Data Science: A Powerful Catalyst for Cross-Sector Collaborations to Transform the Future of Global Health—Developing a New Interactive Relational Mapping Tool (Demo)

ABSTRACT

The increasingly complex and rapidly changing global health and socioeconomic landscape requires fundamentally new ways of thinking, acting, and collaborating to solve growing systems challenges. Cross-sectoral collaborations between governments, businesses, international organizations, private investors, academia, and nonprofits are essential for lasting success in achieving the Sustainable Development Goals (SDGs), and securing a prosperous future for the health and well-being of all people (United Nations, n.d United Nations. (n.d.). SDGs: Sustainable development knowledge platform. Sustainable Development United Nations. Retrieved from https://sustainabledevelopment.un.org/ [Google Scholar].). Our aim is to use data science and innovative technologies to map diverse stakeholders and their initiatives around SDGs and specific health targets—with particular focus on SDG 3 (Good Health & Well Being) and SDG 17 (Partnerships for the Goals)—to accelerate cross-sector and multidisciplinary collaborations. Initially, the mapping tool focuses on Geneva, Switzerland as the world center of global health diplomacy with over 80 key stakeholders and influencers present. As we develop the next level pilot, we aim to build on users’ interests, with a potential focus on non-communicable diseases (NCDs) as one of the emerging and most pressing global health issues that requires new collaborative approaches. Building on this pilot, we can later expand beyond only SDG 3 to other SDGs.     

非线性面板数据聚类方法研究

对于一类变量非线性相关的面板数据,现有的基于线性算法的面板数据聚类方法并不能准确地度量样本间的相似性,且聚类结果的可解释性低。综合考虑变量非线性相关问题及聚类结果可解释性问题,提出一种非线性面板数据的聚类方法,通过非线性核主成分算法实现对样本相似性的测度,并基于混合高斯模型进行样本概率聚类,实证表明该方法的有效性及其对聚类结果的可解释性有所提高。     

All Data are Wrong,but Some are Useful? Advocating the Need for Data Auditing

In a recent article from the Annals of Applied Statistics, Cox discussed the main phases of applied statistical research ranging from clarifying study objectives to final data analysis and interpreting results. As an incidental remark to these main phases, we advocate that beyond cleaning and preprocessing the data, it is a good practice to audit the data to determine if they can be trusted at all. A case study based on Ghanaian Official Fishery Statistics is used to illustrate this need, with Benford's law being the tool used to carrying out the data audit. Supplementary materials for this article are available online.     

Ignorability conditions for frequentist non parametric analysis of conditional distributions with incomplete data

Rubin (1976 Rubin, D.B. (1976). Inference and missing data. Biometrika 63(3):581–592.[Crossref], [Web of Science ®] , [Google Scholar]) derived general conditions under which inferences that ignore missing data are valid. These conditions are sufficient but not generally necessary, and therefore may be relaxed in some special cases. We consider here the case of frequentist estimation of a conditional cdf subject to missing outcomes. We partition a set of data into outcome, conditioning, and latent variables, all of which potentially affect the probability of a missing response. We describe sufficient conditions under which a complete-case estimate of the conditional cdf of the outcome given the conditioning variable is unbiased. We use simulations on a renal transplant data set (Dienemann et al.) to illustrate the implications of these results.     

人口受教育程度对居民消费的影响研究--基于动态面板分位数回归法

本文采用1996～2015年的31个省市自治区宏观数据,使用动态面板分位数回归方法研究人口受教育程度和居民消费的数量关系。研究结果表明,人口受教育程度对消费有着较为显著的影响,且人口受教育程度对中等消费支出水平的影响略大于高、低消费支出水平的影响,这说明中等消费水平的居民消费受到人口受教育程度的影响更加敏感和有弹性。另外,模型中其他各解释变量系数在各分位数上的差异不大,说明这些影响因素对于不同消费支出水平的消费影响没有太明显差异。     

Nominal comparability is not enough: (In-)equivalence of construct validity of cross-national measures of educational attainment in the European Social Survey

Educational attainment is a core social background variable covered in each and every survey of individuals. Since educational institutions and qualifications are difficult to compare across countries, cross-national surveys pose a particular challenge to the measurement of educational attainment. This study performs a comparative construct validation of a number of cross-national measures of education using the European Social Survey. The measures comprise two versions of the International Standard Classification of Education (ISCED), the education scheme developed in the project ‘Comparative Analysis of Social Mobility in Industrial Nations’ (CASMIN) and hypothetical as well as actual years of education. The first ISCED measure corresponds to the well-known main ISCED levels. The second one, the European Survey Version of ISCED (ES-ISCED) developed for this study, represents an effort to reflect different types of education within levels of education by considering ISCED sub-dimensions, most importantly ‘programme orientation’. Using linear regression models, it is shown how much explanatory power educational attainment loses when different cross-national variables are used, as compared to country-specific educational attainment variables (CSEVs), and how these losses vary across measures and countries. The dependent variable used for the construct validation is social status as measured by the International Socio-Economic Index (ISEI). Results suggest that harmonisation always entails some loss of explanatory power for at least a few countries. However, there are clear performance differences between the comparable measures in terms of both the average amount of losses as well as the distribution of losses across countries. The use of actual years of education as well as the levels-only ISCED strongly attenuates the education-social status association on average, but also to very different degrees across countries. CASMIN and ES-ISCED fare considerably better: they show the lowest losses of explanatory power and the lowest variation of losses across countries. Hypothetical years of education lie in between. Some practical implications are then proposed, e.g. on how to implement cross-national measures of educational attainment in international surveys.     

NUSAP Method for Evaluating the Data Quality in a Quantitative Microbial Risk Assessment Model for Salmonella in the Pork Production Chain

The numeral unit spread assessment pedigree (NUSAP) system was implemented to evaluate the quality of input parameters in a quantitative microbial risk assessment (QMRA) model for Salmonella spp. in minced pork meat. The input parameters were grouped according to four successive exposure pathways: (1) primary production (2) transport, holding, and slaughterhouse, (3) postprocessing, distribution, and storage, and (4) preparation and consumption. An inventory of 101 potential input parameters was used for building the QMRA model. The characteristics of each parameter were defined using a standardized procedure to assess (1) the source of information, (2) the sampling methodology and sample size, and (3) the distributional properties of the estimate. Each parameter was scored by a panel of experts using a pedigree matrix containing four criteria (proxy, empirical basis, method, and validation) to assess the quality, and this was graphically represented by means of kite diagrams. The parameters obtained significantly lower scores for the validation criterion as compared with the other criteria. Overall strengths of parameters related to the primary production module were significantly stronger compared to the other modules (the transport, holding, and slaughterhouse module, the processing, distribution, and storage module, and the preparation and consumption module). The pedigree assessment contributed to select 20 parameters, which were subsequently introduced in the QMRA model. The NUSAP methodology and kite diagrams are objective tools to discuss and visualize the quality of the parameters in a structured way. These two tools can be used in the selection procedure of input parameters for a QMRA, and can lead to a more transparent quality assurance in the QMRA.     

基于流数据挖掘的网络流量异常检测及分析研究

网络流量异常检测及分析是网络及安全管理领域的重要研究内容.本文探讨了网络流量异常的种类、网络流量异常检测的方法,分析了基于传统检测方法在网络流量异常检测应用中存在的问题.并重点对基于流数据模型的网络流量异常检测进行了研究,综述了已有流数据挖掘研究方法在网络流量异常检测中的研究进展.最后,本文对现有研究工作存在的问题及未来的研究方向进行了探讨.     

Column generation with free replicability in DEA

The evaluation of efficiency scores in data envelopment analysis is based on the construction of artificial decision making units subject to some assumptions, usually requiring convexity of the production possibility set. This demands divisibility in input and output, which is not always possible. The so-called free replicability model, proposed by Henry Tulkens, permits input and output to enter in only discrete amounts. The model is of a mixed integer programming type, for which the number of variables, here corresponding to the decision making units, may be critical in order to reach an optimal solution.     

Electronic Data Interchange: Research Review and Future Directions*

For nearly two decades, electronic data interchange (EDI) has been widely viewed as a technology pivotal to supply chain management that has also provided benefits to firms on multiple levels. Despite a substantial body of literature, there are a number of conflicting and inconclusive research results in this field. In this study, we synthesize the diverse body of research in EDI by organizing the literature into an initial theoretical framework. Based on a meta‐analysis of results from the empirical literature, we seek to clarify conflicting results from the literature in order to develop a more unified theoretical framework of contextual variables associated with EDI adoption factors and outcomes. From a managerial standpoint, our literature‐based framework offers a set of guidelines for making successful EDI adoption and implementation decisions.