Epidemic Wave Dynamics Attributable to Urban Community Structure: A Theoretical Characterization of Disease Transmission in a Large Network.

BACKGROUND: Multiple waves of transmission during infectious disease epidemics represent a major public health challenge, but the ecological and behavioral drivers of epidemic resurgence are poorly understood. In theory, community structure—aggregation into highly intraconnected and loosely interconnected social groups—within human populations may lead to punctuated outbreaks as diseases progress from one community to the next. However, this explanation has been largely overlooked in favor of temporal shifts in environmental conditions and human behavior and because of the difficulties associated with estimating large-scale contact patterns. OBJECTIVE: The aim was to characterize naturally arising patterns of human contact that are capable of producing simulated epidemics with multiple wave structures. METHODS: We used an extensive dataset of proximal physical contacts between users of a public Wi-Fi Internet system to evaluate the epidemiological implications of an empirical urban contact network. We characterized the modularity (community structure) of the network and then estimated epidemic dynamics under a percolation-based model of infectious disease spread on the network. We classified simulated epidemics as multiwave using a novel metric and we identified network structures that were critical to the network's ability to produce multiwave epidemics. RESULTS: We identified robust community structure in a large, empirical urban contact network from which multiwave epidemics may emerge naturally. This pattern was fueled by a special kind of insularity in which locally popular individuals were not the ones forging contacts with more distant social groups. CONCLUSIONS: Our results suggest that ordinary contact patterns can produce multiwave epidemics at the scale of a single urban area without the temporal shifts that are usually assumed to be responsible. Understanding the role of community structure in epidemic dynamics allows officials to anticipate epidemic resurgence without having to forecast future changes in hosts, pathogens, or the environment

Mapping drivers of tropical forest loss with satellite image time series and machine learning

The rates of tropical deforestation remain high, resulting in carbon emissions, biodiversity loss, and impacts on local communities. To design effective policies to tackle this, it is necessary to know what the drivers behind deforestation are. Since drivers vary in space and time, producing accurate spatially explicit maps with regular temporal updates is essential. Drivers can be recognized from satellite imagery but the scale of tropical deforestation makes it unfeasible to do so manually. Machine learning opens up possibilities for automating and scaling up this process. In this study, we developed and trained a deep learning model to classify the drivers of any forest loss—including deforestation—from satellite image time series. Our model architecture allows understanding of how the input time series is used to make a prediction, showing the model learns different patterns for recognizing each driver and highlighting the need for temporal data. We used our model to classify over 588 ′ 000 sites to produce a map detailing the drivers behind tropical forest loss. The results confirm that the majority of it is driven by agriculture, but also show significant regional differences. Such data is a crucial source of information to enable targeting specific drivers locally and can be updated in the future using free satellite data

Diffusive Realization of a Lyapunov Equation Solution, and its FPGA Implementation

International audienceIn Yakoubi [2010] and Lenczner et al. [2010] we developed a theoretical framework of diffusive realization for state-realizations of some linear operators. Those are solutions to certain operator linear differential equations inone-dimensional bounded domains. We also illustrated the theory and developed a numerical method for a Lyapunov equation arising from optimal control theory of the heat equation. However, the principles of our numerical methods were only sketched, and now we provide more details. Then, we do not only provide validation results of the method, but we also report our experience in its implementation on a Field Programmable Gate Arrays (FPGA), for the purpose of promoting embedded real-time computation

Reading Of The Sacroiliac Joints On Plain Radiographs: Agreement Between Clinical Practice and Trained Central Reading Of The DESIR-Cohort

Pathophysiology and treatment of rheumatic disease

Classification Of Axspa Based On Positive Imaging (Radiographs and/or MRI of the Sacroiliac Joints) By Local Rheumatologists Or Radiologists Versus Central Trained Readers In The DESIR-Cohort

Imaging- and therapeutic targets in neoplastic and musculoskeletal inflammatory diseas