Networks and the epidemiology of infectious disease

The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues

Small Area Estimation under Limited Auxiliary Population Data Dealing with Model Violations and their Economic Applications

For evidence-based policy-making, reliable information on socio-economic indicators are essential. Sample surveys have a long tradition of providing cost-efficient information on these indicators. Mostly, there is a demand for the quantity of interest not only at the level of the total population, but especially at the level of sub-populations (geographic areas or sociodemographic groups) called areas or domains. To gain insights into these sub-populations, disaggregated direct estimators can be used, which are calculated solely on area-specific survey data. An area is regarded as ’large’ if the sample size is large enough to enable reliable direct estimates. If the precision of the direct estimates is not sufficient or the sample size is even zero, the area is considered as ’small’. This is particularly common at high spatial or socio-demographic resolutions. Small area estimation (SAE) is promising to overcome this problem without the need for larger and thus more costly surveys. The essence of SAE techniques is that they ’borrow strength’ from other areas to improve their predictions. For this purpose, a model is built on survey data that links additional auxiliary data and exploits area-specific structures. Suitable auxiliary data sources are administrative and register data, such as the census. In many countries, such data are strictly protected by confidentiality agreements and access to population micro-data is a challenge even for gatekeeper organisations. Thus, users have an increased interest in SAE estimators that do not require population micro-data to serve as auxiliary data. In this thesis, new methods in the absence of population micro-data are presented and applications on socio-economic highly relevant indicators are demonstrated. Since different SAE models impose different data requirements, Part I bundles research combining unit-level survey data and limited auxiliary data, e.g., aggregated data such as means, which is a common data situation for users. To account for the unit-level survey information the use of the well-known nested error regression (NER) model is targeted. This model is a special case of a linear mixed model based on several assumptions. But how can users proceed if the model assumptions are not fulfilled? In Part I, this thesis provides two new approaches to deal with this issue. One promising approach is to transform the response. Since several socio-economically relevant variables, such as income, have a skewed distribution, the log-transformation of the response is an established way to meet the assumptions. However, the data-driven log-shift transformation is even more promising because it extends the log by an additional parameter and achieves more flexibility. Chapter 1 introduces both transformations in the absence of population micro-data. A particular challenge is the transformation of the small area means back to the original scale. Hence, the proposed approach introduces aggregate statistics (means and covariances) and kernel density estimation to resolve the issue of lacking population micro-data. Uncertainty estimation is developed, and all methods are evaluated in design- and model-based settings. The proposed method is applied to estimate regional income in Germany using the Socio-Economic Panel and census data. It achieves a clear improvement in reliability, and thus demonstrates the importance of the method. To conveniently enable further applications, this new methodology is implementedin the R package saeTrafo. Chapter 2 describes the various functionalities of the package using publicly available income data. To increase user-friendliness, established unit-level models under transformations and their uncertainty estimations are implemented and the most suitable method is automatically selected. For some applications, however, it is challenging to find a suitable transformation or, more generally, to specify a model, particularly in the presence of complex interactions. For this case, machine learning methods are valuable as a transformation is not necessarily required nor a model needs to be explicitly specified. The semi-parametric framework of mixed effects random forest (MERF) combines the advantages of random forests (robustness against outliers and implicit model-selection) with the ability to model hierarchical dependencies as present in SAE approaches. Chapter 3 introduces MERFs in the absence of population micro-data. As existing random forest algorithm require unit-level auxiliary population data, an alternative strategy is introduced. It adaptively incorporates aggregated auxiliary information through calibration-weights to circumvent unit-level auxiliary data. Applying the proposed method on opportunity costs of care work for Germany using the Socio-Economic Panel and census data demonstrates the gain in accuracy in comparison to both direct estimates and the classical NER model. In contrast to methods using a unit-level sample survey, Part II focuses on the well-known class of area-level SAE models requiring direct estimates from a survey while using (once again) only aggregated population auxiliary data. This thesis presents two particularly relevant applications of this model class. Chapter 4 examines regional consumer price indices (CPIs) in the United Kingdom (UK), contributing to the great interest in monitoring inflation at the spatial level. The SAE challenge is to construct model-based expenditure weights to generate the regional basket of goods and services for the twelve regions of the UK. They are estimated and constructed from the living cost and food survey. Furthermore, available price data are linked to the SAE estimated baskets to produce regional CPIs. The resulting CPI series are closely examined, and smoothing techniques are applied. As a result, the reliability improves, but the CPI series are still too volatile for policy use. However, our research serves as a valuable framework for the creation of a regional CPI in the future. The second application also explores the reliability of the disaggregated estimation of a politically and economically highly relevant indicator, in this case the unemployment rate. The regional target level are the functional urban areas in the German federal state North Rhine-Westphalia. In Chapter 5, two types of unemployment rates - the traditional one and an alternative definition taking commuting into account - are estimated and compared. Direct estimates from the labour force survey are linked with SAE methods to passively collected mobile network data. This alternative data source is real-time available, offers spatial flexible resolutions, and is dynamic. In compliance with data protection rules, we obtain aggregated auxiliary mobile network information from the data provider. The SAE methods improve the reliability, and the resulting predictions show that alternative unemployment rates in German city cores are lower than traditional estimated official unemployment rates indicate

자동차 사양 변경을 실시간 반영하는 데이터 기반 디자인 접근 방법

학위논문 (박사) -- 서울대학교 대학원 : 융합과학기술대학원 융합과학부(지능형융합시스템전공), 2020. 8. 곽노준.The automotive industry is entering a new phase in response to changes in the external environment through the expansion of eco-friendly electric/hydrogen vehicles and the simplification of modules during the manufacturing process. However, in the existing automotive industry, conflicts between structured production guidelines and various stake-holders, who are aligned with periodic production plans, can be problematic. For example, if there is a sudden need to change either production parts or situation-specific designs, it is often difficult for designers to reflect those requirements within the preexisting guidelines. Automotive design includes comprehensive processes that represent the philosophy and ideology of a vehicle, and seeks to derive maximum value from the vehicle specifications. In this study, a system that displays information on parts/module components necessary for real-time design was proposed. Designers will be able to use this system in automotive design processes, based on data from various sources. By applying the system, three channels of information provision were established. These channels will aid in the replacement of specific component parts if an unexpected external problem occurs during the design process, and will help in understanding and using the components in advance. The first approach is to visualize real-time data aggregation in automobile factories using Google Analytics, and to reflect these in self-growing characters to be provided to designers. Through this, it is possible to check production and quality status data in real time without the use of complicated labor resources such as command centers. The second approach is to configure the data flow to be able to recognize and analyze the surrounding situation. This is done by applying the vehicles camera to the CCTV in the inventory and distribution center, as well as the direction inside the vehicle. Therefore, it is possible to identify and record the parts resources and real-time delivery status from the internal camera function without hesitation from existing stakeholders. The final approach is to supply real-time databases of vehicle parts at the site of an accident for on-site repair, using a public API and sensor-based IoT. This allows the designer to obtain information on the behavior of parts to be replaced after accidents involving light contact, so that it can be reflected in the design of the vehicle. The advantage of using these three information channels is that designers can accurately understand and reflect the modules and components that are brought in during the automotive design process. In order to easily compose the interface for the purpose of providing information, the information coming from the three channels is displayed in their respective, case-specific color in the CAD software that designers use in the automobile development process. Its eye tracking usability evaluation makes it easy for business designers to use as well. The improved evaluation process including usability test is also included in this study. The impact of the research is both dashboard application and CAD system as well as data systems from case studies are currently reflected to the design ecosystem of the motors group.자동차 산업은 친환경 전기/수소 자동차의 확대와 제조 공정에서의 모듈 단순화를 통해서 외부 환경의 변화에 따른 새로운 국면을 맞이하고 있다. 하지만 기존의 자동차 산업에서 구조화된 생산 가이드라인과 기간 단위 생산 계획에 맞춰진 여러 이해관계자들과의 갈등은 변화에 대응하는 방안이 관성과 부딪히는 문제로 나타날 수 있다. 예를 들어, 갑작스럽게 생산에 필요한 부품을 변경해야 하거나 특정 상황에 적용되는 디자인을 변경할 경우, 주어진 가이드라인에 따라 디자이너가 직접 의견을 반영하기 어려운 경우가 많다. 자동차 디자인은 차종의 철학과 이념을 나타내고 해당 차량제원으로 최대의 가치를 끌어내고자 하는 종합적인 과정이다. 본 연구에서는 여러 원천의 데이터를 기반으로 자동차 디자인 과정에서 활용할 수 있도록 디자인에 필요한 부품/모듈 구성요소들에 대한 정보를 실시간으로 표시해주는 시스템을 고안하였다. 이를 적용하여 자동차 디자인 과정에서 예상 못한 외부 문제가 발생했을 때 선택할 구성 부품을 대체하거나 사전에 해당 부품을 이해하고 디자인에 활용할 수 있도록 세 가지 정보 제공 채널을 구성하였다. 첫 번째는 자동차 공장 내 실시간 데이터 집계를 Google Analytics를 활용하여 시각화하고, 이를 공장 자체의 자가 성장 캐릭터에 반영하여 디자이너에게 제공하는 방식이다. 이를 통해 종합상황실 등의 복잡한 인력 체계 없이도 생산 및 품질 현황 데이터를 실시간으로 확인 가능하도록 하였다. 두 번째는 차량용 주차보조 센서 카메라를 차량 부착 뿐만 아니라 인벤토리와 물류센터의 CCTV에도 적용하여 주변상황을 인식하고 분석할 수 있도록 구성하였다. 차량의 조립 생산 단계에서 부품 단위의 이동, 운송, 출하를 거쳐 완성차의 주행 단계에 이르기까지 데이터 흐름을 파악하는 것이 디자인 부문에 필요한 정보를 제공할 수 있는 방법으로 활용되었다. 이를 통해 기존 이해관계자들의 큰 반발 없이 내부의 카메라 기능으로부터 부품 리소스와 운송 상태를 실시간 파악 및 기록 가능하도록 하였다. 마지막으로 공공 API와 센서 기반의 사물인터넷을 활용해서 도로 위 차량 사고가 발생한 위치에서의 현장 수리를 위한 차량 부품 즉시 수급 및 데이터베이스화 방법도 개발 되었다. 이는 디자이너로 하여금 가벼운 접촉 사고에서의 부품 교체 행태에 대한 정보를 얻게 하여 차량의 디자인에 반영 가능하도록 하였다. 시나리오를 바탕으로 이 세 가지 정보 제공 채널을 활용할 경우, 자동차 디자인 과정에서 불러들여오는 부품 및 모듈의 구성 요소들을 디자이너가 정확히 알고 반영할 수 있다는 장점이 부각되었다. 정보 제공의 인터페이스를 쉽게 구성하기 위해서, 실제로 디자이너들이 자동차 개발 과정에서 디자인 프로세스 상에서 활용하는 CAD software에 세 가지 채널들로부터 들어오는 정보를 사례별 컬러로 표시하고, 이를 시선추적 사용성 평가를 통해 현업 디자이너들이 사용하기 쉽게 개선한 과정도 본 연구에 포함시켜 설명하였다.1 Introduction 1 1.1 Research Background 1 1.2 Objective and Scope 2 1.3 Environmental Changes 3 1.4 Research Method 3 1.4.1 Causal Inference with Graphical Model 3 1.4.2 Design Thinking Methodology with Co-Evolution 4 1.4.3 Required Resources 4 1.5 Research Flow 4 2 Data-driven Design 7 2.1 Big Data and Data Management 6 2.1.1 Artificial Intelligence and Data Economy 6 2.1.2 API (Application Programming Interface) 7 2.1.3 AI driven Data Management for Designer 7 2.2 Datatype from Automotive Industry 8 2.2.1 Data-driven Management in Automotive Industry 8 2.2.2 Automotive Parts Case Studies 8 2.2.3 Parameter for Generative Design 9 2.3 Examples of Data-driven Design 9 2.3.1 Responsive-reactive 9 2.3.2 Dynamic Document Design 9 2.3.3 Insignts from Data-driven Design 10 3 Benchmark of Data-driven Automotive Design 12 3.1 Method of Global Benchmarking 11 3.2 Automotive Design 11 3.2.1 HMI Design and UI/UX 11 3.2.2 Hardware Design 12 3.2.3 Software Design 12 3.2.4 Convergence Design Process Model 13 3.3 Component Design Management 14 4 Vehicle Specification Design in Mobility Industry 16 4.1 Definition of Vehicle Specification 16 4.2 Field Study 17 4.3 Hypothesis 18 5 Three Preliminary Practical Case Studies for Vehicle Specification to Datadriven 21 5.1 Production Level 31 5.1.1 Background and Input 31 5.1.2 Data Process from Inventory to Designer 41 5.1.3 Output to Designer 51 5.2 Delivery Level 61 5.2.1 Background and Input 61 5.2.2 Data Process from Inventory to Designer 71 5.2.3 Output to Designer 81 5.3 Consumer Level 91 5.3.1 Background and Input 91 5.3.2 Data Process from Inventory to Designer 101 5.3.3 Output to Designer 111 6 Two Applications for Vehicle Designer 86 6.1 Real-time Dashboard DB for Decision Making 123 6.1.1 Searchable Infographic as a Designer's Tool 123 6.1.2 Scope and Method 123 6.1.3 Implementation 123 6.1.4 Result 124 6.1.5 Evaluation 124 6.1.6 Summary 124 6.2 Application to CAD for vehicle designer 124 6.2.1 CAD as a Designer's Tool 124 6.2.2 Scope and Method 125 6.2.3 Implementation and the Display of the CAD Software 125 6.2.4 Result 125 6.2.5 Evaluation: Usability Test with Eyetracking 126 6.2.6 Summary 128 7 Conclusion 96 7.1 Summary of Case Studies and Application Release 129 7.2 Impact of the Research 130 7.3 Further Study 131Docto

Analysing livestock network data for infectious diseases control:an argument for routine data collection in emerging economies

Livestock movements are an important mechanism of infectious disease transmission. Where these are well recorded, network analysis tools have been used to successfully identify system properties, highlight vulnerabilities to transmission, and inform targeted surveillance and control. Here we highlight the main uses of network properties in understanding livestock disease epidemiology and discuss statistical approaches to infer network characteristics from biased or fragmented datasets. We use a ‘hurdle model’ approach that predicts (i) the probability of movement and (ii) the number of livestock moved to generate synthetic ‘complete’ networks of movements between administrative wards, exploiting routinely collected government movement permit data from northern Tanzania. We demonstrate that this model captures a significant amount of the observed variation. Combining the cattle movement network with a spatial between-ward contact layer, we create a multiplex, over which we simulated the spread of ‘fast’ (R0 = 3) and ‘slow’ (R0 = 1.5) pathogens, and assess the effects of random versus targeted disease control interventions (vaccination and movement ban). The targeted interventions substantially outperform those randomly implemented for both fast and slow pathogens. Our findings provide motivation to encourage routine collection and centralization of movement data to construct representative networks. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’

Analysing livestock network data for infectious disease control: an argument for routine data collection in emerging economies

Livestock movements are an important mechanism of infectious disease transmission. Where these are well recorded, network analysis tools have been used to successfully identify system properties, highlight vulnerabilities to transmission, and inform targeted surveillance and control. Here we highlight the main uses of network properties in understanding livestock disease epidemiology and discuss statistical approaches to infer network characteristics from biased or fragmented datasets. We use a ‘hurdle model’ approach that predicts (i) the probability of movement and (ii) the number of livestock moved to generate synthetic ‘complete’ networks of movements between administrative wards, exploiting routinely collected government movement permit data from northern Tanzania. We demonstrate that this model captures a significant amount of the observed variation. Combining the cattle movement network with a spatial between-ward contact layer, we create a multiplex, over which we simulated the spread of ‘fast’ (R0 = 3) and ‘slow’ (R0 = 1.5) pathogens, and assess the effects of random versus targeted disease control interventions (vaccination and movement ban). The targeted interventions substantially outperform those randomly implemented for both fast and slow pathogens. Our findings provide motivation to encourage routine collection and centralization of movement data to construct representative networks. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’

Graph-based Heuristic Solution for Placing Distributed Video Processing Applications on Moving Vehicle Clusters

Vehicular fog computing (VFC) is envisioned as an extension of cloud and mobile edge computing to utilize the rich sensing and processing resources available in vehicles. We focus on slow-moving cars that spend a significant time in urban traffic congestion as a potential pool of onboard sensors, video cameras, and processing capacity. For leveraging the dynamic network and processing resources, we utilize a stochastic mobility model to select nodes with similar mobility patterns. We then design two distributed applications that are scaled in real-time and placed as multiple instances on selected vehicular fog nodes. We handle the unstable vehicular environment by a), Using real vehicle density data to build a realistic mobility model that helps in selecting nodes for service deployment b), Using communitydetection algorithms for selecting a robust vehicular cluster using the predicted mobility behavior of vehicles. The stability of the chosen cluster is validated using a graph centrality measure, and c), Graph-based placement heuristics is developed to find the optimal placement of service graphs based on a multi-objective constrained optimization problem with the objective of efficient resource utilization. The heuristic solves an important problem of processing data generated from distributed devices by balancing the trade-off between increasing the number of service instances to have enough redundancy of processing instances to increase resilience in the service in case of node or link failure, versus reducing their number to minimize resource usage. We compare our heuristic to a mixed integer program (MIP) solution and a first-fit heuristic. Our approach performs better than these comparable schemes in terms of resource utilization and/or has a lesser service latency when compared to an edge computingbased service placement scheme

Colocation aware content sharing in urban transport

People living in urban areas spend a considerable amount of time on public transport. During these periods, opportunities for inter-personal networking present themselves, as many of us now carry electronic devices equipped with Bluetooth or other wireless capabilities. Using these devices, individuals can share content (e.g., music, news or video clips) with fellow travellers that happen to be on the same train or bus. Transferring media takes time; in order to maximise the chances of successfully completing interesting downloads, users should identify neighbours that possess desirable content and who will travel with them for long-enough periods. In this thesis, a peer-to-peer content distribution system for wireless devices is proposed, grounded on three main contributions: (1) a technique to predict colocation durations (2) a mechanism to exclude poorly performing peers and (3) a library advertisement protocol. The prediction scheme works on the observation that people have a high degree of regularity in their movements. Ensuring that content is accurately described and delivered is a challenge in open networks, requiring the use of a trust framework, to avoid devices that do not behave appropriately. Content advertising methodologies are investigated, showing their effect on whether popular material or niche tastes are disseminated. We first validate our assumptions on synthetic and real datasets, particularly movement traces that are comparable to urban environments. We then illustrate real world operation using measurements from mobile devices running our system in the proposed environment. Finally, we demonstrate experimentally on these traces that our content sharing system significantly improves data communication efficiency, and file availability compared to naive approaches