Today's supply chains face increasing volatility on many fronts. From the shop-floor where machines break and suppliers fail to the boardrooms where unanticipated price inflation erodes profi tability. Turbulence is the new normal. To remain competitive and weather these (daily) storms, supply chains need to move away from an effi ciency mindset towards a resilience mindset. For over a little more than a decade industry and academia have awakened to this reality. Academic literature and case studies show that there is no longer a shortage of resilience strategies and designs. Unfortunately, industry still lacks the tools with which to assess and evaluate the effectiveness of such strategies and designs. Without the ability to quantify the benefi t it is impossible to motivate the cost. This thesis adds one piece to the puzzle of quantifying supply chain vulnerability. Speci fically, it focussed on supply chains within urban areas. It addresses the question: "How does a supply chain's network design (internal con figuration) and its dependence on the underlying road network (external circumstances) make it more or less vulnerable to disruptions of the road network?" Multilayered Complex Network Theory (CNT) held promise as a modelling approach that could capture the complexity of the dependence between a logical supply chain network and the physical road network that underpins it. This approach addressed two research gaps in complex network theory applications. In the supply chain arena CNT applications have reaped many benefi ts but the majority of studies regarded single-layer networks that model only supply chain relations. There were no studies found where the dependence of supply chain layers on underlying physical infrastructure was modelled in a multilayered manner. Road network applications offered many more multilayered applications but these primarily focussed on passenger transport, not freight transport. The first artefact developed in the thesis was a multilayered complex network formulation representing a logical (supply chain) layer placed on a physical (road infrastructure) layer. The individual layers had predefi ned network characteristics and on their own could not hint at the inherent vulnerability that the system as a whole might have. From the multilayered formulation, the collection of shortest paths emerged. This is the collection of all shortest path alternatives within a network. The collection of shortest paths is the unique fingerprint of each multilayered network instance. The key to understanding vulnerability lies within the characteristics of the collection of shortest paths. Three standard supply chain network archetypes were de fined namely the Fully Connected (FC), Single Hub (SH) and Double Hub (DH) archetypes. A sample of 500 theoretical multilayered network instances was generated for each archetype. These theoretical instances were subjected to three link-based progressive targeted disruption simulations to study the vulnerability characteristics of the collection of shortest paths. Two of the simulations used relative link betweenness to prioritise the disruptions while the third used the concept of network skeletons as captured by link salience. The results from these simulations showed that the link betweenness strategies were far more effective than the link salience strategy. From these results three aspects of vulnerability were identifi ed. Redundancy quantifi es the number of alternative shortest paths available to an instance. Overlap measures to what degree the shortest path sets of an instance overlap and have road segments in common. Effi ciency step-change is a measure of the magnitude of the "shock" absorbed by the shortest paths of an instance during a disruption. For each of these aspects one or more metrics were defi ned. This suite of vulnerability metrics is the second artefact produced by the thesis. The design of the artefacts itself, although novel, was not considered research. It is the insights derived during analysis of the artefacts' performance that contributes to the body of knowledge. Link-based progressive random disturbance simulations were used to assess the ability of the vulnerability metrics to quantify supply chain vulnerability. It was found that none of the de fined vulnerability aspects are good stand-alone predictors of vulnerability. The multilayered nature and random disturbance protocol result in vulnerability being more multi-faceted than initially imagined. Nonetheless, the formulation of the multilayered network proved useful and intuitive and even though the vulnerability metrics fail as predictors they still succeed in capturing shortest path phenomena that would lead to vulnerability under non-random protocols. To validate the fi ndings from the theoretical instances, link-based random disturbance simulations were executed on 191 case study instances. These instances were extracted from real-life data in three urban areas in South Africa, namely Gauteng Province (GT), City of Cape Town (CoCT) and eThekwini Metropolitan Municipality (ET). The case study instances showed marked deviations from the assumptions underlying the theoretical instances. Despite these differences, the multilayered formulation still enables the quanti fication of the relationship between supply chain structure and road infrastructure. The performance of the vulnerability metrics in the case study corroborates the findings from the theoretical instances. Although the suite of vulnerability metrics was unsuccessful in quantifying or predicting vulnerability in both the theoretical and case study instances, the rationale behind their development is sound. Future work that will result in more effective metrics is outlined in this thesis. On the one hand the development of a more realistic disruption strategy is suggested. Road network disruptions are neither completely random nor specifi cally targeted. Important segments with greater tra ffic loads are more likely to be disrupted, but the reality is that disruptions such as accidents, equipment failure or road maintenance could really occur anywhere on the network. A more realistic disruption strategy would lie somewhere on the continuum between targeted and random disruptions. Other future work suggests the refi nement of both artefacts by incorporating link weights in both the logical and physical layers. An unanticipated fi nding from this thesis is that future research in the fi eld may be expedited if theory-building emanates from real-life empirical networks as opposed to theoretically generated networks. Expanding the scope of the case study, characterising the true network archetypes found in practice and increasing the number of case study samples is a high priority for future work. / Thesis (PhD)--University of Pretoria, 2018. / National Research Foundation of South Africa (Grant UID: 105519). Partial funding of doctoral research. / Industrial and Systems Engineering / PhD / Unrestricted

Highway bridges are among the most vulnerable and expensive components in transportation networks. In response, the Government of Ontario has allocated $26 billion in the next 10 years to address issues pertaining to aging bridge and deteriorating highway infrastructure in the province. Although several approaches have been developed to guide their rehabilitation, most bridge rehabilitation approaches are focused on the component level (individual bridge) in a relative isolation of other bridges in the network. The current study utilizes a complex network theoretic approach to quantify the topological characteristics of the Ontario Bridge Network (OBN) and subsequently evaluate the OBN robustness and vulnerability characteristics. These measures are then integrated in the development of a Multi Scale Bridge Classification (MSBC) approach—an innovative classification approach that links the OBN component level data (i.e., Bridge Condition Index and year of construction, etc.) to the corresponding dynamic network-level measures. The novel approach calls for a paradigm shift in the strategy governing classifying and prioritizing bridge rehabilitation projects based on bridge criticality within the entire network, rather than only the individual bridge’s structural conditions. The model was also used to identify the most critical bridges in the OBN under different disruptions to facilitate rapid implementation of the study results. / Thesis / Master of Applied Science (MASc)

Battery electric buses (BEBs) bring several advantages to public transportation systems. With fixed routes and scheduled trips, the implementation of BEBs in the transit context is considered a seamless transition towards a zero greenhouse gases transit system. However, energy consumption uncertainty is a significant deterrent for mainstream implementation of BEBs. Demonstration and trial projects are often conducted to better understand the uncertainty in energy consumption (EC). However, the BEB's energy consumption varies due to uncertainty in operational, topological, and environmental attributes. This thesis aims at developing simulation, data-driven, and low-resolution models using big data to quantify the EC of BEBs, with the overarching goal of developing a comprehensive planning framework for BEB implementation in bus transit networks. This aim is achieved through four interwind objectives. 1) Quantify the operational and topological characteristics of bus transit networks using complex network theory. This objective provides a fundamental base to understanding the behaviour of bus transit networks under disruptive events. 2) Investigate the impacts of the vehicular, operational, topological, and external parameters on the EC of BEBs. 3) Develop and evaluate the feasibility of big-data analytics and data-driven models to numerically estimate BEB's EC. 4) Create an open-source low-resolution data-based framework to estimate the EC of BEBs. This framework integrates the modelling efforts in objectives 1-3 and offers practical knowledge for transit providers. Overall, the thesis provides genuine contributions to BEB research and offers a practical framework for addressing the EC uncertainty associated with BEB operation in the transit context. Further, the results offer transit planners the means to set up the optimum transit operations profile that improves BEB energy utilization, and in turn, reduces transit-related greenhouse gases. / Thesis / Doctor of Engineering (DEng)

Modern societies are fully dependent on critical infrastructures networks to support the economy, security, and prosperity. Energy infrastructure network is of paramount importance to our societies. As a pillar of the economy, it is necessary that energy infrastructure networks continue to operate safely and be resilient to provide reliable power to other critical infrastructure networks. Nonetheless, frequent large-scale blackouts in recent years have highlighted the vulnerability in the power grids, where disruptions can trigger cascading failures causing a catastrophic regional-level blackout. Such catastrophic blackouts call for a systemic risk assessment approach whereby the entire network/system is assessed against such failures considering the dynamic power flow within. However, the lack of detailed data combining both topological and functional information, and the computational resources typically required for large-scale modelling, considering also operational corrective actions, have impeded large-scale resilience studies. In this respect, the research in the present dissertation focuses on investigating, analyzing, and evaluating the vulnerability of power grid infrastructure networks in an effort to enhance their resilience. Through a Complex Network Theory (CNT) lens, the power grid robustness has been evaluated against random and targeted attacks through evaluating a family of centrality measures. The results shows that CNT models provide a quick and potential indication to identify key network components, which support regulators and operators in making informed decisions to maintain and upgrade the network, constrained by the tolerable risk and allocated financial resources. Furthermore, a dynamic Cascade Failure Model (CFM) has been employed to develop a Physical Flow-Based Model (PFBM). The CFM considers the operational corrective actions in case of failure to rebalance the supply and demand (i.e., dispatch and load shedding). The CFM was subsequently utilized to construct a grid vulnerability map function of the Link Vulnerability Index (LVI), which can be used to rank the line maintenance priority. In addition, a Node Importance Index (NII) has been developed for power substations ranking according to the resulting cascade failure size. The results from CNT and CFM approaches were compared to address the impact of considering the physical behavior of the power grid. The comparison results indicate that relying solely on CNT topology-based model could result in erroneous conclusions pertaining to the grid behavior. Moving forward, a systemic risk mitigation strategy based on the Intentional Controlled Islanding (ICI) approach has been introduced to suppress the failure propagation. The proposed mitigation strategy integrated the operation- with structure-guided strategies has shown excellent capabilities in terms of enhancing the network robustness and minimizing the possibility of catastrophic large-scale blackouts. This research demonstrates the model application on a real large-scale network with data ranging from low to high voltage. In the future, the CFM model can be integrated with other critical infrastructure network systems to establish a network-of-networks interaction model for assessing the systemic risk throughout and between multiple network layers. Understanding the interdependence between different networks will provide stakeholders with insight on enhancing resilience and support policymakers in making informed decisions pertaining to the tolerable systemic risk level to take reliable actions under abnormal conditions. / Thesis / Doctor of Philosophy (PhD)

Mining of uranium ore and its extraction using the milling process generates solid and liquid waste, commonly termed uranium mine tailings. Uranium mine tailings is radioactive, as it consists of residual uranium, thorium, and radium, which amounts to 85% of the original ore’s radioactivity. Due to the extensively long half-lives of uranium (4.5x109 years), thorium (75,400 years), and radium (1,620 years) and their harmful radioactive, it is imperative to isolate uranium mine tailings from the environment for a longer period. Containment of uranium mine tailings in dam-like structures, called uranium mine tailings dam (UMTD), is the most followed disposal and storage method. Like a conventional water retention dam, UMTDs are also susceptible to failure, mainly due to adverse weather conditions. Once the UMTD fails, a fraction of the radioactive tailings infiltrates and migrate through the vadose zone contaminating the groundwater sources underlying it. Radionuclide behavior and migration in the subsurface are affected by several environmental factors. To minimize the uncertainty and improve current radionuclide fate and transport models, it is vital to study these factors and any interdependence existing between them. This study aims to understand these environmental factors by i) enlisting the factors affecting subsurface radionuclide migration through scoping review of articles and reports, and ii) analyzing the interdependence existing between the factors using the complex network theory (CNT) approach and identifying the dominant factors among them. Factors such as chemical and biological characteristics of soil stratigraphy, groundwater, and radioactive tailings plume, meteorological, and hydrogeological are found to influence radionuclide behavior and transport mechanisms in the vadose zone. CNT approach described soil microorganisms, fraction of organic carbon, infiltration rate of the soil, transmissivity, clay fraction in the soil, particulates in groundwater, and infiltrating rainwater as dominant factors in the NoF based on their centrality measures and sensitivity analysis of the network of factors (NoF). Any uncertainty associated with these factors will affect and propagate through the model. Hence, sufficient resources should be directed in the future to characterize these factors and minimize their uncertainty, which will lead to developing reliable fate and transport models for radionuclides. / Thesis / Master of Applied Science (MASc) / Waste products from uranium mining and milling operations are called uranium mine tailings, which are radioactive. Generally, uranium mine tailings are disposed of and isolated in dam-like structures referred to as uranium mine tailings dams (UMTD). One of the most common causes of UMTD failure is extreme weather conditions. When a UMTD fails, a part of tailings, consisting of radionuclides uranium, thorium, and radium, infiltrate into the subsurface through the vadose zone. Radionuclide behavior and transport in the subsurface is influenced by several environmental factors. The objective of the present study is to understand the factors affecting radionuclide migration by i) conducting a scoping review on radionuclide migration in the subsurface to describe the factors studied in the literature, and ii) understanding and analyzing any relation among the factors and deriving the most dominant factors based on their relation. This study can be used further to develop accurate and reliable radionuclide fate and transport models with minimal uncertainty.

A key attribute of resilience, robustness serves as a predictor of infrastructure system performance under disruptions, thus informing proactive infrastructure risk management. A literature review indicated that previous studies did not consider some key factors that can influence the robustness of Air Transportation Infrastructure Networks (ATIN) and thus their (system-level cascade) systemic risk management processes. In this respect, the current study first assesses existing and then develops a new methodology to quantify the robustness of ATIN. Specifically, based on integrating travel time and flight frequency, the study develops alternative best route and link weight approaches to assess key ATIN robustness measures and relevant operating cost losses (OCL). In order to demonstrate the practical use of the developed methodology, the robustness and the associated OCL of the Canadian Domestic Air Traffic Network are evaluated under random failures (i.e., disruptive events that occur randomly) and targeted threats (i.e., disruptive events that occur deliberately). The analysis results show that the network robustness is influenced by the utilized evaluation approach, especially after 20% of the network components become nonoperational. Overall, the methodology developed within this study is expected to provide ATIN policymakers with the means to quantify the network robustness and OCL, and thus enable ATIN resilience-guided proactive risk management in the face of natural or anthropogenic hazard realizations. / Thesis / Master of Applied Science (MASc)

Disruptions due to either natural or anthropogenic hazards significantly impact the operation of critical infrastructure networks because they may instigate network-level cascade (i.e., systemic) risks. Therefore, quantifying and enhancing the resilience of such complex dynamically evolving networks ensure minimizing the possibility and consequences of systemic risks. Focusing only on robustness, as one of the key resilience attributes, and on transportation networks, key critical infrastructure, the current study develops a hybrid complex network theoretic-genetic algorithms analysis approach. To demonstrate the developed approach, the robustness of a city transportation network is quantified by integrating complex network theoretic topology measures with a dynamic flow redistribution model. The network robustness is subsequently investigated under different operational measures and the corresponding absorptive capacity thresholds are quantified. Finally, the robustness of the network under different failure scenarios is evaluated using genetic algorithms coupled with k-means clustering to classify the different network components. The hybrid approach developed in the current study is expected to facilitate optimizing potential systemic risk mitigation strategies for critical infrastructure networks under disruptive events. / Thesis / Master of Applied Science (MASc)

Vom Standpunkt des Physikers aus gesehen, ist die Erde ein dynamisches System von großer Komplexität. Funktionale Netzwerke werden aus Beobachtungs-, und Modelldaten abgeleitet oder aufgrund theoretischer Überlegungen konstruiert. Indem sie statistische Zusammenhänge oder kausale Wirkbeziehungen zwischen der Dynamik gewisser Objekte, z.B. verschiedenen Sphären des Erdsystems, Prozessen oder lokalen Feldvariablen darstellen, bieten funktionale Netzwerke einen natürlichen Ansatz zur Bearbeitung fundamentaler Probleme der Erdsystemanalyse. Dazu gehören Fragen nach dominanten, dynamischen Mustern, Telekonnektionen und Rückkopplungsschleifen in der planetaren Maschinerie, sowie nach kritischen Elementen wie Schwellwerten, sogn. Flaschenhälsen und Schaltern im Erdsystem. Der erste Teil dieser Dissertation behandelt die Theorie komplexer Netzwerke und die netzwerkbasierte Zeitreihenanalyse. Die Beiträge zur Theorie komplexer Netzwerke beinhalten Maße und Modelle zur Analyse der Topologie (i) von Netzwerken wechselwirkender Netzwerke und (ii) Netzwerken mit ungleichen Knotengewichten, sowie (iii) eine analytische Theorie zur Beschreibung von räumlichen Netzwerken. Zur Zeitreihenanalyse werden (i) Rekurrenznetzwerke als eine theoretisch gut begründete, nichtlineare Methode zum Studium multivariater Zeitreihen vorgestellt. (ii) Gekoppelte Klimanetzwerke werden als ein exploratives Werkzeug der Datenanalyse zur quantitativen Charakterisierung der komplexen statistischen Interdependenzstruktur innerhalb und zwischen distinkten Feldern von Zeitreihen eingeführt. Im zweiten Teil der Arbeit werden Anwendungen zur Detektion von dynamischen Übergängen (Kipppunkten) in Zeitreihen, sowie zum Studium von Flaschenhälsen in der atmosphärischen Zirkulationsstruktur vorgestellt. Die Analyse von Paläoklimadaten deutet auf mögliche Zusammenhänge zwischen großskaligen Veränderungen der afrikanischen Klimadynamik während des Plio-Pleistozäns und Ereignissen in der Menschheitsevolution hin. / The Earth, as viewed from a physicist''s perspective, is a dynamical system of great complexity. Functional complex networks are inferred from observational data and model runs or constructed on the basis of theoretical considerations. Representing statistical interdependencies or causal interactions between objects (e.g., Earth system subdomains, processes, or local field variables), functional complex networks are conceptually well-suited for naturally addressing some of the fundamental questions of Earth system analysis concerning, among others, major dynamical patterns, teleconnections, and feedback loops in the planetary machinery, as well as critical elements such as thresholds, bottlenecks, and switches. The first part of this thesis concerns complex network theory and network-based time series analysis. Regarding complex network theory, the novel contributions include consistent frameworks for analyzing the topology of (i) general networks of interacting networks and (ii) networks with vertices of heterogeneously distributed weights, as well as (iii) an analytical theory for describing spatial networks. In the realm of time series analysis, (i) recurrence network analysis is put forward as a theoretically founded, nonlinear technique for the study of single, but possibly multivariate time series. (ii) Coupled climate networks are introduced as an exploratory tool of data analysis for quantitatively characterizing the intricate statistical interdependency structure within and between several fields of time series. The second part presents applications for detecting dynamical transitions (tipping points) in time series and studying bottlenecks in the atmosphere''s general circulation structure. The analysis of paleoclimate data reveals a possible influence of large-scale shifts in Plio-Pleistocene African climate variability on events in human evolution.

A prominent challenge in the healthcare system today is the limitation of resources in combi- nation with an increasing need for healthcare services. The pressure on healthcare is already extremely high and increasing due to a larger number of people seeking care as well as an aging population with an increased need for care. Therefore, it becomes more important to distribute resources effectively within healthcare to ensure high-quality care for everyone. Still, research shows that overcrowding of emergency departments and hospital wards is increasing affecting patient safety negatively with several negative implications including higher rates of medical errors and higher mortality. The problem is that healthcare is a complex system with many components that are interrelated and therefore hard to study with traditional approaches. Despite the huge quantity of studies on the overcrowding problem, there is yet to find a solution that could solve the problem. Thus, this thesis aims to design a data-driven pipeline to explore the clinical and logistical complexity of Emergency medicine patients admitted to hospital wards adopting a complex graph approach. Complex network theory provides a suitable tool to investigate complex networks by breaking complex systems down into smaller graphs with objects (nodes) and studying the relationship between these through various analysis tools. In this thesis, five complex networks were constructed representing co-morbidities in the car- diac, medicine, surgery, stroke, and orthopedic wards of the Academic Hospital of Uppsala, a hospital suffering from overcrowding. These networks were analyzed using degree distribution, centrality metrics, clustering coefficient, and community detection to reveal structural and clin- ical patterns. A comprehensive network of all hospital co-morbidities was also created and an- alyzed to compare it with the ward structures. Additionally, a network mapping patient flow from the emergency department based on chief complaints and ICD codes to wards was created and analyzed to identify admission patterns. The analysis of the co-morbidity networks revealed that there was an indication of structure between the wards. This was based on the visualization of nodes and edges of the networks, identified communities, and community comparisons between the wards. Further, it showed that there was a big overlap of common co-morbidities which could indicate the contrary. But it was also revealed that in terms of community structure, the wards were considerably different from each other indicating a good separation of diseases. The results of this research show that complex network theory could be used to increase the understanding of the complexity of healthcare wards in terms of the structure of diseases as well as clinical variability and allow for a discussion regarding if this is related to clinical or logistical factors. It also shows the potential of using complex network theory to increase the understanding of the path patients take from the emergency department to the wards based on the community detection analysis showing that there is a structure of where patient ends up based on the assigned ICD code and chief complaint in the emergency department. Previous studies have typically focused on specific diseases or patient flow within a single ward or the emergency department. This approach offers a tool to examine patient logistics across multiple wards alongside their clinical characteristics. The insights gained could help improve hospital structure by more efficiently distributing patients between wards, thereby enhancing resource use and hospital operations. Further research using complex network theory could deepen understanding of overcrowding issues and identify potential solutions. / En stor utmaning inom sjukvårdssystemet idag är begräsningen av resurser i kombination med ett ökat vårdbehov. Trycket på sjukvården är redan högt och ökar till följd av ett ökat antal personer som söker vård samt en åldrande befolkning med ett ökat vårdbehov. Därav blir det viktigare att fördela resurser inom sjukvården på ett effektivt sätt för att säkerställa en högkva- litativ vård till alla. Forskning visar dock att överbeläggningar på akutvårdsavdelningar och sjukvårdsavdelningar ökar vilket påverkar patientsäkerheten negativt med flera negativa kon- sekvenser däribland en högre andel medicinska misstag och en högre mortalitet. Problemet är att sjukvården är ett komplext system med många komponenter som samverkar och det är därav svårt att studera med traditionella tillvägagångssätt. Trots det höga antalet studier på överbeläggningar inom sjukvården behöver man fortfarande hitta en lösning på problemet. Därav är målet med denna avhandling att designa en datadriven pipeline för att undersöka den kliniska och logistiska komplexiteten hos patienter inlagda från akutvårdsavdelningen med hjälp av en komplex grafmetodik. Komplex nätverksteori är ett lämpligt verktyg för att studera komplexa nätverk genom att bryta ned det i mindre komponen- ter och undersöka sambanden mellan dem med hjälp av olika analysverktyg. I denna avhandling skapades 5 komplexa nätverk som representerade komorbiditeter utifrån tilldelad ICD-10-kod på hjärt-, medicin-, kirurgi-, stroke- och ortopediska avdelningen vid det akademiska sjukhuset i Uppsala, ett sjukhus som för närvarande lider av överbeläggningar. Nätverken analyserades med hjälp av gradfördelning, olika centralitetsmått, klusterkoefficient och samhällsdetektering för att identifiera skillnader eller likheter när det gäller struktur och klinisk variation. Ett heltäckande komplext nätverk skapades där alla komorbiditeter på hela sjukhuset inkluderades för att möjliggöra en jämförelse med strukturen på avdelningarna. Utö- ver detta, skapades och analyserades ett nätverk för att kartlägga patientflödet från akuten till sjukvårdsavdelningarna baserat på huvudorsak till patientens akutbesök och ICD kod. Analysen av samhällsstrukturen visade att det fanns en indikation av struktur mellan avdelning- arna. Detta baserat på visualisering av noder och kopplingar i nätverken, identifierade sam- hällen samt jämförelser av samhällen mellan avdelningarna. Vidare visade det dock att det fanns ett stort överlapp av vanliga komorbiditeter vilket kunde indikera motsatsen. Det visades dock att även när det gäller samhällsstruktur var avdelningarna väldigt olika vilket indikerade en god separering av sjukdomar. Resultaten av denna forskning visar att komplex nätverksteori kan användas för att öka förstå- elsen för komplexiteten på sjukvårdsavdelningarna gällande strukturen mellan sjukdomar såväl som klinisk variationen och öppnar upp för en diskussion om dessa är relaterade till kliniska eller logistiska faktorer. Det visar också potentialen att använda komplex nätverksteori för att öka förståelsen för den väg som patienterna tar från akutvårdsavdelningen till avdelningarna baserat på samhällsdetekteringsanalysen som visar att det finns en struktur av var patienten hamnar baserat på den tilldelade ICD-koden och huvudklagomål från akutvårdsavdelningen. Tidigare studier som har använt detta tillvägagångssätt har i huvudsak undersökt specifika sjuk- domar eller flöden på en specifik avdelning eller akutvårdsavdelning. Det här tillvägagångssät- tet ger ett verktyg för att utforska logistiken för patienters rutter till olika avdelningar samtidigt som deras kliniska egenskaper beaktas. Resultaten genom denna pipeline kan ge en grund för att öka förståelsen för hur man bättre kan strukturera sjukhuset genom att dela patienter mellanvavdelningar och genom detta effektivisera användningen av resurser och potentiellt förbättra rutiner på sjukhuset. Genom vidare studier, kan komplex nätverksteori användas för att öka förståelsen kring faktorer relaterade till problemet med överbeläggningar och hitta potentiella lösningar på problemet.