Human mobility is a key element in the understanding of epidemic spreading. Thus, correctly modeling and quantifying human mobility is critical for studying large-scale spatial transmission of infectious diseases and improving epidemic control. In this study, a large-scale agent-based transport simulation (MATSim) is linked with a generic epidemic spread model to simulate the spread of communicable diseases in an urban environment. The use of an agent-based model allows reproduction of the real-world behavior of individuals’ daily path in an urban setting and allows the capture of interactions among them, in the form of a spatial-temporal social network. This model is used to study seasonal influenza outbreaks in the metropolitan area of Zurich, Switzerland. The observations of the agent-based models are compared with results from classical SIR models. The model presented is a prototype that can be used to analyze multiple scenarios in the case of a disease spread at an urban scale, considering variations of different model parameters settings. The results of this simulation can help to improve comprehension of the disease spread dynamics and to take better steps towards the prevention and control of an epidemic.

Disruptive events, such as earthquakes, floods, and landslides, may disrupt the service provided by transportation networks on a vast scale, as their occurrence is likely to cause multiple objects to fail simultaneously. The restoration program following a disruptive event should restore service as much, and as fast, as possible. The estimation of risk due to natural hazards must take into consideration the resilience of the network, which requires estimating the restoration program as accurately as possible. In this article, a restoration model using simulated annealing is formulated to determine near-optimal restoration programs following the occurrence of hazard events. The objective function of the model is to minimize the costs, taking into consideration the direct costs of executing the physical interventions, and the indirect costs that are being incurred due to the inadequate service being provided by the network. The constraints of the model are annual and total budget constraints, annual and total resource constraints, and the specification of the number and type of interventions to be executed within a given time period. The restoration model is demonstrated by using it to determine the near-optimal restoration program for an example road network in Switzerland following the occurrence of an extreme flood event. The strengths and weaknesses of the restoration model are discussed, and an outlook for future work is given.

Networks such as transportation, water, and power are critical lifelines for society. Managers plan and execute interventions to guarantee the operational state of their networks under various circumstances, including after the occurrence of (natural) hazard events. Creating an intervention program demands knowing the probable direct and indirect consequences (i.e., risk) of the various hazard events that could occur in order to be able to mitigate their effects. This paper introduces a methodology to support network managers in the quantification of the risk related to their networks. The methodology is centered on the integration of the spatial and temporal attributes of the events that need to be modeled to estimate the risk. Furthermore, the methodology supports the inclusion of the uncertainty of these events and the propagation of these uncertainties throughout the risk modeling. The methodology is implemented through a modular simulation engine that supports the updating and swapping of models according to the needs of network managers. This work demonstrates the usefulness of the methodology and simulation engine through an application to estimate the potential impact of floods and mudflows on a road network located in Switzerland. The application includes the modeling of (i) multiple time-varying hazard events; (ii) their physical and functional effects on network objects (i.e., bridges and road sections); (iii) the functional interrelationships of the affected objects; (iv) the resulting probable consequences in terms of expected costs of restoration, cost of traffic changes, and duration of network disruption; and (v) the restoration of the network.

How and when transportation networks are restored following a natural hazard event plays a key role in post-event recovery. However, determining the optimal repair strategies considering user costs requires intensive computational effort impeding the application in practice. This paper uses a modified network robustness index (MNRI), a measure of resilience, to minimize total costs including the repair cost and the travel cost during the recovery. For each link, the repair program is selected from multiple options using incremental benefit cost analysis. The efficiency and effectiveness of the method is tested for a realistic damaged network in Chur, Switzerland. Three scenarios with different resources in terms of repair budgets and crew availability are investigated. The results demonstrate that the approximations obtained using the proposed method are close to the results using a near optimal heuristic algorithm, and reduce the computational effort and the time needed.

Bridges, as all objects in road networks, are built to provide a specified level of service over a specified time period. This level of service ensures that acceptable levels of health, safety, and prosperity of society are guaranteed. The level of service, required from the transportation infrastructure, changes over time, as does the ability of infrastructure to provide it. The extent of maintenance is influenced by gradual deterioration, such as that caused by chloride-induced corrosion of concrete, and sudden deterioration such as that caused by the occurrence of scour resulting from extreme floods. To ensure that infrastructure provides the required service levels, its performance needs to be monitored. Determining how monitoring is to be done is a trade-off between accuracy and cost. Ideally, one will have access to accurate but inexpensive monitoring techniques. This paper contains the results of an investigation into the use of an unmanned aerial vehicle and modern photogrammetric technology to obtain topographical information to apply in bridge risk assessment. The unmanned aerial vehicle was used to take georeferenced images. With the images and photogrammetric technology, a three-dimensional (3D) mesh of the terrain was generated. This mesh was then converted to a computational mesh, which could be used to run computational fluid dynamic simulations during a bridge risk assessment. The investigated bridge was a single span concrete bridge in the Canton of Grisons, Switzerland. The hydraulic events, predicted by the developed model, correspond with historical observations, indicating that the topographical information collected is sufficiently accurate to be used to simulate complex flow situations, which can be used in bridge risk assessments.