SIR型传染病的模糊控制

针对SIR型传染病数学模型,将疾病的发展程度这一影响传染病传播的主要因素模糊化,利用条件S0〈P和S0〉p对疫情的影响,建立了一种模糊控制模型,使之在疫情发展的不同阶段对应不同控制措施。     

一类格传染病模型稳态解的存在性（英文）

本文基于经典的SIR传染病模型建立了一类格SIR传染病模型.首先,我们应用抽象空间中的隐函数存在定理得到延展定理,该定理可用于讨论具单稳反应项的格微分方程稳态解的存在性.其次,我们计算得到一个扩散系数的界估计,即证明了当扩散系数小于此界时,模型存在无穷多稳态解.     

解析一类SIS和SIR传染病模型的稳定性

借助微分方程建立传染病SIS模型和SIR模型,进一步研究了一类SIS和SIR传染病模型,得出了决定SIS传染病是否发生的阈值;解析了SIR模型无病平衡点和地方平衡点的稳定性.     

基于个体的传染病传播模型应用

西非埃博拉疫情是今年全球重大公共卫生事件,其态势发展一直是国际关注的焦点。为了研判其发展态势,计算流行病学家使用基于个体的传染病传播模型对疫情的发展进行预判,相关结论在疫情防控策略的制定中起到了重要的参考作用。本文综述了基于个体的传染病传播模型的基本原理和发展,并讨论了当前基于个体的传染病传播模型待解决的问题。     

重要呼吸道传染病智慧化监测预警与有效应对策略探讨

全球新发突发传染病不断出现,特别是新型冠状病毒肺炎疫情暴发,对人类健康和社会发展造成巨大影响.本文通过不同渠道收集有关资料,试图梳理我国呼吸道传染病监测预警发展和现状,探讨采用大数据、区块链、人工智能等高新技术,建立智慧化新发突发呼吸道传染病监测预警平台.运用统一关键数据标准、口径,打通部门之间数据壁垒,建立数据共享机制,实时自动采集与传染病有关的数据信息.利用大数据、人工智能、云计算等技术,建立相关数据模型,强化数据分析应用,达到早期识别和预警,做到遏止突发事件的发生发展和传播.同时,在疫情防控过程中能够实时获得疫情的发生发展趋势,及时提出科学精准的有效防控策略.不断提升我国应对突发公共卫生事件的能力和水平,保障我国生物安全.     

基于快鸟影像纹理特性的刺槐林叶面积指数估算

以渭北地区刺槐林为研究对象,通过计算高分辨率影像不同窗口的纹理参数,分别运用一元一次方程、一元二次方程、乘幂模型和指数模型,研究刺槐林野外实测叶面积指数（LAI）与快鸟影像纹理参数间的相关关系,遴选黄土高原地区估算刺槐林LAI最适合的纹理参数以及计算窗口大小.结果表明： 纹理指数对LAI反演精度影响显著,其中角二阶矩阵和熵的估算精度明显高于其他纹理指数;计算窗口的大小显著影响LAI反演精度,非相似度和对比度的反演精度在窗口9×9达到最大,其余纹理指数对于LAI的估算精度随着计算窗口的变大不断降低,在窗口3×3时达到最大;乘幂模型的估算精度低于其他3种方法.     

霍乱的动力学行为分析

与通常的SIR类传染病模型有所不同,本文中所研究的模型考虑霍乱菌受环境和时滞的影响.在文中,当基本再生数R_01时,利用Lyapunov泛函,证明了无病平衡点的全局渐近稳定性.当R_01时,证明正平衡点是局部渐近稳定的和持久的.     

地表反照率不同计算方法对干旱区流域蒸散反演结果的影响——以新疆三工河流域为例

地表蒸散是维持地球表面水量平衡和热量平衡的重要环节,SEBAL模型作为一种快速且有效的反演地表蒸散的遥感物理模型方法,在地表蒸散研究中得到广泛应用。地表反照率作为影响地表能量平衡的重要因素,同时也是SEBAL模型的重要输入参数,因此不同的地表反照率计算方法对SEBAL模型的反演结果有重要影响。以新疆三工河流域为研究区,利用Landsat8 OLI/TIRS数据,以应用最为广泛的Smith地表反照率计算法和Liang地表反照率计算法两种方法计算地表反照率,并输入SEBAL模型中反演日蒸散量,比较分析两种地表反照率计算方法对蒸散反演结果的影响,得出以下结论:(1)两种地表反照率计算方法下经SEBAL模型得到的日蒸散量与实测值拟合程度均较高,不同年份下线性拟合决定系数大于0.75,但是使用Smith方法计算出的地表反照率结合SEBAL模型得到的日蒸散量与实测值拟合程度更高;(2)通过RMSE等精度指标比较两种地表反照率计算方法下基于SEBAL模型反演的日蒸散量,结果显示,Smith地表反照率计算方法下反演的日蒸散量精度略高;(3)Smith地表反照率计算方法下最终得到的区域日均蒸散量高于使用Liang地表反照率计算方法最终得到的区域日均蒸散量,夏季差异最大,差异为0.64 mm/d,其他季节差异较小,差异约为0.2 mm/d。(4)进一步比较研究日内两种地表反照率计算方法得到的地表反照率,结果显示,Smith地表反照率计算法得到的地表反照率均值均小于同时期Liang地表反照率计算法得到的地表反照率均值。     

深圳市南山区学校及托幼机构2010-2012年传染病暴发疫情分析

目的:分析南山区学校幼儿园2010-2012年传染病暴发疫情流行情况,探讨预防及控制疫情暴发的策略及方法.方法:以2010年南山区区级研究课题《学校传染病监测和预警系统研究》中开发的《学校传染病监测和预警系统》中的监测数据为基础数据,同时结合现场流行病学调查结果,采用MS Excel和SPSS对数据进行统计分析.结果:2010-2012年南山区学校及托幼机构共发生519起暴发疫情,其中甲类传染病疫情0起,乙类疫情56起,丙类传染病暴发疫情428起,其他传染病暴发疫情35起.暴发疫情种类以流感、手足口病、流行性出血热、感染性腹泻、水痘等为主,罹患人群共4227人,全区8个街道均有疫情发生,春秋季节暴发疫情较多,尤以3月份和9月份居多.结论:应重点加强学校和托幼机构等易感人群密集场所的传染病监测及疫情报告机制,落实晨检和因病缺课报告工作,提高疫情预警效率并及时采取防控措施,预防及控制流感、感染性腹泻、手足口病、水痘等常见传染病暴发疫情的发生,保护学生及幼儿的身体健康.     

基于PROSAIL模型的水稻田缨帽三角-叶面积指数模型及其应用

缨帽三角(tasseled cap triangle,TCT)-叶面积指数(leaf area index,LAI)等值线模型是一种反映植被叶面积指数等值线在红光(Red)-近红外(NIR)波段反射率组成的光谱空间中分布规律的模型,在此基础上建立LAI遥感反演模型比常用的统计关系模型更加精确.本文利用水稻田实测数据,验证了PROSAIL模型对水稻冠层反射率模拟的适用性,并对模型的输入参数进行率定,最终确定了PROSAIL模型模拟水稻冠层反射率的输入参数的取值范围.在此基础上构建了水稻田TCT-LAI等值线模型,建立了LAI遥感反演所需的查找表,将其分别用于Landsat 8和WorldView 3数据进行水稻田LAI反演.结果表明: 利用基于TCT-LAI等值线模型建立查找表反演的LAI与实测LAI具有良好的线性相关关系,R²=0.76,RMSE=0.47;与Landsat 8的LAI反演结果相比,WorldView 3反演的LAI值域范围更大,数据分布更离散.将Landsat 8、WorldView 3反射率数据重采样至1 km后进行LAI反演, MODIS LAI 产品的反演结果存在明显低估现象.     

污染环境中单种群生存分析

本文研究了污染环境中种群的动力学模型,基于已有结果,考虑了内禀增长率为非线性函数的情形.利用微分方程定性理论中的比较定理,对于种群的持续生存与绝灭给出了判别条件.     

Population Size Affects Adaptation in Complex Ways: Simulations on Empirical Adaptive Landscapes

Do large populations always outcompete smaller ones? Does increasing the mutation rate have a similar effect to increasing the population size, with respect to the adaptation of a population? How important are substitutions in determining the adaptation rate? In this study, we ask how population size and mutation rate interact to affect adaptation on empirical adaptive landscapes. Using such landscapes, we do not need to make many ad hoc assumption about landscape topography, such as about epistatic interactions among mutations or about the distribution of fitness effects. Moreover, we have a better understanding of all the mutations that occur in a population and their effects on the average fitness of the population than we can know in experimental studies. Our results show that the evolutionary dynamics of a population cannot be fully explained by the population mutation rate \(N\mu\); even at constant \(N\mu\), there can be dramatic differences in the adaptation of populations of different sizes. Moreover, the substitution rate of mutations is not always equivalent to the adaptation rate, because we observed populations adapting to high adaptive peaks without fixing any mutations. Finally, in contrast to some theoretical predictions, even on the most rugged landscapes we study, small population size is never an advantage over larger population size. These result show that complex interactions among multiple factors can affect the evolutionary dynamics of populations, and simple models should be taken with caution.     

Talkin' 'bout my generation: environmental variability and cohort effects

In variable environments, it is probable that environmental conditions in the past can influence demographic performance now. Cohort effects occur when these delayed life-history effects are synchronized among groups of individuals in a population. Here we show how plasticity in density-dependent demographic traits throughout the life cycle can lead to cohort effects and that there can be substantial population dynamic consequences of these effects. We show experimentally that density and food conditions early in development can influence subsequent juvenile life-history traits. We also show that conditions early in development can interact with conditions at maturity to shape future adult performance. In fact, conditions such as food availability and density at maturity, like conditions early in development, can generate cohort effects in mature stages. Based on these data, and on current theory about the effects of plasticity generated by historical environments, we make predictions about the consequences of such changes on density-dependent demography and on mite population dynamics. We use a stochastic cohort effects model to generate a range of population dynamics. In accordance with the theory, we find the predicted changes in the strength of density dependence and associated changes in population dynamics and population variability.     

Microsatellite mutations and inferences about human demography

Microsatellites have been widely used as tools for population studies. However, inference about population processes relies on the specification of mutation parameters that are largely unknown and likely to differ across loci. Here, we use data on somatic mutations to investigate the mutation process at 14 tetranucleotide repeats and carry out an advanced multilocus analysis of different demographic scenarios on worldwide population samples. We use a method based on less restrictive assumptions about the mutation process, which is more powerful to detect departures from the null hypothesis of constant population size than other methods previously applied to similar data sets. We detect a signal of population expansion in all samples examined, except for one African sample. As part of this analysis, we identify an "anomalous" locus whose extreme pattern of variation cannot be explained by variability in mutation size. Exaggerated mutation rate is proposed as a possible cause for its unusual variation pattern. We evaluate the effect of using it to infer population histories and show that inferences about demographic histories are markedly affected by its inclusion. In fact, exclusion of the anomalous locus reduces interlocus variability of statistics summarizing population variation and strengthens the evidence in favor of demographic growth.     

Augmenting superpopulation capture-recapture models with population assignment data

Summary Ecologists applying capture–recapture models to animal populations sometimes have access to additional information about individuals' populations of origin (e.g., information about genetics, stable isotopes, etc.). Tests that assign an individual's genotype to its most likely source population are increasingly used. Here we show how to augment a superpopulation capture–recapture model with such information. We consider a single superpopulation model without age structure, and split each entry probability into separate components due to births in situ and immigration. We show that it is possible to estimate these two probabilities separately. We first consider the case of perfect information about population of origin, where we can distinguish individuals born in situ from immigrants with certainty. Then we consider the more realistic case of imperfect information, where we use genetic or other information to assign probabilities to each individual's origin as in situ or outside the population. We use a resampling approach to impute the true population of origin from imperfect assignment information. The integration of data on population of origin with capture–recapture data allows us to determine the contributions of immigration and in situ reproduction to the growth of the population, an issue of importance to ecologists. We illustrate our new models with capture–recapture and genetic assignment data from a population of banner‐tailed kangaroo rats Dipodomys spectabilis in Arizona.     

Linkage and association analysis in pedigrees from different populations

Using the Genetic Analysis Workshop 14 simulated datasets we carried out nonparametric linkage analyses and applied a log-linear method for analysis of case-parent-triad data with stratification on parental mating type. We proposed and applied a random effect modelling approach to explore the impact of population heterogeneity on tests of association between genetic markers and disease status. The estimated genetic effect may appear to be strongly significant in one population but nonsignificant in another population, leading to confusion about interpretation. However, when results are interpreted in the light of a random effects model, both studies may be making similar statements about a genetic effect that varies depending on environment and background.     

Similarity in temporal variation in sex‐biased dispersal over short and long distances in the dark‐eyed junco,Junco hyemalis

Patterns of sex‐biased dispersal (SBD) are typically consistent within taxa, for example female‐biased in birds and male‐biased in mammals, leading to theories about the evolutionary pressures that lead to SBD. However, generalizations about the evolution of sex biases tend to overlook that dispersal is mediated by ecological factors that vary over time. We examined potential temporal variation in between‐ and within‐population dispersal over an 11‐year period in a bird, the dark‐eyed junco (Junco hyemalis). We measured between‐population dispersal patterns using genetic assignment indices and found yearly variation in which sex was more likely to have immigrated. When we measured within‐population spatial genetic structure and mark–recapture dispersal distances, we typically found yearly SBD patterns that mirrored between‐population dispersal, indicating common eco‐evolutionary causes despite expected differences due to the scale of dispersal. However, in years without detectable between‐population sex biases, we found genetic similarity between nearby males within our population. This suggests that, in certain circumstances, ecological pressures may act on within‐population dispersal without affecting dispersal between populations. Alternatively, current analytical tools may be better able to detect within‐population SBD. Future work will investigate potential causes of the observed temporal variation in dispersal patterns and whether they have greater effects on within‐population dispersal.     

From Experiment to Theory: What Can We Learn from Growth Curves?

Finding an appropriate functional form to describe population growth based on key properties of a described system allows making justified predictions about future population development. This information can be of vital importance in all areas of research, ranging from cell growth to global demography. Here, we use this connection between theory and observation to pose the following question: what can we infer about intrinsic properties of a population (i.e., degree of heterogeneity, or dependence on external resources) based on which growth function best fits its growth dynamics? We investigate several nonstandard classes of multi-phase growth curves that capture different stages of population growth; these models include hyperbolic–exponential, exponential–linear, exponential–linear–saturation growth patterns. The constructed models account explicitly for the process of natural selection within inhomogeneous populations. Based on the underlying hypotheses for each of the models, we identify whether the population that it best fits by a particular curve is more likely to be homogeneous or heterogeneous, grow in a density-dependent or frequency-dependent manner, and whether it depends on external resources during any or all stages of its development. We apply these predictions to cancer cell growth and demographic data obtained from the literature. Our theory, if confirmed, can provide an additional biomarker and a predictive tool to complement experimental research.     

The evolution of dispersal – the importance of information about population density and habitat characteristics

The evolution of mobility patterns and dispersal strategies depend on different population, habitat and life history characteristics. The ability to perceive and make use of information about the surrounding environment for dispersal decisions will also differ between organisms. To investigate the evolutionary consequences of such differences, we have used a simulation model with nearest-neighbour dispersal in a metapopulation to study how variation in the ability to obtain and make use of information about habitat quality and conspecific density affects the evolution of dispersal strategies. We found a rather strong influence of variation in information on the overall rate of dispersal in a metapopulation. The highest emigration rate evolved in organisms with no information about either density or habitat quality and the lowest rate was found in organisms with information about both the natal and the neighbouring patches. For organisms that can make use of information about conspecific density, positively density-dependent dispersal evolved in the majority of cases, with the strongest density dependence occurring when an individual only has information about density in the natal patch. However, we also identified situations, involving strong local population fluctuations and frequent local extinctions, where negatively density-dependent dispersal evolved.