型

数学地分析了一类带有接种和年龄结构的SVIR传染病模型的动力学性质,得到了接种疫苗策略φ和年龄a有关的基本再生数R(φ,a)的表达式,证明了当R(0,a)1时,系统中无病平衡态是全局渐近稳定的;当R(φ,a)1时,无病平衡态是不稳定的,此时系统至少存在一地方病平衡态.     

带有食饵保护的Gause型扩散捕食系统的稳定性分析

考虑了具有扩散项和食饵保护的Gause型捕食系统.该模型带有齐次Neumann边界条件.讨论了系统的全局吸引性以及系统非负常数平衡态的局部稳定性和全局稳定性.其条件依赖于食饵保护参数,表明了食饵保护对系统动力学行为的影响.     

一类具有时滞和治愈率的HIV病理模型的稳定性

研究一类具有饱和感染率、治愈率和细胞内时滞的HIV病理模型.首先分析平衡态的存在性与稳定性,然后给出染病平衡态对于任意时滞保持稳定（不稳定）的充分条件,并利用Nyquist准则度量染病平衡点保持稳定的时滞长度.     

香蕉叶病虫害的状态反馈控制(英文)

香蕉叶病虫害是植物病害中非常重要的研究领域,若能积极有效地控制香蕉病害,对具体的农业生产有着巨大的经济价值和指导意义.本文研究具有连续时滞的Logistic增长模型的香蕉叶病虫害的状态脉冲反馈控制系统,利用线性链技巧将系统转成非线性微分方程组,通过Lyapunov方法证明弱时滞核函数下正平衡态全局稳定,最后利用微分方程几何理论中后继函数法得到系统阶一周期解存在的充分条件,并证明该周期解是轨道渐近稳定的.     

含三种群的植物病虫害模型的稳定性

考虑植物、害虫和害虫天敌三种群之间的关系,在人工喷洒杀虫剂作用下,建立一类新的三种群的植物病虫害模型.给出了模型无天敌病虫害平衡点和有天敌病虫害平衡点,利用Hurwitz定理和稳定性第一近似方法讨论了平衡点的稳定性,得到了两类平衡点渐近稳定的充分条件,并用Matlab进行了数值模拟,验证了结论的正确性.     

A Network-based Analysis of the 1861 Hagelloch Measles Data

Summary In this article, we demonstrate a statistical method for fitting the parameters of a sophisticated network and epidemic model to disease data. The pattern of contacts between hosts is described by a class of dyadic independence exponential-family random graph models (ERGMs), whereas the transmission process that runs over the network is modeled as a stochastic susceptible-exposed-infectious-removed (SEIR) epidemic. We fit these models to very detailed data from the 1861 measles outbreak in Hagelloch, Germany. The network models include parameters for all recorded host covariates including age, sex, household, and classroom membership and household location whereas the SEIR epidemic model has exponentially distributed transmission times with gamma-distributed latent and infective periods. This approach allows us to make meaningful statements about the structure of the population-separate from the transmission process-as well as to provide estimates of various biological quantities of interest, such as the effective reproductive number, R. Using reversible jump Markov chain Monte Carlo, we produce samples from the joint posterior distribution of all the parameters of this model-the network, transmission tree, network parameters, and SEIR parameters-and perform Bayesian model selection to find the best-fitting network model. We compare our results with those of previous analyses and show that the ERGM network model better fits the data than a Bernoulli network model previously used. We also provide a software package, written in R, that performs this type of analysis.     

Parameter sensitivity of a model of viral epidemics simulated with Monte Carlo techniques. II. Durations and peaks

This is the second of a series of papers concerning sensitivity analyses of stochastic micropopulation models. A model of epidemic spread of viral infection is used in the series to illustrate the principles and performance of the sensitivity analysis system. For these studies the analysis system now known as SENSEN was redesigned; it can be used with any specialization of the SUMMERS simulation shell. Previous applications of SENSEN and its predecessors studied the sensitivity simultaneously to at most six input parameters. The applicability of SENSEN when twelve input parameters are selected is illustrated by sensitivity analyses of epidemic durations. A time-related output, day of the epidemic at which the peak number of cases are present, is also studied.     

Dissecting recurrent waves of pertussis across the boroughs of London

Pertussis has resurfaced in the UK, with incidence levels not seen since the 1980s. While the fundamental causes of this resurgence remain the subject of much conjecture, the study of historical patterns of pathogen diffusion can be illuminating. Here, we examined time series of pertussis incidence in the boroughs of Greater London from 1982 to 2013 to document the spatial epidemiology of this bacterial infection and to identify the potential drivers of its percolation. The incidence of pertussis over this period is characterized by 3 distinct stages: a period exhibiting declining trends with 4-year inter-epidemic cycles from 1982 to 1994, followed by a deep trough until 2006 and the subsequent resurgence. We observed systematic temporal trends in the age distribution of cases and the fade-out profile of pertussis coincident with increasing national vaccine coverage from 1982 to 1990. To quantify the hierarchy of epidemic phases across the boroughs of London, we used the Hilbert transform. We report a consistent pattern of spatial organization from 1982 to the early 1990s, with some boroughs consistently leading epidemic waves and others routinely lagging. To determine the potential drivers of these geographic patterns, a comprehensive parallel database of borough-specific features was compiled, comprising of demographic, movement and socio-economic factors that were used in statistical analyses to predict epidemic phase relationships among boroughs. Specifically, we used a combination of a feed-forward neural network (FFNN), and SHapley Additive exPlanations (SHAP) values to quantify the contribution of each covariate to model predictions. Our analyses identified a number of predictors of a borough’s historical epidemic phase, specifically the age composition of households, the number of agricultural and skilled manual workers, latitude, the population of public transport commuters and high-occupancy households. Univariate regression analysis of the 2012 epidemic identified the ratio of cumulative unvaccinated children to the total population and population of Pakistan-born population to have moderate positive and negative association, respectively, with the timing of epidemic. In addition to providing a comprehensive overview of contemporary pertussis transmission in a large metropolitan population, this study has identified the characteristics that determine the spatial spread of this bacterium across the boroughs of London.     

Approximate aggregation of a two time scales periodic multi-strain SIS epidemic model: A patchy environment with fast migrations

In this work we consider a spatially distributed periodic multi strain SIS epidemic model. We let susceptible and infected individuals migrate between patches, with periodic migration rates. Considering that migrations are much faster than the epidemic process, we build up a less dimensional (aggregated) system that allows to study some features of the asymptotic behavior of the original model. In particular, we are able to define global reproduction numbers in the non-spatialized aggregated system that serve to decide the eradication or endemicity of the epidemic in the initial spatially distributed nonautonomous model. Comparing these global reproductive numbers with those corresponding to isolated patches we show that adequate periodic fast migrations can in many cases reverse local endemicity and get global eradication of the epidemic.     

Network-based analysis of stochastic SIR epidemic models with random and proportionate mixing

In this paper, we outline the theory of epidemic percolation networks and their use in the analysis of stochastic susceptible-infectious-removed (SIR) epidemic models on undirected contact networks. We then show how the same theory can be used to analyze stochastic SIR models with random and proportionate mixing. The epidemic percolation networks for these models are purely directed because undirected edges disappear in the limit of a large population. In a series of simulations, we show that epidemic percolation networks accurately predict the mean outbreak size and probability and final size of an epidemic for a variety of epidemic models in homogeneous and heterogeneous populations. Finally, we show that epidemic percolation networks can be used to re-derive classical results from several different areas of infectious disease epidemiology. In an Appendix, we show that an epidemic percolation network can be defined for any time-homogeneous stochastic SIR model in a closed population and prove that the distribution of outbreak sizes given the infection of any given node in the SIR model is identical to the distribution of its out-component sizes in the corresponding probability space of epidemic percolation networks. We conclude that the theory of percolation on semi-directed networks provides a very general framework for the analysis of stochastic SIR models in closed populations.     

React or wait: which optimal culling strategy to control infectious diseases in wildlife

We applied optimal control theory to an SI epidemic model to identify optimal culling strategies for diseases management in wildlife. We focused on different forms of the objective function, including linear control, quadratic control, and control with limited amount of resources. Moreover, we identified optimal solutions under different assumptions on disease-free host dynamics, namely: self-regulating logistic growth, Malthusian growth, and the case of negligible demography. We showed that the correct characterization of the disease-free host growth is crucial for defining optimal disease control strategies. By analytical investigations of the model with negligible demography, we demonstrated that the optimal strategy for the linear control can be either to cull at the maximum rate at the very beginning of the epidemic (reactive culling) when the culling cost is low, or never to cull, when culling cost is high. On the other hand, in the cases of quadratic control or limited resources, we demonstrated that the optimal strategy is always reactive. Numerical analyses for hosts with logistic growth showed that, in the case of linear control, the optimal strategy is always reactive when culling cost is low. In contrast, if the culling cost is high, the optimal strategy is to delay control, i.e. not to cull at the onset of the epidemic. Finally, we showed that for diseases with the same basic reproduction number delayed control can be optimal for acute infections, i.e. characterized by high disease-induced mortality and fast dynamics, while reactive control can be optimal for chronic ones.     

Estimation and inference of R0 of an infectious pathogen by a removal method

The basic reproductive ratio, R0, is a central quantity in the investigation and management of infectious pathogens. The standard model for describing stochastic epidemics is the continuous time epidemic birth-and-death process. The incidence data used to fit this model tend to be collected in discrete units (days, weeks, etc.), which makes model fitting, and estimation of R0 difficult. Discrete time epidemic models better match the time scale of data collection but make simplistic assumptions about the stochastic epidemic process. By investigating the nature of the assumptions of a discrete time epidemic model, we derive a bias corrected maximum likelihood estimate of R0 based on the chain binomial model. The resulting 'removal' estimators provide estimates of R0 and the initial susceptible population size from time series of infectious case counts. We illustrate the performance of the estimators on both simulated data and real epidemics. Lastly, we discuss methods to address data collected with observation error.     

Resource allocation for epidemic control over short time horizons

We present a model for allocation of epidemic control resources among a set of interventions. We assume that the epidemic is modeled by a general compartmental epidemic model, and that interventions change one or more of the parameters that describe the epidemic. Associated with each intervention is a 'production function' that relates the amount invested in the intervention to values of parameters in the epidemic model. The goal is to maximize quality-adjusted life years gained or the number of new infections averted over a fixed time horizon, subject to a budget constraint. Unlike previous models, our model allows for interacting populations and non-linear interacting production functions and does not require a long time horizon. We show that an analytical solution to the model may be difficult or impossible to derive, even for simple cases. Therefore, we derive a method of approximating the objective functions. We use the approximations to gain insight into the optimal resource allocation for three problem instances. We also develop heuristics for solving the general resource allocation problem. We present results of numerical studies using our approximations and heuristics. Finally, we discuss implications and applications of this work.     

Inference of Epidemiological Dynamics Based on Simulated Phylogenies Using Birth-Death and Coalescent Models

Quantifying epidemiological dynamics is crucial for understanding and forecasting the spread of an epidemic. The coalescent and the birth-death model are used interchangeably to infer epidemiological parameters from the genealogical relationships of the pathogen population under study, which in turn are inferred from the pathogen genetic sequencing data. To compare the performance of these widely applied models, we performed a simulation study. We simulated phylogenetic trees under the constant rate birth-death model and the coalescent model with a deterministic exponentially growing infected population. For each tree, we re-estimated the epidemiological parameters using both a birth-death and a coalescent based method, implemented as an MCMC procedure in BEAST v2.0. In our analyses that estimate the growth rate of an epidemic based on simulated birth-death trees, the point estimates such as the maximum a posteriori/maximum likelihood estimates are not very different. However, the estimates of uncertainty are very different. The birth-death model had a higher coverage than the coalescent model, i.e. contained the true value in the highest posterior density (HPD) interval more often (2–13% vs. 31–75% error). The coverage of the coalescent decreases with decreasing basic reproductive ratio and increasing sampling probability of infecteds. We hypothesize that the biases in the coalescent are due to the assumption of deterministic rather than stochastic population size changes. Both methods performed reasonably well when analyzing trees simulated under the coalescent. The methods can also identify other key epidemiological parameters as long as one of the parameters is fixed to its true value. In summary, when using genetic data to estimate epidemic dynamics, our results suggest that the birth-death method will be less sensitive to population fluctuations of early outbreaks than the coalescent method that assumes a deterministic exponentially growing infected population.     

An SEI model for sarcoptic mange among chamois

We consider a simple model to study the dynamics of sarcoptic mange in a population of chamois. The epidemiological patterns observed during an epidemic in Italy are reconstructed and key parameters of the model are estimated from field data. In particular, we calculate the basic reproductive ratio R (0), a threshold value for chamois density for the occurrence of an epidemic and the speed of propagation of the epidemic wave. The model is then used to obtain indications on the effect of culling as a possible control measure in a closed population and extended to analyse the spatial diffusion of the epidemic. Our results are in agreement with mange epidemiology and observations, and suggest that intervention could be efficacious in reducing the impact of an epidemic.