Metapopulation Graph Neural Networks: Deep Metapopulation Epidemic Modeling with Human Mobility

Epidemic prediction is a fundamental task for epidemic control and prevention. Many mechanistic models and deep learning models are built for this task. However, most mechanistic models have difficulty estimating the time/region-varying epidemiological parameters, while most deep learning models lack the guidance of epidemiological domain knowledge and interpretability of prediction results. In this study, we propose a novel hybrid model called MepoGNN for multi-step multi-region epidemic forecasting by incorporating Graph Neural Networks (GNNs) and graph learning mechanisms into Metapopulation SIR model. Our model can not only predict the number of confirmed cases but also explicitly learn the epidemiological parameters and the underlying epidemic propagation graph from heterogeneous data in an end-to-end manner. The multi-source epidemic-related data and mobility data of Japan are collected and processed to form the dataset for experiments. The experimental results demonstrate our model outperforms the existing mechanistic models and deep learning models by a large margin. Furthermore, the analysis on the learned parameters illustrate the high reliability and interpretability of our model and helps better understanding of epidemic spread. In addition, a mobility generation method is presented to address the issue of unavailable mobility data, and the experimental results demonstrate effectiveness of the generated mobility data as an input to our model.Comment: This is the extended version of an ECMLPKDD2022 pape

Physics of interdependent dynamical processes.

La emergencia de fenómenos colectivos a escalas macroscópicas no observados en escalas microscópicas cuestiona la validez de las teorías reduccionistas. Para explicar estos fenómenos se necesitan enfoques sistémicos que den cuenta de los patrones de interacción no triviales existentes entre los constituyentes de los sistemas sociales, biológicos o económicos, lo que ha dado lugar al nacimiento de la disciplina conocida como ciencia de los sistemas complejos. Una vía habitual para caracterizar los sistemas complejos ha sido la búsqueda de la conexión entre la estructura de interacciones y el comportamiento colectivo observado en sistemas reales mediante el estudio individual de dinámicas aisladas. No obstante, los sistemas complejos no son inmutables y se encuentran constantemente intercambiando información mediante estímulos internos y externos. Esta tesis se centra en la adaptación de modelos sobre diferentes dinámicas en el campo de los sistemas complejos para caracterizar el impacto de este flujo de información, ya sea entre escalas microscópicas y macroscópicas de un mismo sistema o mediante la existencia de interdependencias entre procesos dinámicos que se propagan de forma simultánea.La primera parte de la tesis aborda el estudio dinámicas acopladas en redes de contacto estáticas. Adaptando los modelos compartimentales introducidos en el siglo XX a la naturaleza de cada dinámica, caracterizamos cuatro problemas diferentes: la propagación de patógenos que interactúan, cuya coexistencia puede ser beneficiosa o perjudicial para su evolución, el control de brotes epidémicos con el uso del rastreo de contactos digital, la aparición de movimientos sociales desencadenados por pequeñas minorías sociales bien coordinadas y la competencia entre honestidad y la corrupción en las sociedades modernas. En todas estas dinámicas, encontramos que el flujo de información cambia las propiedades críticas del sistema así como algunas de las conclusiones extraídas sobre el papel de la estructura de contactos al estudiar cada dinámica de forma individual.La segunda parte de la tesis se centra en el impacto de la movilidad recurrente en la propagación de epidemias en entornos urbanos. Derivamos un modelo sencillo que permite incorporar fácilmente la distribución de la población en las ciudades reales y sus patrones habituales de desplazamiento sin ninguna pérdida de información. Demostramos que los efectos de las políticas de contención basadas en la reducción de la movilidad no son universales y dependen en gran medida de las características estructurales de las ciudades y los parámetros epidemiológicos del virus circulante en la población. En particular, descubrimos y caracterizamos un nuevo fenómeno, el detrimento epidémico, que refleja el efecto beneficioso de la movilidad en algunos escenarios para contener un brote epidémico. Por último, exploramos tres casos de estudio reales, mostrando que nuestro modelo permite capturar algunos de los mecanismos que han convertido a los núcleos urbanos en importantes focos de contagio en recientes epidemias y que el modelo desarrollado puede servir como base para desarrollar marcos teóricos más realistas que reproducen la evolución de distintas enfermedades como la COVID-19 o el dengue.<br /

A systematic review of the data, methods and environmental covariates used to map Aedes-borne arbovirus transmission risk

BACKGROUND: Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used. METHODS: We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.). RESULTS: We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002-2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures. CONCLUSIONS: Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping

Saving Human Lives: What Complexity Science and Information Systems can Contribute

We discuss models and data of crowd disasters, crime, terrorism, war and disease spreading to show that conventional recipes, such as deterrence strategies, are often not effective and sufficient to contain them. Many common approaches do not provide a good picture of the actual system behavior, because they neglect feedback loops, instabilities and cascade effects. The complex and often counter-intuitive behavior of social systems and their macro-level collective dynamics can be better understood by means of complexity science. We highlight that a suitable system design and management can help to stop undesirable cascade effects and to enable favorable kinds of self-organization in the system. In such a way, complexity science can help to save human lives.Comment: 67 pages, 25 figures; accepted for publication in Journal of Statistical Physics [for related work see http://www.futurict.eu/

The Effects of Spatio-Temporal Heterogeneities on the Emergence and Spread of Dengue Virus

The dengue virus (DENV) remains a considerable global public health concern. The interactions between the virus, its mosquito vectors and the human host are complex and only partially understood. Dependencies of vector ecology on environmental attributes, such as temperature and rainfall, together with host population density, introduce strong spatiotemporal heterogeneities, resulting in irregular epidemic outbreaks and asynchronous oscillations in serotype prevalence. Human movements across different spatial scales have also been implicated as important drivers of dengue epidemiology across space and time, and further create the conditions for the geographic expansion of dengue into new habitats. Previously proposed transmission models often relied on strong, unrealistic assumptions regarding key epidemiological and ecological interactions to elucidate the effects of these spatio-temporal heterogeneities on the emergence, spread and persistence of dengue. Furthermore, the computational limitations of individual based models have hindered the development of more detailed descriptions of the influence of vector ecology, environment and human mobility on dengue epidemiology. In order to address these shortcomings, the main aim of this thesis was to rigorously quantify the effects of ecological drivers on dengue epidemiology within a robust and computational efficient framework. The individual based model presented included an explicit spatial structure, vector and human movement, spatio-temporal heterogeneity in population densities, and climate effects. The flexibility of the framework allowed robust assessment of the implications of classical modelling assumptions on the basic reproduction number, R₀, demonstrating that traditional approaches grossly inflate R₀ estimates. The model's more realistic meta-population formulation was then exploited to elucidate the effects of ecological heterogeneities on dengue incidence which showed that sufficient levels of community connectivity are required for the spread and persistence of dengue virus. By fitting the individual based model to empirical data, the influence of climate and on dengue was quantified, revealing the strong benefits that cross-sectional serological data could bring to more precisely inferring ecological drivers of arboviral epidemiology. Overall, the findings presented here demonstrate the wide epidemiological landscape which ecological drivers induce, forewarning against the strong implications of generalising interpretations from one particular setting across wider spatial contexts. These findings will prove invaluable for the assessment of vector-borne control strategies, such as mosquito elimination or vaccination deployment programs

Statistical Inference for Propagation Processes on Complex Networks

Die Methoden der Netzwerktheorie erfreuen sich wachsender Beliebtheit, da sie die Darstellung von komplexen Systemen durch Netzwerke erlauben. Diese werden nur mit einer Menge von Knoten erfasst, die durch Kanten verbunden werden. Derzeit verfügbare Methoden beschränken sich hauptsächlich auf die deskriptive Analyse der Netzwerkstruktur. In der hier vorliegenden Arbeit werden verschiedene Ansätze für die Inferenz über Prozessen in komplexen Netzwerken vorgestellt. Diese Prozesse beeinflussen messbare Größen in Netzwerkknoten und werden durch eine Menge von Zufallszahlen beschrieben. Alle vorgestellten Methoden sind durch praktische Anwendungen motiviert, wie die Übertragung von Lebensmittelinfektionen, die Verbreitung von Zugverspätungen, oder auch die Regulierung von genetischen Effekten. Zunächst wird ein allgemeines dynamisches Metapopulationsmodell für die Verbreitung von Lebensmittelinfektionen vorgestellt, welches die lokalen Infektionsdynamiken mit den netzwerkbasierten Transportwegen von kontaminierten Lebensmitteln zusammenführt. Dieses Modell ermöglicht die effiziente Simulationen verschiedener realistischer Lebensmittelinfektionsepidemien. Zweitens wird ein explorativer Ansatz zur Ursprungsbestimmung von Verbreitungsprozessen entwickelt. Auf Grundlage einer netzwerkbasierten Redefinition der geodätischen Distanz können komplexe Verbreitungsmuster in ein systematisches, kreisrundes Ausbreitungsschema projiziert werden. Dies gilt genau dann, wenn der Ursprungsnetzwerkknoten als Bezugspunkt gewählt wird. Die Methode wird erfolgreich auf den EHEC/HUS Epidemie 2011 in Deutschland angewandt. Die Ergebnisse legen nahe, dass die Methode die aufwändigen Standarduntersuchungen bei Lebensmittelinfektionsepidemien sinnvoll ergänzen kann. Zudem kann dieser explorative Ansatz zur Identifikation von Ursprungsverspätungen in Transportnetzwerken angewandt werden. Die Ergebnisse von umfangreichen Simulationsstudien mit verschiedenstensten Übertragungsmechanismen lassen auf eine allgemeine Anwendbarkeit des Ansatzes bei der Ursprungsbestimmung von Verbreitungsprozessen in vielfältigen Bereichen hoffen. Schließlich wird gezeigt, dass kernelbasierte Methoden eine Alternative für die statistische Analyse von Prozessen in Netzwerken darstellen können. Es wurde ein netzwerkbasierter Kern für den logistischen Kernel Machine Test entwickelt, welcher die nahtlose Integration von biologischem Wissen in die Analyse von Daten aus genomweiten Assoziationsstudien erlaubt. Die Methode wird erfolgreich bei der Analyse genetischer Ursachen für rheumatische Arthritis und Lungenkrebs getestet. Zusammenfassend machen die Ergebnisse der vorgestellten Methoden deutlich, dass die Netzwerk-theoretische Analyse von Verbreitungsprozessen einen wesentlichen Beitrag zur Beantwortung verschiedenster Fragestellungen in unterschiedlichen Anwendungen liefern kann