Presentation : The scales of Human Mobility
A highly influential stream of literature driven by analyses of massive empirical datasets found that human movements show no evidence of characteristic spatial scales. There, human mobility is described as scale-free. However, in geography, the concept of scale, referring to meaningful levels of description from individual buildings through neighbourhoods, cities, regions, and countries, is central. In this talk, I will present how we resolved this apparent paradox by showing that human mobility does indeed contain meaningful scales, corresponding to spatial containers restricting mobility behaviour. The scale-free results arise from aggregating displacements across containers. Finally, I will present some of the implications for epidemic modelling. |
Federico Baldo is a Ph.D. candidate in Computer Science and Engineering at the University of Bologna. His research focuses on Neuro-symbolic Machine Learning models, with applications in the Health sector and High-Performance Computing. He actively contributed to the DETECOVID project, funded by the Italian Ministry of Health and in collaboration with the national institute of infectious disease "Lazzaro Spallanzani." Federico also participated in the XPrize Pandemic Response Challenge, where he contributed in the development of a physics-informed deep learning method for epidemics forecasting. During his visiting period at IPLESP, supervised by Dr. Eugenio Valdano, he specialized in transfer learning models for epidemics.
Presentation : Transfer deep renewal equations for epidemic forecasting |
Presentation : Epidemic inference in contact networks through variational generative machine learning methods
The reconstruction of missing information in partially observed epidemic processes on large and structured contact networks is a challenging problem of modern computational epidemiology. I will briefly review recent advances in the development of algorithms for source detection and individual risk assessment on networks, then introduce new scalable machine learning methods that overcome some limitations of state-of-the-art techniques. These methods are based on variational approximations of the posterior distribution that encode information about the causal constraints induced by epidemic propagation. Notably, the obtained probabilistic models can be also used to generate epidemic realisations compatible with observations. |
Raluca Eftimie has obtained a PhD in Applied Mathematics (2008) from the University of Alberta, Canada. Between 2008-2011 she was a Postdoctoral Fellow at McMaster University working on mathematical models for cancer immunotherapies and virotherapies. Between 2011-2020 Raluca Eftimie was employed at the University of Dundee, UK, moving through all UK academic ranks (from Lecturer, to Senior Lecturer, Reader and finally Personal Chair), working on applications of mathematics to cell biology, medicine and ecology, from the very applied side to the very theoretical side. Since December 2020 she is Professor at the University of Franche-Comté, France.
Presentation : Single-scale and multi-scale models in epidemiology: applications to COVID-19 infections The spread of infectious diseases is a multi-scale process that occurs not only between susceptible and infected individuals (at the level of human/animal populations), but also between susceptible and infected cells at the level of a single individual. This multi-scale process has become extremely clear during the COVID-19 pandemics, which was caused by infections with various strains of the SARS-CoV-2 virus. In this presentation I will briefly overview some single-scale and multi-scale mathematical models developed in my research group over the last 3 years, investigating different aspects related to the spread of SARS-CoV-2 at the within-host level as well as the between-host level. |
Raphaël is a researcher at the French National Research Institute for Agriculture, Food and Environmnent (INRAE). He completed a PhD in mathematics at the centre of applied mathematics of the Ecole Polytechnique in 2017, and has since been working as an INRAE researcher at the BioSP research unit in Avignon. Raphaël works on spatial stochastic models in population genetics, evolutionary biology and spatial epidemic models. He is mainly interested in how populations and epidemics spread through heterogeneous physical and phenotypic spaces as a result of dispersal and/or adaptation and the traces left by these events in the genetic makeup of populations.
Presentation : Modelling the spread of epidemics in heterogeneous communities He will present some recent developments regarding stochastic epidemic models in various kinds of heterogeneous communities. The models will include some randomness in the infectivity of individuals and heterogeneous contact rates between individuals, reflecting social and spatial structure. He will present some mathematical results and some challenges arising in the analysis of these models. |
Simon Gubbins is a group leader in Transmission Biology at The Pirbright Institute, UK. After completing a BSc in Mathematics at Imperial College London he undertook a PhD in Botanical Epidemiology at the University of Cambridge. His main research interest is understanding the transmission of viral diseases of livestock across scales. This work explores the question of how the dynamics of viruses at one scale (for example, within an animal or within a farm) influence the dynamics at another scale (for example, between animals or between farms). A particular focus of his work is the epidemiology of foot-and-mouth disease, bluetongue and African horse sickness, integrating field and laboratory data for these diseases into mathematical frameworks that can be used to inform disease control policy.
Presentation : Quantifying the relationship between within-host dynamics and transmission for livestock viruses He will present a simple modelling framework that links within-host dynamics and between-host transmission. Data from transmission experiments for two viral diseases of livestock, foot-and-mouth disease virus in cattle and swine influenza virus in pigs, are used to parameterise the model and, importantly, test the underlying assumptions. He will also show how the model can be extended to describe transmission of vector borne-diseases. |
Jeremie Guedj is a research scientist in biostatistics/pharmacometrics at the French Institute of Health & Medical Research (Inserm), specialized in infectious diseases and antiviral treatment. His researches have theoretical objectives, such as developing mathematical models to understand quantitative aspects of host/pathogen interaction. They also aim to impact clinical research by optimizing drug combination, dosing regimen and identify characteristics associated with a differential response to antiviral treatment.
His researches have initially focused on chronic viral infections (HIV, HBV, HCV) and have progressively shifted to acute emerging viral infections (SARS-CoV-2, viral hemorrhagic fever). He also applies the models and the statistical methods developed in virus dynamics to other fields of research, in particular bacterial dynamics (microbiota, phage therapy) and cancer. He works in the IAME laboratory devoted to infectious diseases, and located on the premises of Hospital Bichat campus, in the north of Paris. Together with Pr France Mentré, he is co-leading a group of pharmacometrics, biostatistics and clinical investigation in infectious diseases of about 30 young scientists (Master & PhD students, Postdocs) and tenured scientists. Presentation : SARS-CoV-2 viral dynamic models to inform on transmission and infection In this talk he will present viral dynamic models and how they can be used to understand some aspects of transmission and infection. He will discuss how they can be used, together with transmission models, to predict the impact of antiviral treatment strategies |
Jose is a CSIC Research Professor at IFISC in Palma de Mallorca, Spain. Broadly speaking, his research is focused on networks and how they can be used to better understand complex systems. More in particular, he is working in the application of complexity concepts to human mobility and transport, urban systems and the derivations of it such as the effect of mobility on infectious disease propagation.
Presentation: Spatial immunization to abate disease spreading in transportation hubs Proximity social interactions are crucial for infectious diseases transmission. Crowded agglomerations pose serious risk of triggering superspreading events. Locations like transportation hubs (airports and stations) are designed to optimize logistic efficiency, not to reduce crowding, and are characterized by a constant in and out flow of people. In this work, we analyze the paradigmatic example of London Heathrow, one of the busiest European airports. Thanks to a dataset of anonymized individuals' trajectories, we can model the spreading of different diseases to localize the contagion hotspots and to propose a spatial immunization policy targeting them to reduce disease spreading risk. We also detect the most vulnerable destinations to contagions produced at the airport and quantify the benefits of the spatial immunization technique to prevent regional and global disease diffusion. This method is immediately generalizable to train, metro and bus stations and to other facilities such as commercial or convention centers. |