Migration and divisions:thoughts on (anti-) narrativity in visual representations of mobile people

This article developed as part of a dialogue between the two authors. The dialogue was sparked off by MARLEY's response to a seminar presentation by GILLIGAN. In keeping with its origins we have retained the dialogue format. The article focuses on two sets of images—one a still image taken by a photojournalist, the other a sequence of stills taken by one of the authors. The authors use these images to explore the question "what imbues an image with narrative content?" and to explore the possibilities for developing a positive visual representation which promotes the idea of open borders. The article draws on linguistic theory to explore the grammar of visual narrative and relates this to the issue of the visual representation of immigration in contemporary Europe.

Progmosis:evaluating risky individual behavior during epidemics using mobile network data

The possibility to analyze, quantify and forecast epidemic outbreaks is fundamental when devising effective disease containment strategies. Policy makers are faced with the intricate task of drafting realistically implementable policies that strike a balance between risk management and cost. Two major techniques policy makers have at their disposal are: epidemic modeling and contact tracing. Models are used to forecast the evolution of the epidemic both globally and regionally, while contact tracing is used to reconstruct the chain of people who have been potentially infected, so that they can be tested, isolated and treated immediately. However, both techniques might provide limited information, especially during an already advanced crisis when the need for action is urgent. In this paper we propose an alternative approach that goes beyond epidemic modeling and contact tracing, and leverages behavioral data generated by mobile carrier networks to evaluate contagion risk on a per-user basis. The individual risk represents the loss incurred by not isolating or treating a specific person, both in terms of how likely it is for this person to spread the disease as well as how many secondary infections it will cause. To this aim, we develop a model, named Progmosis, which quantifies this risk based on movement and regional aggregated statistics about infection rates. We develop and release an open-source tool that calculates this risk based on cellular network events. We simulate a realistic epidemic scenarios, based on an Ebola virus outbreak; we find that gradually restricting the mobility of a subset of individuals reduces the number of infected people after 30 days by 24%.