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Alessandro Vespignani, Northeastern University, Boston 3.5.2017

Epidemic modeling does more than forecast

Recent years have witnessed the development of data driven models of infectious diseases rooted in the combination of large–scale data mining techniques, computational approaches and mathematical modeling. Although these models are increasingly used to support public-health decisions they are often under debate by only considering their value as forecasting tools. Here I will discuss, by using specific modeling examples of the H1N1 pandemic and the West Africa Ebola epidemic, how computational models can be used in real time to provide situational awareness, intervention planning and projections, and the identification of factors that fundamentally influence the course of an outbreak.

Marcel Salathe, EPFL, Lausanne 17.5.2017

Digital Health & Epidemiology

Online, mobile, global - the ongoing digital revolution affects all aspects of life. Massive amounts of data are now shared by billions of people around the globe through mobile phones, social media services, and other outlets, on any issue imaginable, including issues of health. These data sources can be mined for epidemiological purposes, giving rise to digital epidemiology. Of equal importance, but less discussed, is the fact that these large data sets (big data) provide the raw material for new machine learning algorithms to train on (e.g . "deep learning"), resulting in software that in various domains is close to achieving, or already has achieved, human performance. As human expertise, specifically also white collar expertise, can increasingly be replaced by artificial intelligence, huge disruptive potential will be unleashed. The health domain in particular will be affected deeply. In this seminar, I will discuss opportunities and challenges in these turbulent times.

Sune Lehmann, DTU Kopenhagen 31.5.2017

The dynamic network of meetings in physical space (and some implications for epidemiology)

Using top-of-the-line cell phones running custom software as social sensors, my group has measured the face-to-face meetings and mobility patterns of nearly 1000 densely connected individuals (freshmen at the Technical University of Denmark) from medio 2014 to primo 2016. In my talk I begin by discussing the data collection effort. Next, I explain our recent finding that the dynamic network itself is structured around certain fundamental patterns that characterize how individuals tend meet in physical space. Finally, I discuss how these contact networks may shape the spreading of infectious diseases. Specifically I will talk about our recent work which analyzes the role of the spreading mechanism (long range vs short range), and when to use online social networks to understand epidemic spreading in the real world.

Richard Neher, Tübingen, 14.6.2017

Real-time tracking of seasonal influenza, Ebola, and Zika viruses

Human seasonal influenza viruses evolve rapidly, enabling the virus population to evade immunity and reinfect previously infected individuals. To maintain efficacy, the vaccine needs to be updated whenever the viruses change antigenically. To facilitate timely and informed decisions on the influenza vaccine composition, we have developed, an automated real-time analysis tool that ingests influenza virus sequences, analyzes those data phylogenetically, and presents the results to the user via an interactive and intuitive web-tool. In addition to tracking, also implements methods that predict which viral variant is most likely going to dominate the upcoming season. Beyond seasonal influenza, phylogenetic analysis and molecular epidemiology can help elucidate transmission patterns in public health crisis. During the recent outbreaks of Ebola virus in West Africa and Zika virus in the Americas, we have used our tools to analyze viral genomes immediately after they are sequenced. We are currently developing a flexible tool-box that can be rapidly deployed for new emerging diseases and provide real-time phylogenetic analysis for diverse emerging diseases. I will discuss the concepts, potential, and challenges of real-time phylogenetics for public health policy.

Oliver Pybus, Oxford, 28.6.2017

Genomic epidemiology of emerging viruses: from avian influenza to Zika

Pathogen genome sequences contain a remarkable amount of information about epidemiological processes. With appropriate analysis, they can reveal where and when an outbreak initiated, estimate transmission rates, quantify routes and rates of spatial spread, and inform studies of pathogenicity. Yet the contribution that genomics can make to infectious disease surveillance and outbreak control is only beginning to be appreciated by public health agencies. Faster, cheaper and more portable sequencing technologies mean that genomics can now take place alongside field epidemiology investigations. I will outline the opportunities and challenges ahead as we try to formally integrate genomic, spatial, and epidemiological data. I will present results from recent epidemics, such as highly pathogenic H5N8 avian influenza virus in Europe, Ebola virus in west Africa, and Zika virus in the Americas. I’ll also introduce the ZiBRA project, a mobile sequencing lab that travelled across north-east Brazil to study Zika virus in summer 2016.

Ciro Cattuto, Turin, 5.7.2107
High resolution contact networks: from sensor data to targeted interventions

Digital technologies provide the opportunity to quantify human behavior with unprecedented levels of detail and scale. Personal electronic devices and wearable sensors, in particular, can be used to map the network structure of human or animal close-range interactions in a variety of settings relevant for research in epidemiology and public health. This talk will review the experience of the SocioPatterns collaboration, an ongoing effort aimed at measuring and studying high-resolution contact networks using wearable proximity sensors. I will discuss measurement experiences in diverse environments comprising schools, hospitals, households and low-resource rural settings, and reflect on challenges such as generalization and data incompleteness. I will illustrate the complex structural and temporal features of empirical high-resolution contact networks, discuss their effect on the dynamics of epidemic processes, and show how high-resolution contact networks can be used to design targeted intervention strategies.

Stand: 24.03.2017