M
Marcel Salathé
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 120
Citations - 11559
Marcel Salathé is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Population & Social media. The author has an hindex of 40, co-authored 114 publications receiving 8713 citations. Previous affiliations of Marcel Salathé include Pennsylvania State University & Stanford University.
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Using Deep Learning for Image-Based Plant Disease Detection
TL;DR: In this article, a deep convolutional neural network was used to identify 14 crop species and 26 diseases (or absence thereof) using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions.
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Modelling the influence of human behaviour on the spread of infectious diseases: a review
TL;DR: Recent efforts to incorporate human behaviour into disease models are reviewed, and it is proposed that such models can be broadly classified according to the type and source of information which individuals are assumed to base their behaviour on, andAccording to the assumed effects of such behaviour.
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Statistical physics of vaccination
Zhen Wang,Zhen Wang,Chris T. Bauch,Samit Bhattacharyya,Alberto d’Onofrio,Piero Manfredi,Matjaz Perc,Nicola Perra,Marcel Salathé,Dawei Zhao +9 more
TL;DR: This report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure.
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A high-resolution human contact network for infectious disease transmission
Marcel Salathé,Maria A. Kazandjieva,Jung Woo Lee,Philip Levis,Marcus W. Feldman,James Holland Jones +5 more
TL;DR: High-resolution data of CPIs during a typical day at an American high school is obtained, permitting the reconstruction of the social network relevant for infectious disease transmission and suggested that contact network data are required to design strategies that are significantly more effective than random immunization.
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Dynamics and Control of Diseases in Networks with Community Structure
TL;DR: It is found that community structure has a major impact on disease dynamics, and it is shown that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals.