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. In the meanwhile, both Chagas disease and knowledge about it spread out geographically. We analyze the worldwide scientific production on Chagas, showing that countries' research strategies depend on two main factors: endemicity and research traditions. This approach complements previous studies, allowing us to better understand the construction of Chagas disease as a social and scientific problem.

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. The effectiveness of the approach is first supported by simulations, and two main applications to real COVID-19 data are then discussed. The first one refers to the comparative evolution of the reproduction number for a number of countries, while the second one focuses on French departments and their joint analysis, leading to dynamic maps revealing the temporal co-evolution of their reproduction numbers.

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. Using a 5-year dataset covering 120,827 distinct year-long subscribers, we show the heterogeneous individual trajectories masked by the overall system stability. Users follow two main trajectories: about half remain in the system for at most one year, showing a low median activity (47 trips); the remaining half corresponds to more active users (median activity of 91 trips in their first year) that remain continuously active for several years (mean time = 2.9 years). We show that users from urban cores, middle-aged and male are over represented among these long-term users, which profit most from the BSS. This provides further support for the view that BSS mostly benefit the already privileged.

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., the United States). Instead, we find a multipolar world, with four main ways of doing nanosciences.

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. METHODS: We describe a spatially structured population with non-homogeneous mixing and travel behaviour through a multi-host stochastic epidemic metapopulation model. Different population partitions, mixing patterns and mobility structures are considered, along with a specific application for the study of the role of age partition in the early spread of the 2009 H1N1 pandemic influenza. RESULTS: We provide a complete mathematical formulation of the model and derive a semi-analytical expression of the threshold condition for global invasion of an emerging infectious disease in the metapopulation system. A rich solution space is found that depends on the social partition of the population, the pattern of contacts across groups and their relative social activity, the travel attitude of each class, and the topological and traffic features of the mobility network. Reducing the activity of the less social group and reducing the cross-group mixing are predicted to be the most efficient strategies for controlling the pandemic potential in the case the less active group constitutes the majority of travellers. If instead traveling is dominated by the more social class, our model predicts the existence of an optimal across-groups mixing that maximises the pandemic potential of the disease, whereas the impact of variations in the activity of each group is less important. CONCLUSIONS: The proposed modelling approach introduces a theoretical framework for the study of infectious diseases spread in a population with two layers of heterogeneity relevant for the local transmission and the spatial propagation of the disease. It can be used for pandemic preparedness studies to identify adequate interventions and quantitatively estimate the corresponding required effort, as well as in an emerging epidemic situation to assess the pandemic potential of the pathogen from population and early outbreak data.

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. This model depends essentially on a single scale and produces a family of networks that range from the star graph to the minimum spanning tree and are characterized by a continuously varying exponent. We show that spatial hierarchy emerges naturally, with structures composed of various hubs controlling geographically separated service areas, and appears as a large-scale consequence of local cost-benefit considerations. Our model thus provides the basic building blocks for a better understanding of the evolution of spatial networks and their properties. We also find that, surprisingly, the average detour is minimal in the intermediate regime as a result of a large diversity in link lengths. Finally, we estimate the important parameters for various world railway networks and find that, remarkably, they all fall in this intermediate regime, suggesting that spatial hierarchy is a crucial feature for these systems and probably possesses an important evolutionary advantage.

'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. It becomes possible to give some credibility to Tarde's strange notion of 'monads'. We claim that it is just this sort of navigational practice that is now made possible by digitally available databases and that such a practice could modify social theory if we could visualize this new type of exploration in a coherent way.

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. First, agents may interact with other agents even if their opinion difference is large. Second, agents randomly change their opinion at a constant rate. We show that for a large range of control parameters, our model yields stable and fluctuating polarized states, where the composition and mean opinion of the emerging groups is fluctuating over time.

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. We show that increasing the degree of cooperativity induces a qualitative transition from a segregated phase of low utility toward a mixed phase of high utility. By introducing a simple function that links the individual and global levels, we pave the way to a rigorous approach of a wide class of systems, where dynamics are governed by individual strategies.

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. We apply our results to a real network of correlated data between stores in the city of Lyon (France).

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., links of negative weight. From pure location data, network analysis leads to a community structure that closely follows the commercial classification of the U.S. Department of Labor. The interaction network allows one to build a "quality" index of optimal location niches for stores, which has been empirically tested.