The effect of social distancing on the reach of an epidemic in social networks

How does social distancing affect the reach of an epidemic in social networks? We present Monte Carlo simulation results of a susceptible–infected–removed with social distancing model. The key feature of the model is that individuals are limited in the number of acquaintances that they can interact with, thereby constraining disease transmission to an infectious subnetwork of the original social network. While increased social distancing typically reduces the spread of an infectious disease, the magnitude varies greatly depending on the topology of the network, indicating the need for policies that are network dependent. Our results also reveal the importance of coordinating policies at the ‘global’ level. In particular, the public health benefits from social distancing to a group (e.g. a country) may be completely undone if that group maintains connections with outside groups that are not following suit.

     

Statistical Network Analysis: A Review with Applications to the Coronavirus Disease 2019 Pandemic

As the coronavirus disease 2019 outbreak evolves, statistical network analysis is playing an essential role in informing policy decisions. Therefore, researchers who are new to such studies need to understand the techniques available to them. As a field, statistical network analysis aims to develop methods that account for the complex dependencies found in network data. Over the last few decades, the area has rapidly accumulated methods, including techniques for network modelling and simulating the spread of infectious disease. This article reviews these network modelling techniques and their applications to the coronavirus disease 2019 pandemic.     

Effect of armed conflicts on humanitarian operations: Total factor productivity and efficiency of rural hospitals

We study an important but widely neglected topic in humanitarian operations: armed conflicts. Specifically, this paper empirically analyzes the effect of armed conflicts on the operational performance of first-layer response organizations. Using as a case study the Colombian conflict we investigate the effect of conflict on public rural hospitals' (i) total factor productivity, (ii) efficiency and (iii) efficiency variability. The panel data set (2007–2011) used in this study includes information at the hospital level for 163 hospitals and qualitative data collected from interviews with medical staff from the Colombian Ministry of Health and hospitals in different conflict zones. Our results indicate that armed conflict has a positive effect on total factor productivity, while it has a negative impact on hospital efficiency, and interestingly that efficiency and total factor productivity both increase in post conflict. Finally, the results show that efficiency variability is higher in peace and post-conflict hospitals and lower in medium and severe-conflict hospitals. These results have operations management implications and opportunities for future research related to sourcing decisions, supply chain and workforce flexibility, behavioral impacts on the workforce, and humanitarian response to conflicts.     

基于传染疾病防控的GIS构架实现

随着信息技术的发展,GIS为传染疾病信息的处理以及疾病与人们周围的社会环境、自然环境和卫生服务环境的分析提供了一个理想的平台,它可以很好地阐述环境和健康之间的因果关系。本文论证了GIS与元胞自动机在理论上相结合的可行性并分析了元胞动态演化的相关参数,通过将GIS与元胞自动机技术相结合,建立起基于传染疾病的CA模型,这样就解决了一系列实验数据与现实有出入的问题,从而为公共安全和流行病学的研究提供更为接近现实的资料,更有效地模拟传染疾病发生、发展的复杂性以及可变性,更好地保护人民的生命财产不受损失。     

Analysis of a network structure of the foreign currency exchange market

We analyse structure of the world foreign currency exchange (FX) market viewed as a network of interacting currencies. We analyse daily time series of FX data for a set of 63 currencies, including gold, silver and platinum. We group together all the exchange rates with a common base currency and study each group separately. By applying the methods of filtered correlation matrix we identify clusters of closely related currencies. The clusters are formed typically according to the economical and geographical factors. We also study topology of weighted minimal spanning trees for different network representations (i.e., for different base currencies) and find that in a majority of representations the network has a hierarchical scale-free structure. In addition, we analyse the temporal evolution of the network and detect that its structure is not stable over time. A medium-term trend can be identified which affects the USD node by decreasing its centrality. Our analysis shows also an increasing role of euro in the world’s currency market.     

The use of network analysis to handle semantic differential data

The aim of this paper is to propose a method to transform semantic differential data into a network whose graph representation is interpreted as an empirical network of adjectives. The graph is constituted by the adjectives of the semantic differential task. Two adjectives are linked depending on the scoring assigned by a set of respondents. The proposed approach aims at using concepts and methods of Social Network Analysis to explore the network structure and study roles and positions of dominant adjectives. A simulation design has been realized to assess the stability of results under different conditions, i.e. in order to set the optimal threshold in presence of different data generator processes. A case study carried out on real data shows how the emerging network of adjectives can be effectively used to define the concept arising from a semantic differential task.     

Endemic-epidemic models with discrete-time serial interval distributions for infectious disease prediction

Multivariate count time series models are an important tool for analyzing and predicting the spread of infectious disease. We consider the endemic-epidemic framework, a class of autoregressive models for infectious disease surveillance counts, and replace the default autoregression on counts from the previous time period with more flexible weighting schemes inspired by discrete-time serial interval distributions. We employ three different parametric formulations, each with an additional unknown weighting parameter estimated via a profile likelihood approach, and compare them to an unrestricted nonparametric approach. The new methods are illustrated in a univariate analysis of dengue fever incidence in San Juan, Puerto Rico, and a spatiotemporal study of viral gastroenteritis in the 12 districts of Berlin. We assess the predictive performance of the suggested models and several reference models at various forecast horizons. In both applications, the performance of the endemic-epidemic models is considerably improved by the proposed weighting schemes.     

Reconfiguring a set of coverage-providing facilities under travel time uncertainty

We study networks of facilities that must provide coverage under conditions of uncertainty with respect to travel times and customer demand. We model this uncertainty through a set of scenarios. Since opening new facilities and/or closing existing ones is often quite expensive, we focus on optimal re-configuration of the network, that is finding a facility set that achieves desired thresholds with respect to expected and minimal coverage, while retaining as many of the existing facilities as possible. We illustrate our model with an example of Toronto Fire Service. We demonstrate that relocating just a few facilities can have the same effect as opening a similar number of new ones. We develop exact and approximate solution approaches and test them with computational experiments. Algorithm based on Tabu Search (with certain novel components) appears to be particularly successful for this problem. We also analyze the multi-objective version of the problem, where the expected and minimum coverage levels are treated as objectives in addition to the objective of maximizing the number of pre-existing facilities in the final location set.     

Network dynamics with a nested node set: Sociability in seven villages in Senegal

We propose two complementary ways to deal with a nesting structure in the node set of a network—such a structure may be called a multilevel network, with a node set consisting of several groups. First, within-group ties are distinguished from between-group ties by considering them as two distinct but interrelated networks. Second, effects of nodal variables are differentiated according to the levels of the nesting structure, to prevent ecological fallacies. This is elaborated in a study of two repeated observations of a sociability network in seven villages in Senegal, analyzed using the Stochastic Actor-oriented Model.     

Building entrepreneurial inter-firm networks in an emerging economy: the role of cognitive legitimacy

We take an organizational legitimacy perspective and use data from a large-scale nationally representative study on the state of small business in Saudi Arabia, in order to explore the antecedents to the formation of entrepreneurial ventures’ inter-firm networks in the context of an emerging economy (n?=?331). We argue that entrepreneurial ventures need to overcome a threshold of cognitive legitimacy in order to develop inter-firm ties with a diverse set of large, established firms. Results indicate that having a written business plan and a formal organizational structure are positively associated with the diversity of the new venture’s inter-firm network, while the education level of the entrepreneur does not have a significant effect. In addition, the effect of having a formal organizational structure is stronger for younger ventures. Implications are discussed.     

Hierarchies and Groups in DEA

Conventional applications of data envelopment analysis (DEA) presume the existence of a set of similar decision making units, wherein each unit is evaluated relative to other members of the set. Often, however, the DMUs fall naturally into groupings, giving rise first to the problem of how to view the groups themselves as DMUs, and second to the issue of how to deal with several different ratings for any given DMU when groupings can be formed in different ways. In the present paper we introduce the concept of hierarchical DEA, where efficiency can be viewed at various levels. We provide a means for adjusting the ratings of DMUs at one level to account for the ratings received by the groups (into which these DMUs fall) at a higher level. We also develop models for aggregating different ratings for a DMU arising from different possible groupings. An application of these models to a set of power plants is given.     

OCB and external–internal social networks: effects on individual performance and adaptation to change

In this study, we develop and test a model that links one's internal and external social network structures to his or her willingness and ability to adapt in a changing work context. Using a survey data from 371 employees working in 133 different branches of a large financial firm, we find that individual behaviors that explain task performance and individual adaptation to change are affected by a number of supportive social ties (size of the support network) with which one receives support inside and, mostly, outside the workplace. The results also show that individual performance and adaptation to change are affected by the level of workers' organizational citizenship behavior. We also identify a mediating effect exerted by the informational network structure (mostly an internal network) on the relationship between the support network (mostly an external network) and individual task performance.     

Epidemics and macroeconomic outcomes: Social distancing intensity and duration

We analyze the determination of the optimal intensity and duration of social distancing policy aiming to control the spread of an infectious disease in a simple macroeconomic–epidemiological model. In our setting the social planner wishes to minimize the social costs associated with the levels of disease prevalence and output lost due to social distancing, both during and at the end of epidemic management program. Indeed, by limiting individuals’ ability to freely move or interact with others (since requiring to wear face mask or to maintain physical distance from others, or even forcing some businesses to remain closed), social distancing has on the one hand the effect to reduce the disease incidence and on the other hand to reduce the economy’s productive capacity. We analyze both the early and the advanced epidemic stage intervention strategies highlighting their implications for short and long run health and macroeconomic outcomes. We show that both the intensity and the duration of the optimal social distancing policy may largely vary according to the epidemiological characteristics of specific diseases, and that the balancing of the health benefits and economic costs associated with social distancing may require to accept the disease to reach an endemic state. Focusing in particular on COVID-19 we present a calibration based on Italian data showing how the optimal social distancing policy may vary if implemented at national or at regional level.     

试论2011年全国传染病医院效益分析

2011年全国传染病医院资产规模扩张速度快,有持续发展能力。应加强成本核算,控制成本支出,加强资产管理,提高资产利用率,提升服务质量,增加业务收入,提高工作效率,加快转型发展。     

Predicting criminal recidivism using neural networks

Prediction of criminal recidivism has been extensively studied in criminology with a variety of statistical models. This article proposes the use of neural network (NN) models to address the problem of splitting the population into two groups — non-recidivists and eventual recidivists — based on a set of predictor variables. The results from an empirical study of the classification capabilities of NN on a well-known recidivism data set are presented and discussed in comparison with logistic regression. Analysis indicates that NN models are competitive with, and may offer some advantages over, traditional statistical models in this domain.     

新建传染病医院污水处理工程设计要点

医院污水排放直接影响周边环境,在设计新建传染病医院污水处理方案时应抓住一些要点进行。为此,本文做了一些设计方面的探讨。     

基于神经网络的框架结构损伤诊断的探讨

利用神经网络技术对框架结构的损伤位置和损伤程度进行了研究。用有限元模态分析技术对平面框架结构的损伤情况进行了模拟,得到一系列频率、竖向位移、水平位移变化数据,将这些数据和损伤位置、损伤程度之间的映射关系作为网络训练样本,解决了以往对于对称结构损伤位置判断的难题,同时提出了用分步输出的方法解决了神经网络技术在框架结构应用中训练速度慢、不易收敛的问题。     

Identifying influential user communities on the social network

Nowadays social network services have been popularly used in electronic commerce systems. Users on the social network can develop different relationships based on their common interests and activities. In order to promote the business, it is interesting to explore hidden relationships among users developed on the social network. Such knowledge can be used to locate target users for different advertisements and to provide effective product recommendations. In this paper, we define and study a novel community detection problem that is to discover the hidden community structure in large social networks based on their common interests. We observe that the users typically pay more attention to those users who share similar interests, which enable a way to partition the users into different communities according to their common interests. We propose two algorithms to detect influential communities using common interests in large social networks efficiently and effectively. We conduct our experimental evaluation using a data set from Epinions, which demonstrates that our method achieves 4–11.8% accuracy improvement over the state-of-the-art method.     

The network structure of knowledge sharing among physicians

This paper applies social network analysis in order to model knowledge sharing among hospital physicians. Drawing on the literature on the diffusion of innovation and cooperation in clinical settings, it aims to furnish better understanding of knowledge sharing in two directions: describing how knowledge flows and identifying individual and contextual factors which facilitate its spontaneous spreading. Used to address these issues is a link- tracing sample of about 800 Italian hospital physicians, potentially involved in prescribing a new drug. The paper represents knowledge sharing about the innovation as a network. It therefore specifies Exponential Random Graphs (ERG) or p* models to reconstruct the network structure of knowledge sharing and to test the effect of exogenous factors on the tendency to take action in the network. The results show that knowledge flows informally, exploiting mutual information-seeking relationships, and, consistently with previous studies, locally, with physicians tending to cluster in small groups of proximate and similar peers. Moreover empirical evidence is provided that the propensity to share information with colleagues is greatly affected by individual-specific characteristics, mainly by the experience in the field and the attitude toward the innovation, and by exposure to commercial communication.     

中国医疗卫生事业效益分析之系列研究——2005-2009年全国传染病医院效益分析

目的:中国是肝炎大国,研究全国传染病医院投入与产出效益分析并找出影响因素并提出对策。方法:收集2005年-2009年统计数据,采用平均增长量、平均发展速度、平均增长速度计算出投入、产出指标值。结果:职工人数(包括医生数)、医疗仪器设备、房屋建筑面积(包括业务用房面积)等投入指标保持年均正增长速度。诊疗人次数、入院人数、病床工作日、病床周转次数等社会效益产出指标保持年均正增长速度,病床使用率提高16.4个百分点,出院者平均住院日每年缩短0.55日。净资产经济效益产出指标年均增长速度达32.47%,病毒性肝炎出院者人均医药费用年增长速度4.6%。结论:全国传染病医院属于朝阳事业,但应加大传染病预防的宣传力度,加强卫生管理,从源头消除传染源。加快完善新型农村合作医疗、城镇居民和职工医疗保险。