Chagas Disease across Contexts: Scientific Knowledge in a Globalized World.

Chagas is the most important endemic disease in Latin America. It was progressively constructed as a relevant public issue, starting as a medical problem, focusing later on housing conditions, poverty, or vector agents. In recent decades, research has mainly focused on the parasite's biological characterization.

Spatial and temporal regularization to estimate COVID-19 reproduction number R(t): Promoting piecewise smoothness via convex optimization.

Among the different indicators that quantify the spread of an epidemic such as the on-going COVID-19, stands first the reproduction number which measures how many people can be contaminated by an infected person. In order to permit the monitoring of the evolution of this number, a new estimation procedure is proposed here, assuming a well-accepted model for current incidence data, based on past observations. The novelty of the proposed approach is twofold: 1) the estimation of the reproduction number is achieved by convex optimization within a proximal-based inverse problem formulation, with constraints aimed at promoting piecewise smoothness; 2) the approach is developed in a multivariate setting, allowing for the simultaneous handling of multiple time series attached to different geographical regions, together with a spatial (graph-based) regularization of their evolutions in time.

Who benefits most from Lyon's bike sharing system?

Bike sharing systems (BSS) have been growing fast all over the world, along with the number of articles analyzing such systems. However the lack of databases at the individual level and covering several years has limited the analysis of BSS users' behavior in the long term. This article gives a first detailed description of the temporal evolution of individual customers.

Giant Catalytic Effect of Altruists in Schelling's Segregation Model.

We study the effect of introducing altruistic agents in a Schelling-like model of residential segregation. We find that even an infinitesimal proportion of altruists has dramatic catalytic effects on the collective utility of the system. Altruists provide pathways that move the system away from the suboptimal equilibrium it would reach if the system included only egoist agents, allowing it to reach the optimal steady state.

Does Size Matter? The Multipolar International Landscape of Nanoscience.

How do different countries tackle nanoscience research? Are all countries similar except for a trivial size effect, as science is often assumed to be universal? Or does size dictate large differences, as large countries are able to develop activities in all directions of research, while small countries have to specialize in some specific niches? Alternatively, is size irrelevant, as all countries have followed different historical paths, leading to different patterns of specialisation? Here, we develop an original method that uses a bottom-up definition of scientific subfields to map the international structure of any scientific field. Our analysis shows that nanoscience research does not show a universal pattern of specialisation, homothetic of that of a single global leader (e.g.

Metapopulation epidemic models with heterogeneous mixing and travel behaviour.

Background: Determining the pandemic potential of an emerging infectious disease and how it depends on the various epidemic and population aspects is critical for the preparation of an adequate response aimed at its control. The complex interplay between population movements in space and non-homogeneous mixing patterns have so far hindered the fundamental understanding of the conditions for spatial invasion through a general theoretical framework. To address this issue, we present an analytical modelling approach taking into account such interplay under general conditions of mobility and interactions, in the simplifying assumption of two population classes.

Emergence of hierarchy in cost-driven growth of spatial networks.

One of the most important features of spatial networks--such as transportation networks, power grids, the Internet, and neural networks--is the existence of a cost associated with the length of links. Such a cost has a profound influence on the global structure of these networks, which usually display a hierarchical spatial organization. The link between local constraints and large-scale structure is not elucidated, however, and we introduce here a generic model for the growth of spatial networks based on the general concept of cost-benefit analysis.

'The whole is always smaller than its parts': a digital test of Gabriel Tardes' monads.

In this paper we argue that the new availability of digital data sets allows one to revisit Gabriel Tarde's (1843-1904) social theory that entirely dispensed with using notions such as individual or society. Our argument is that when it was impossible, cumbersome or simply slow to assemble and to navigate through the masses of information on particular items, it made sense to treat data about social connections by defining two levels: one for the element, the other for the aggregates. But once we have the experience of following individuals through their connections (which is often the case with profiles) it might be more rewarding to begin navigating datasets without making the distinction between the level of individual component and that of aggregated structure.

Opinion group formation and dynamics: structures that last from nonlasting entities.

We extend simple opinion models to obtain stable but continuously evolving communities. Our scope is to meet a challenge raised by sociologists of generating "structures that last from nonlasting entities." We achieve this by introducing two kinds of noise on a standard opinion model.

Competition between collective and individual dynamics.

Linking microscopic and macroscopic behavior is at the heart of many natural and social sciences. This apparent similarity conceals essential differences across disciplines: Although physical particles are assumed to optimize the global energy, economic agents maximize their own utility. Here, we solve exactly a Schelling-like segregation model, which interpolates continuously between cooperative and individual dynamics.

Analysis of community structure in networks of correlated data.

We present a reformulation of modularity that allows the analysis of the community structure in networks of correlated data. The modularity preserves the probabilistic semantics of the original definition even when the network is directed, weighted, signed, and has self-loops. This is the most general condition one can find in the study of any network, in particular those defined from correlated data.

Network-based predictions of retail store commercial categories and optimal locations.

I study the spatial organization of retail commercial activities. These are organized in a network comprising "antilinks," i.e.