Effectiveness of dynamic quarantines against pathogen spread in models of the horticultural trade network

The live plant nursery trade is a potential vector for pests and pathogens, which can spread to natural and developed environments with unintended ecosystem consequences. Simulated, approximately scale-free, tiered horticultural trade networks consisting of growers, wholesalers, and retailers were used to study the efficacy of quarantine inspection and isolation procedures for reducing the spread of infected materials to consumers. The quarantine algorithm temporarily isolated infected nurseries from the rest of the trade network, rewiring the affected trade connections to unquarantined nodes, until the infection was reduced below the detection threshold, at which time the formerly infected nursery was reincorporated into the trade network.Nodes were inspected for infection at regular intervals. Increasing the inspection interval resulted in higher levels of infection with large, system-wide oscillations whose period that matched the inspection interval. The timing of quarantine inspections of the largest hub in the grower tier drove the dynamics of the entire network. Increasing the proportion of growers or wholesalers increased infection level in most networks. Increasing the connectivity within the grower and wholesaler tiers led to large increases in mean infection levels. Focusing quarantine inspection efforts on hubs in the grower and wholesaler tiers may be the most efficient method for reducing the level of infected plant material sold by retailers in real plant trade networks.     

Assessing the impact of a movement network on the spatiotemporal spread of infectious diseases

Summary Linking information on a movement network with space-time data on disease incidence is one of the key challenges in infectious disease epidemiology. In this article, we propose and compare two statistical frameworks for this purpose, namely, parameter-driven (PD) and observation-driven (OD) models. Bayesian inference in PD models is done using integrated nested Laplace approximations, while OD models can be easily fitted with existing software using maximum likelihood. The predictive performance of both formulations is assessed using proper scoring rules. As a case study, the impact of cattle trade on the spatiotemporal spread of Coxiellosis in Swiss cows, 2004-2009, is finally investigated.     

Animal movements and the spread of infectious diseases

Domestic and wild animal population movements are important in the spread of disease. There are many recent examples of disease spread that have occurred as a result of intentional movements of livestock or wildlife. Understanding the volume of these movements and the risks associated with them is fundamental in elucidating the epidemiology of these diseases, some of which might entail zoonotic risks. The importance of the worldwide animal trade is reviewed and the role of the unregulated trade in animals is highlighted. A range of key examples are discussed in which animal movements have resulted in the introduction of pathogens to previously disease-free areas. Measures based on heightened surveillance are proposed that mitigate the risks of new pathogen introductions.     

Social contacts and mixing patterns relevant to the spread of infectious diseases

Background

Mathematical modelling of infectious diseases transmitted by the respiratory or close-contact route (e.g., pandemic influenza) is increasingly being used to determine the impact of possible interventions. Although mixing patterns are known to be crucial determinants for model outcome, researchers often rely on a priori contact assumptions with little or no empirical basis. We conducted a population-based prospective survey of mixing patterns in eight European countries using a common paper-diary methodology.

Methods and Findings

7,290 participants recorded characteristics of 97,904 contacts with different individuals during one day, including age, sex, location, duration, frequency, and occurrence of physical contact. We found that mixing patterns and contact characteristics were remarkably similar across different European countries. Contact patterns were highly assortative with age: schoolchildren and young adults in particular tended to mix with people of the same age. Contacts lasting at least one hour or occurring on a daily basis mostly involved physical contact, while short duration and infrequent contacts tended to be nonphysical. Contacts at home, school, or leisure were more likely to be physical than contacts at the workplace or while travelling. Preliminary modelling indicates that 5- to 19-year-olds are expected to suffer the highest incidence during the initial epidemic phase of an emerging infection transmitted through social contacts measured here when the population is completely susceptible.

Conclusions

To our knowledge, our study provides the first large-scale quantitative approach to contact patterns relevant for infections transmitted by the respiratory or close-contact route, and the results should lead to improved parameterisation of mathematical models used to design control strategies.     

Neighbourhood control policies and the spread of infectious diseases

We present a model of a control programme for a disease outbreak in a population of livestock holdings. Control is achieved by culling infectious holdings when they are discovered and by the pre-emptive culling of livestock on holdings deemed to be at enhanced risk of infection. Because the pre-emptive control programme cannot directly identify exposed holdings, its implementation will result in the removal of both infected and uninfected holdings. This leads to a fundamental trade-off: increased levels of control produce a greater reduction in transmission by removing more exposed holdings, but increase the number of uninfected holdings culled. We derive an expression for the total number of holdings culled during the course of an outbreak and demonstrate that there is an optimal control policy, which minimizes this loss. Using a metapopulation model to incorporate local clustering of infection, we examine a neighbourhood control programme in a locally spreading outbreak. We find that there is an optimal level of control, which increases with increasing basic reproduction ratio, R(0); moreover, implementation of control may be optimal even when R(0) < 1. The total loss to the population is relatively insensitive to the level of control as it increases beyond the optimal level, suggesting that over-control is a safer policy than under-control.     

An improved model considering traditional network properties to assess spatial vulnerability of a network system

We have recently reported a new spatial vulnerability model, which proposed two important curves (i.e., impact curve and neutral curve) and two quantified indices (i.e., absolute spatial vulnerability index and relative spatial vulnerability index) to assess the global impact of spatially local hazards on network systems (Li et al. 2015 Guo XM, Hu XB, Li H, et al. 2015. A study on spatial-temporal rainstorm risk at civil airports in China. J Risk Anal Crisis Response 5:188–198 [Google Scholar]). This paper aims to further investigate and improve the practicability of the new spatial vulnerability model. As some traditional network properties, such as the shortest path, betweenness and connectivity, are often used to assess the vulnerability of network systems, this paper develops a methodology of applying traditional network properties to analyze the spatial vulnerability of network systems. To this end, we firstly describe the new spatial vulnerability model, then analyze its relationship with traditional network properties, and at last conduct a case study on the Beijing subway network to verify this relationship. The results show that, when the global impact of spatially local hazards on network systems is concerned in vulnerability assessment, the combination of some traditional network properties and the new spatial vulnerability model can deliver an effective approach.     

Ichthyogeographic regions and watershed size in the French river Rhône network

Fish community structure can be considered at several scales. The watershed is the largest geographic unit connecting freshwater systems. It describes the upstream-downstream gradient resulting from changes in flow rates. The watershed can also extend over distinct ecological regions. The number of native fish species recorded in each sub-watershed directly connected to the Rhône is strongly correlated with the area of the sub-watershed. Three major groups of sub-watersheds can be retained as ichthyogeographic regions: the Saône region, dominated by a large alluvial plain and rich in species, the Isère region representing the north and central Alpine rivers and the Durance region, strongly influenced by the Mediterranean climate. The River Rhône basin reveals a high diversity of native faunal regions which have to be considered in monitoring and managing the network.     

A branching model for the spread of infectious animal diseases in varying environments

This paper is concerned with a stochastic model, describing outbreaks of infectious diseases that have potentially great animal or human health consequences, and which can result in such severe economic losses that immediate sets of measures need to be taken to curb the spread. During an outbreak of such a disease, the environment that the infectious agent experiences is therefore changing due to the subsequent control measures taken. In our model, we introduce a general branching process in a changing (but not random) environment. With this branching process, we estimate the probability of extinction and the expected number of infected individuals for different control measures. We also use this branching process to calculate the generating function of the number of infected individuals at any given moment. The model and methods are designed using important infections of farmed animals, such as classical swine fever, foot-and-mouth disease and avian influenza as motivating examples, but have a wider application, for example to emerging human infections that lead to strict quarantine of cases and suspected cases (e.g. SARS) and contact and movement restrictions.     

Perspective: human contact patterns and the spread of airborne infectious diseases.

Networks of social contacts channel the transmission of airborne infections. Emerging insights from fields of science as diverse as mathematics, population biology and the social sciences are beginning to reveal how the contact pattern of the hosts determines the spread and evolution of airborne infectious agents.     

Structural proteomics of the SARS coronavirus: a model response to emerging infectious diseases

A number of structural genomics/proteomics initiatives are focused on bacterial or viral pathogens. In this article, we will review the progress of structural proteomics initiatives targeting the SARS coronavirus (SARS-CoV), the etiological agent of the 2003 worldwide epidemic that culminated in approximately 8,000 cases and 800 deaths. The SARS-CoV genome encodes 28 proteins in three distinct classes, many of them with unknown function and sharing low similarity to other proteins. The structures of 16 SARS-CoV proteins or functional domains have been determined to date. Remarkably, eight of these 16 proteins or functional domains have novel folds, indicating the uniqueness of the coronavirus proteins. The results of SARS-CoV structural proteomics initiatives will have several profound biological impacts, including elucidation of the structure-function relationships of coronavirus proteins; identification of targets for the design of anti-viral compounds against SARS-CoV and other coronaviruses; and addition of new protein folds to the fold space, with further understanding of the structure-function relationships for several new protein families. We discuss the use of structural proteomics in response to emerging infectious diseases such as SARS-CoV and to increase preparedness against future emerging coronaviruses.     

A versatile ODE approximation to a network model for the spread of sexually transmitted diseases

 We develop a moment closure approximation (MCA) to a network model of sexually transmitted disease (STD) spread through a steady/casual partnership network. MCA has been used previously to approximate static, regular lattices, whereas application to dynamic, irregular networks is a new endeavour, and application to sociologically-motivated network models has not been attempted. Our goals are 1) to investigate issues relating to the application of moment closure approximations to dynamic and irregular networks, and 2) to understand the impact of concurrent casual partnerships on STD transmission through a population of predominantly steady monogamous partnerships. We are able to derive a moment closure approximation for a dynamic irregular network representing sexual partnership dynamics, however, we are forced to use a triple approximation due to the large error of the standard pair approximation. This example underscores the importance of doing error analysis for moment closure approximations. We also find that a small number of casual partnerships drastically increases the prevalence and rate of spread of the epidemic. Finally, although the approximation is derived for a specific network model, we can recover approximations to a broad range of network models simply by varying model parameters which control the structure of the dynamic network. Thus our moment closure approximation is very flexible in the kinds of network models it can approximate. Received: 26 August 2001 / Revised version: 15 March 2002 / Published online: 23 August 2002 C.T.B. was supported by the NSF. Key words or phrases: Moment closure approximation – Network model – Pair approximation – Sexually transmitted diseases – Steady/casual partnership network     

人工神经网络在发酵工业中的应用

人工神经网络技术具有很强的非线性映射能力,用于系统的非线性建模,具有无可比拟的优势,广泛应用于发酵过程中培养基的优化和系统建模与控制方面,本主要介绍了人工神经网络的基本原理与使用方法,以及BP神经网络在非线性函数逼近的优点,详细介绍了其在发酵培养基优化,连续搅拌反应器神经网络估计,分批发酵及补料分批发酵过程建模与控制优化中的应用实例。     

基于组合药物网络的复杂疾病药物组合模式研究

人体作为一个复杂的功能系统。疾病的发生和发展,尤其是复杂疾病,其病理过程往往涉及多环节、多系统。单一药物难以满足复杂疾病的治疗要求,组合药物成为未来药物发展的新趋势。本文在构建组合药物网络的基础上进行MCODE算法聚类,得到33个独立且内部联系紧密的药物模块。其中26组药物模块用于治疗单一复杂疾病。通过详细分析癌症、疼痛、银屑病、细菌感染、类风湿性关节炎、化疗呕吐这六种复杂疾病,归纳总结出这六种疾病的药物组合模式,从而提出复杂疾病多角度的治疗策略。     

The use of different statistical methods in epidemiological analysis to assess the seasonality and territorial spread of infectious diseases

To solve the problems connected with the evaluation of the seasonal and territorial distribution of infectious diseases, indications and limitations for the use of the following statistical methods have been worked out: the calculation of the monthly morbidity level, the proportion of cases of infectious diseases falling on the months of seasonal morbidity rises, the proportion of such cases appearing due to the influence of seasonal factors, the total and partial indices, the average monthly rate of increase in seasonal morbidity, the complex evaluation of seasonal morbidity, the evaluation of the nonparallelism of two curves, the determination of the mean quadratic deviations and Shannon's entropy. The algorithms based on these methods lie in the foundation of 11 computer programs, forming the software complex "Epidanalysis" and provided with the service menu permitting an epidemiologist to proceed from the task via indications and limitations to the corresponding program. The programs are intended for different types of computers (IBM PC XT, EC-1640, Robotron 1715).     

Biogeographical network analysis of plant species distribution in the Mediterranean region

The delimitation of bioregions helps to understand historical and ecological drivers of species distribution. In this work, we performed a network analysis of the spatial distribution patterns of plants in south of France (Languedoc‐Roussillon and Provence‐Alpes‐Côte d'Azur) to analyze the biogeographical structure of the French Mediterranean flora at different scales. We used a network approach to identify and characterize biogeographical regions, based on a large database containing 2.5 million of geolocalized plant records corresponding to more than 3,500 plant species. This methodology is performed following five steps, from the biogeographical bipartite network construction to the identification of biogeographical regions under the form of spatial network communities, the analysis of their interactions, and the identification of clusters of plant species based on the species contribution to the biogeographical regions. First, we identified two sub‐networks that distinguish Mediterranean and temperate biota. Then, we separated eight statistically significant bioregions that present a complex spatial structure. Some of them are spatially well delimited and match with particular geological entities. On the other hand, fuzzy transitions arise between adjacent bioregions that share a common geological setting, but are spread along a climatic gradient. The proposed network approach illustrates the biogeographical structure of the flora in southern France and provides precise insights into the relationships between bioregions. This approach sheds light on ecological drivers shaping the distribution of Mediterranean biota: The interplay between a climatic gradient and geological substrate shapes biodiversity patterns. Finally, this work exemplifies why fragmented distributions are common in the Mediterranean region, isolating groups of species that share a similar eco‐evolutionary history.     

The spread of infectious diseases in spatially structured populations: an invasory pair approximation

The invasion of new species and the spread of emergent infectious diseases in spatially structured populations has stimulated the study of explicit spatial models such as cellular automata, network models and lattice models. However, the analytic intractability of these models calls for the development of tractable mathematical approximations that can capture the dynamics of discrete, spatially-structured populations. Here we explore moment closure approximations for the invasion of an SIS epidemic on a regular lattice. We use moment closure methods to derive an expression for the basic reproductive number, R(0), in a lattice population. On lattices, R(0) should be bounded above by the number of neighbors per individual. However, we show that conventional pair approximations actually predict unbounded growth in R(0) with increasing transmission rates. To correct this problem, we propose an 'invasory' pair approximation which yields a relatively simple expression for R(0) that remains bounded above, and also predicts R(0) values from lattice model simulations more accurately than conventional pair and triple approximations. The invasory pair approximation is applicable to any spatial model, since it takes into account characteristics of invasions that are common to all spatially structured populations.     

反向遗传学技术在猪瘟病毒研究中的应用

猪瘟目前在许多国家流行并对养猪业造成巨大损失。虽然常规疫苗(如中国猪瘟兔化弱毒疫苗,即C株)在猪瘟防控中发挥巨大作用,但近年来在猪瘟防控中出现的新情况,如非典型感染、持续性感染及免疫失败等;同时目前世界上许多国家正开展的猪瘟扑灭计划使得弱毒疫苗的应用受到很大限制。因此,加强猪瘟病毒在致病机理、传播机制等方面的研究以及加快新型猪瘟疫苗的开发是当务之急。近年来,反向遗传学技术的发展为猪瘟病毒基因功能研究和疫苗制备方面开辟了新思路。以下回顾了反向遗传操作技术在猪瘟病毒基因功能研究与标记疫苗株构建方面的研究进展,同时提出了该领域目前面临的问题,并对其未来发展方向进行了展望。