Inference in epidemiological agent-based models using ensemble-based data assimilation

To represent the complex individual interactions in the dynamics of disease spread informed by data, the coupling of an epidemiological agent-based model with the ensemble Kalman filter is proposed. The statistical inference of the propagation of a disease by means of ensemble-based data assimilation systems has been studied in previous work. The models used are mostly compartmental models representing the mean field evolution through ordinary differential equations. These techniques allow to monitor the propagation of the infections from data and to estimate several parameters of epidemiological interest. However, there are many important features which are based on the individual interactions that cannot be represented in the mean field equations, such as social network and bubbles, contact tracing, isolating individuals in risk, and social network-based distancing strategies. Agent-based models can describe contact networks at an individual level, including demographic attributes such as age, neighbourhood, household, workplaces, schools, entertainment places, among others. Nevertheless, these models have several unknown parameters which are thus difficult to estimate. In this work, we propose the use of ensemble-based data assimilation techniques to calibrate an agent-based model using daily epidemiological data. This raises the challenge of having to adapt the agent populations to incorporate the information provided by the coarse-grained data. To do this, two stochastic strategies to correct the model predictions are developed. The ensemble Kalman filter with perturbed observations is used for the joint estimation of the state and some key epidemiological parameters. We conduct experiments with an agent based-model designed for COVID-19 and assess the proposed methodology on synthetic data and on COVID-19 daily reports from Ciudad Aut\'onoma de Buenos Aires, Argentina

Fractional diffusion emulates a human mobility network during a simulated disease outbreak

From footpaths to flight routes, human mobility networks facilitate the spread of communicable diseases. Control and elimination efforts depend on characterizing these networks in terms of connections and flux rates of individuals between contact nodes. In some cases, transport can be parameterized with gravity-type models or approximated by a diffusive random walk. As a alternative, we have isolated intranational commercial air traffic as a case study for the utility of non-diffusive, heavy-tailed transport models. We implemented new stochastic simulations of a prototypical influenza-like infection, focusing on the dense, highly-connected United States air travel network. We show that mobility on this network can be described mainly by a power law, in agreement with previous studies. Remarkably, we find that the global evolution of an outbreak on this network is accurately reproduced by a two-parameter space-fractional diffusion equation, such that those parameters are determined by the air travel network.Comment: 26 pages, 4 figure

A risk analysis methodology for the use of crowd models during the Covid-19 pandemic

Pandemics such as Covid-19 have posed a set of questions concerning safe space usage given the risk of virus transmission in confined and open spaces. In this context, this report presents a risk analysis methodology for the use of crowd modelling tools as an aid to assess safety in confined and open spaces. Crowd models can be used to investigate people movement in the built environment, thus they have a great potential for the performance of proximity analysis. The report presented here addresses first the psychological and physical aspects linked to physical distancing (also called social distancing). Given the limited current knowledge on human behaviour and space usage during pandemics, the changes needed in crowd modelling tools to appropriately represent people movement are listed. This includes issues associated with modifications of the fundamental relationships between the key people movement variables (speed/flow vs density), and issues linked with interactions between pedestrians (e.g. collision avoidance, queuing mechanisms, route choice). Suggestions for new crowd modelling outputs are provided in order to enhance their use during pandemics. In addition, practical solutions concerning space usage are presented in light of the assessment of human safety through a risk evaluation based on proximity analysis and/or exposure assessment. This is deemed to help identifying design and management solutions to decrease the risk of virus transmission

Urban tourism crowding dynamics: Carrying capacity and digital twinning

The increase in tourism activity globally has led to overcrowding, causing damage to local ecosystems and degradation of the tourism experience. To plan tourist activity it is necessary to define adequate indicators and understand the dynamics of tourist crowds. The main goals of this dissertation are the development of (1) an algorithm for assessing spatially fine-grained, physical carrying capacity (PCC) for a complex urban fabric, (2) an agent-based simulation model for the egress of participants in public open space tourism attraction events and (3) an agent-based simulation model using the PCC algorithm for tourism crowding stress analysis in urban fabric constrained scenarios. OpenStreetMap open-data was used throughout this research. The proposed PCC algorithm was tested in Santa Maria Maior parish in Lisbon that has a complex ancient urban fabric. The GAMA agent-based platform was used in the two simulation studies. The first compared two scenarios (normal and COVID-19) in three major public spaces in Lisbon and the second focused on the simulation of a real-time tourism crowding stress analysis scenario of visitors’ arrival at the Lisbon Cruise Terminal. The results show the proposed algorithm’s feasibility to determine the PCC of complex urban fabrics zones and its application as an initial reference value for the evaluation of real-time crowding stress, namely in simulations for assessing overtourism scenarios, both in public open spaces as in highly constrained urban fabrics.O aumento da atividade turística a nível global tem levado à superlotação, causando danos aos ecossistemas locais e degradação da experiência turística. Para planear a atividade turística é necessário definir indicadores adequados e entender as dinâmicas das multidões turísticas. Os principais objetivos desta dissertação são o desenvolvimento de (1) um algoritmo para avaliar a capacidade de carga física (CCF) de fino grão espacial para uma malha urbana complexa, (2) um modelo de simulação baseado em agentes para o escoamento de participantes em eventos de atração turística em espaços abertos e (3) um modelo de simulação baseado em agentes usando o algoritmo de CCF para análise do stress de aglomeração de turistas em cenários de malha urbana restritiva. Os dados abertos do OpenStreetMap foram usados nesta investigação. O algoritmo CCF proposto foi testado na freguesia de Santa Maria Maior, em Lisboa, que tem uma malha urbana antiga e complexo. A plataforma GAMA baseada em agentes foi usada nos dois estudos de simulação. O primeiro comparou dois cenários (normal e COVID-19) em três grandes espaços públicos de Lisboa e o segundo analisou o stress de aglomeração causado pela chegada de navios ao Terminal de Cruzeiros de Lisboa. Os resultados mostram a viabilidade do algoritmo proposto para determinar a CCF de zonas com tecidos urbanos complexos e a sua aplicação como valor de referência inicial para a avaliação do stress de superlotação em tempo real, nomeadamente na avaliação de cenários de aglomeração turística excessiva, tanto em espaços abertos, como em malhas urbanas intrincadas

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

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Behavioral Human Crowds and Society

This chapter provides an introduction to the contents of this edited volume. In keeping with the style of the previous edited volumes, we also consider research perspectives. The first part of this chapter contributes to the selection of some key perspectives that take into account not only the technical interest of modeling and simulation, but also the impact that this research activity can have on the well-being of society. The second part provides a brief introduction to the contents of the chapters that follow this editorial introduction. The contents of the chapter refer both to the aforementioned key topics and to the contents of the preceding edited volumes (Bellomo and Gibelli, Behavioural human crowds, recent results towards new research frontiers. In: Bellomo, Gibelli (eds) Crowd dynamics, Volume 3 - Modeling and social applications in the time of COVID 19. Birkhäuser, New York, pp 1–9, 2021; Bellomo et al., Behavioural human crowds. In: Gibelli (ed) Crowd dynamics, Volume 2 - Theory, models, and applications. Birkhäuser, New York, pp 1–10, 2020; Gibelli and Bellomo, Behavioral human crowds. In: Crowd dynamics, Volume 1 - Theory, models, and safety problems. Birkhäuser, New York, pp 1–14, 2018).</p

Progress Report 1: Resilience and Adaptation to Climatic Extreme Wildfires (RACE Wildfires)

This is the first progress report of the international project funded by the National Research Council of Canada called Resilience and Adaptation to Climatic Extreme Wildfires (RACE Wildfires). In this first phase, the research performed included two main tasks: 1) the development of a sub-model for the representation of the impact of reduced visibility conditions on driving speed and 2) the development of a conceptual model for the study of the impact of the pandemic on shelter availability and destination choice. An experimental dataset collected in a virtual reality environment has been used to develop a sub-model for macroscopic traffic models considering the impact of reduced visibility conditions on driving speed. An application of a calibrated traffic model considering the impact of smoke has been performed using the WUI-NITY platform, an open multi-physics platform which includes wildfire spread, pedestrian response and traffic modelling. Verification testing has been performed as well. A conceptual framework for the development of a destination choice model to be applied in wildfire scenarios has also been developed