An Initial Framework Assessing the Safety of Complex Systems

Trabajo presentado en la Conference on Complex Systems, celebrada online del 7 al 11 de diciembre de 2020.Atmospheric blocking events, that is large-scale nearly stationary atmospheric pressure patterns, are often associated with extreme weather in the mid-latitudes, such as heat waves and cold spells which have significant consequences on ecosystems, human health and economy. The high impact of blocking events has motivated numerous studies. However, there is not yet a comprehensive theory explaining their onset, maintenance and decay and their numerical prediction remains a challenge. In recent years, a number of studies have successfully employed complex network descriptions of fluid transport to characterize dynamical patterns in geophysical flows. The aim of the current work is to investigate the potential of so called Lagrangian flow networks for the detection and perhaps forecasting of atmospheric blocking events. The network is constructed by associating nodes to regions of the atmosphere and establishing links based on the flux of material between these nodes during a given time interval. One can then use effective tools and metrics developed in the context of graph theory to explore the atmospheric flow properties. In particular, Ser-Giacomi et al. [1] showed how optimal paths in a Lagrangian flow network highlight distinctive circulation patterns associated with atmospheric blocking events. We extend these results by studying the behavior of selected network measures (such as degree, entropy and harmonic closeness centrality)at the onset of and during blocking situations, demonstrating their ability to trace the spatio-temporal characteristics of these events.This research was conducted as part of the CAFE (Climate Advanced Forecasting of sub-seasonal Extremes) Innovative Training Network which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 813844

Epidemic spreading : the role of host mobility and transportation networks

In recent years, the increasing availability of computer power has enabled both to gather an unprecedented amount of data depicting the global interconnections of the modern society and to envision computational tools able to tackle the analysis and the modeling of dynamical pro- cesses unfolding on such a complex reality. In this perspective, the quantitative approach of Physics is catalyzing the growth of new interdisciplinary fields aimed at the understanding of complex techno-socio-ecological systems. By recognizing the crucial role of host mobility in the dissemination of infectious diseases and by leveraging on a network science approach to handle the large scale datasets describing the global interconnectivity, in this thesis we present a theo- retical and computational framework to simulate epidemics of emerging infectious diseases in real settings. In particular we will tackle two different public health related issues. First, we present a Global Epidemic and Mobility model (GLEaM) that is designed to simulate the spreading of an influenza-like illness at the global scale integrating real world-wide mobility data. The 2009 H1N1 pandemic demonstrated the need of mathematical models to provide epidemic forecasts and to assess the effectiveness of different intervention policies. In this perspective we present the results achieved in real time during the unfolding of the epidemic and a posteriori analysis on travel related mitigation strategies and model validation. The second problem that we address is related to the epidemic spreading on evolving networked systems. In particular we analyze a detailed dataset of livestock movements in order to characterize the temporal correlations and the statistical properties governing the system. We then study an infectious disease spreading, in order to characterize the vulnerability of the system and to design novel control strategies. This work is an interdisciplinary approach that merges statistical physics techniques, complex and multiscale system analysis in the context of hosts mobility and computational epidemiology.Ces dernières années, la puissance croissante des ordinateurs a permis a` la fois de rassembler une quantité sans précédent de données décrivant la société moderne et d’envisager des outils numériques capables de s’attaquer a` l’analyse et la modélisation les processus dynamiques qui se déroulent dans cette réalité complexe. Dans cette perspective, l’approche quantitative de la physique est un des catalyseurs de la croissance de nouveaux domaines interdisciplinaires visant a` la compréhension des systèmes complexes techno-sociaux. Dans cette thèse, nous présentons dans cette thèse un cadre théorique et numérique pour simuler des épidémies de maladies infectieuses émergentes dans des contextes réalistes. Dans ce but, nous utilisons le rôle crucial de la mobilité des agents dans la diffusion des maladies infectieuses et nous nous appuyons sur l’ étude des réseaux complexes pour gérer les ensembles de données à grande échelle décrivant les interconnexions de la population mondiale. En particulier, nous abordons deux différents probl`emes de sant ́e publique. Tout d’abord, nous consid ́erons la propagation d’une ́epid ́emie au niveau mondial, et pr ́esentons un mod`ele de mobilit ́e (GLEAM) conc ̧u pour simuler la propagation d’une maladie de type grippal a` l’ ́echelle globale, en int ́egrant des donn ́ees r ́eelles de mobilit ́e dans le monde entier. La derni`ere pand ́emie de grippe H1N1 2009 a d ́emontr ́e la n ́ecessit ́e de mod`eles math ́ematiques pour fournir des pr ́evisions ́epid ́emiques et ́evaluer l’efficacit ́e des politiques d’interventions. Dans cette perspective, nous pr ́esentons les r ́esultats obtenus en temps r ́eel pendant le d ́eroulement de l’ ́epid ́emie, ainsi qu’une analyse a posteriori portant sur les strat ́egies de lutte et sur la validation du mod`ele. Le deuxi`eme probl`eme que nous abordons est li ́e a` la propagation de l’ ́epid ́emie sur des syst`emes en r ́eseau d ́ependant du temps. En particulier, nous analysons des donn ́ees d ́ecrivant les mouvements du b ́etail en Italie afin de caract ́eriser les corr ́elations temporelles et les propri ́et ́es statistiques qui r ́egissent ce syst`eme. Nous étudions ensuite la propagation d’une maladie infectieuse, en vue de caractériser la vulnérabilité du syst`eme et de concevoir des strat ́egies de controˆle. Ce travail est une approche interdisciplinaire qui combine les techniques de la physique statistique et de l’analyse des syst`emes complexes dans le contexte de la mobilit ́e des agents et de l’ ́epid ́emiologie num ́erique

Large-scale vector-borne disease agent-based model, with application to Chikungunya in Colombia

This document presents the development of a large-scale agent-based model to represent vector-borne disease transmission dynamics. Specifically the model represents the transmission of chikungunya in Colombia. Due to their similarities, the model can also be applied to simulate dengue epidemics. The aim of this model is to contribute to the knowledge of chikungunya, to reproduce realistic epidemics, and to quantify the impact of vector control programs to halt the spread of the disease. Chikungunya is a disease transmitted by the \textit{aedes} mosquitoes, particularly \aegypti and \textit{Aedes albopictus}. Chikungunya symptoms are similar to dengue, but it is characterized by acute join-pain that can last for years. In Colombia, the \aegypti is found in larger proportions than the \textit{Aedes albopictus}. The \aegypti mainly obtains its food from humans, hence it is often considered a residential mosquito. The model proposed in this thesis represents humans and mosquitoes. Humans are represented by agents whose health status can be classified in a S-E-I-R structure (Susceptible, Exposed, Infectious, Recovered). Whereas mosquitoes are represented by a homogeneous meta population model with the S-E-I compartments. In the model, the virus transmission occurs in specific locations such as households, workplaces, or schools. In each location, the number of mosquitoes are computed based on temperature and the human density. Mosquitoes and humans can transmit the infection to each other with specific probabilities determined in the model. Transmission occurs when an infectious agent visits a place with susceptible vectors, or when a susceptible agent visits a place with infectious mosquitoes. These visits are determined by each agent's activities that are assigned in a synthetic population, these activities include: household visits, school attendance, work attendance, and travel. A synthetic population was developed to represent a realistic population of Colombia. The synthetic population represents the population of the 1122 municipalities and 33 departments of the country. Additionally, the synthetic population reproduces daily activities for each individual based on the census data. Human mobility was also represented in the model implementing a calibrated gravity model to represent air travel. The model's parameters were calibrated to represent chikungunya dynamics reported in the Riohacha, Guajira. Some of the parameter values were obtained from the literature while others were adjusted using an optimization algorithm. This calibrated model was used to estimate the impact of vector control strategies in the city of Santa Marta, Magdalena. The control parameters in the model were modified to determine improvements to design optimal vector control strategies. Lastly, the model was simulated in a national-scale to evaluate the burden of the chikungunya with and without vector control strategies.Resumen. En esta tesis se presenta el desarrollo de un modelo basado en agentes para representar la dinámica de enfermedades transmitidas por vectores. En específico, en Colombia para chikungunya, con posibles aplicaciones para dengue. Con el modelo se busca estimar la carga de casos de chikungunya en el país y evaluar el impacto de control vectorial para controlar su expansión en el país. El chikungunya es una enfermedad transmisible por mosquitos del tipo aedes, en especial aedes aegypti y albopictus. Su sintomatología es similar al dengue, con una diferencia de dolor en las articulaciones que puede prolongarse por años, dependiendo del paciente. En Colombia, el mosquito aegypti se encuentra en mayor proporción que el albopictus. Este es considerado un mosquito residencial, debido a que se alimenta principalmente de humanos, consecuentemente, se encuentra en su mayoria en cercanías a los hogares. El modelo representa estados de salud en humanos, basados en un modelo SEIR (Susceptible-Expuesto-Infectado-Recuperado). Mientras que para mosquitos se basa en la estructura SEI. La transmisión del virus en el modelo ocurre en lugares específicos, tales como, hogares, lugares de trabajo o colegios. Dentro de estos establecimientos, se encuentra un número determinado de mosquitos que depende de la cantidad de humanos y de la temperatura promedio anual. Estos mosquitos pueden infectarse con el virus con una probabilidad de infección determinada en el modelo, para luego transmitirlo a los humanos susceptibles que visitan ese lugar. Por su parte, cada humano tiene asignadas una cantidad de actividades dependiendo de su situación (estudiante, trabajado, ama de casa, etc), estas actividades se realizan con prioridad, mientras que hay una lista extra de actividades opcionales como viajar, visitar a un vecino, etc. De esta manera, los agentes pueden transmitir o infectarse con el virus. Con el objetivo de representar una población cercana a la realidad, hubo la necesidad de desarrollar una población sintética que represente estadísticamente la población de Colombia. Además, que represente las actividades principales de cada agente, e.g. estudiar, trabajar, etc. La población sintética representa los 1122 municipios del país. Además, el modelo requiere de grillas de temperatura que fueron obtenidos de bases de datos de libre acceso. Finalmente, el modelo incluye una estimación de los viajes interdepartamentales, basado en datos de flujo entre aeropuertos del país. El modelo fue sintonizado utilizando reportes de casos de chikungunya del 2014-2015. Usando un municipio como muestra y estimando el desempeño del modelo con municipios no sintonizados. Este modelo sintonizado fue utilizado para evaluar el impacto de las campañas de control en el municipio de Santa Marta (Magdalena), donde se registro un caso exitoso de prevención de la enfermedad utilizando control vectorial. Finalmente, el efecto del control vectorial fue estimado, simulando una epidemia en todo el país con diferentes estrategias vectoriales.Doctorad

Life in the Time of a Pandemic

It has been confirmed that the number of cases and the death toll of COVID-19 are continuing to rise in many countries around the globe. Governments around the world have been struggling with containing and reducing the socioeconomic impacts of COVID-19; however, their respective responses have not been consistent. Aggressive measures imposed by some governments have resulted in a complete lockdown that has disrupted all facets of life and poses massive health, social, and financial impacts. Other countries, however, are taking a more wait-and-see approach in an attempt to maintain business as usual. Collectively, these challenges reflect a super wicked problem that places immense pressure on economies and societies and requires the strategic management of health systems to avoid overwhelming them—this has been linked to the public mantra of ‘flattening the curve’, which acknowledges that while the pandemic cannot be stopped, its impact can be regulated so that the number of cases at any given time is not beyond the capacity of the health system. Dynamic simulation modelling is a framework that facilitates the understanding/exploring of complex problems, of searching for and finding the best option(s) from all practical solutions where time dynamics are essential. The papers in this book provide research insights into this super wicked problem and case studies exploring the interactions between social, economic, environmental, and health factors through the use of a systems approach

Simulation modeling of zoonotic diseases between swine and human populations for informing policy decisions

Approximately 60% of human pathogens and emerging infectious diseases are zoonotic. Simulation models are increasingly being used to investigate the spread of diseases, evaluate intervention strategies and guide the decisions of policy makers. In this thesis a systematic review of modeling methods and approaches used for zoonotic influenza in animals and humans was conducted, and knowledge gaps were identified. Furthermore, the disease spread and intervention parameters used in these studies were summarized for ready reference in future work. Building on this review work, the research presented in this thesis evaluated the effects of transmissibility of the pandemic H1N1 2009 (pH1N1) virus at the swine- human interface and the control strategies against its spread in swine and human populations as a case study for zoonotic disease modeling. The feasibility of North American Animal Disease Spread Model (NAADSM) for modeling directly transmitted zoonoses was also assessed. Population data based on swine herds and households (categorized as rural households with or without swine workers, and urban households without swine workers) of a county in Ontario, Canada was used. The swine workers served as a bridging population for the spread of the virus between swine herds and households. Scenarios based on the combinations of the transmissibility of the virus (low (L), medium (M), and high (H)) from swine-to-human and human-to-swine (LL, ML, HL, MM, HM, LL), and targeted vaccination of swine worker households (0% to 60%) were evaluated. The results showed that lowering the influenza transmissibility at the interface to low level and providing higher vaccine coverage (60%) had significant beneficial effects on all outcome measures. However, these measures had little or negligible impact on the total number of rural and urban households infected. A set of models evaluating the combination of control strategies indicated that a moderate speed of the detection (within 5 to 10 days of the first infection), combined with the quarantine of detected units alone, contained the outbreak within the swine population in most simulations. However, a zone-based quarantine strategy was more effective when the detection was delayed until around three weeks after initial infection. Ring vaccination had no added beneficial effect. This work suggested that NAADSM can be used for modeling the directly transmitted zoonotic diseases under similar simplifying assumptions adopted in these studies. However, this needs to be evaluated further with more accurate parameters and influenza outbreak data. To fill in some of the gaps identified in the review study, network analyses of swine shipments among farms, and between farms and abattoirs were conducted. This provided network metrics and parameters necessary for disease modeling and risk-based disease management in swine in Ontario for the first time. Finally, agent-based network models assessing the spread and control of pH1N1 in swine established the importance of explicitly incorporating appropriate contact network structures into such models to increase their validity. It also demonstrated the benefits of targeted control strategies against highly connected farms. In conclusion, the modeling tools developed in this thesis can assist decision makers in preparedness and response of outbreaks of infectious diseases as more information become available for the parameterization of models

The use and reporting of airline passenger data for infectious disease modelling:a systematic review

Background A variety of airline passenger data sources are used for modelling the international spread of infectious diseases. Questions exist regarding the suitability and validity of these sources. Aim We conducted a systematic review to identify the sources of airline passenger data used for these purposes and to assess validation of the data and reproducibility of the methodology. Methods Articles matching our search criteria and describing a model of the international spread of human infectious disease, parameterised with airline passenger data, were identified. Information regarding type and source of airline passenger data used was collated and the studies’ reproducibility assessed. Results We identified 136 articles. The majority (n = 96) sourced data primarily used by the airline industry. Governmental data sources were used in 30 studies and data published by individual airports in four studies. Validation of passenger data was conducted in only seven studies. No study was found to be fully reproducible, although eight were partially reproducible. Limitations By limiting the articles to international spread, articles focussed on within-country transmission even if they used relevant data sources were excluded. Authors were not contacted to clarify their methods. Searches were limited to articles in PubMed, Web of Science and Scopus. Conclusion We recommend greater efforts to assess validity and biases of airline passenger data used for modelling studies, particularly when model outputs are to inform national and international public health policies. We also recommend improving reporting standards and more detailed studies on biases in commercial and open-access data to assess their reproducibility