Distanciation physique

Résumé

vignette|Respect des distances physiques dans la (avec marquage au sol) d'un bureau de poste à Genève en avril 2020. lang=fr|vignette|Affiche de l'Office suisse de la santé publique recommandant de « garder ses distances » pendant la pandémie de coronavirus de 2019-2020. vignette|alt=Alignements de petits cercles blancs, certains vides, certains occupés par une personne.|Marquages au sol pour favoriser le respect des distances physiques au Dolores Park de San Francisco. Mai 2020. vignette|Marquages et séparations de distanciation sociale, Église catholique du Sacré-cœur le 17 juin 2020 à Montreux, Suisse La distanciation physique, également appelée distanciation sociale ou éloignement sanitaire, est une mesure visant à éloigner les individus les uns des autres. C'est l'une des mesures non pharmaceutiques de contrôle des infections prises par les responsables de la santé publique pour arrêter ou ralentir la propagation d'une m

Source officielle

https://fr.wikipedia.org/wiki/Distanciation_physique

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Dynamic Model Predictions of Covid-19 Pandemic Mitigation Scenarios

David Jenny

Relevant pandemic-spread scenario simulations provide guiding principles for containment and mitigation policy developments. In this report we discuss model predictions for infected, recovered and dead people during a pandemic. The model consists of a simple reaction mechanism, which considers the population size, reported and unreported infections, reported and unreported recoveries and number of deaths. It is assumed that recovered persons are immune (e.g. mutations are not considered), that the virus can only be passed on by unreported infected persons and that no vaccine will be found within the considered periods. Moreover, the model does not take into account age dependency, but considers higher mortality rates due to temporary shortage of emergency rooms. Some of the model parameters have been fitted to the reported cases outside of China1 from January 22 to March 12 of the 2020 Covid-19 pandemic. The other parameters were chosen in a plausible range and sensitivity studies were performed; among all tested parameter values, those leading to the lowest number of deaths were chosen for the presented case studies, i.e., the predictions are rather optimistic. Scenarios in which the infection rate per person is reduced for periods of 680 and 183 days (”social distancing phase”) and scenarios in which the testing frequency is increased for 680 days (”frequent testing phase”) are studied. While the predicted number of deaths can dramatically be reduced during the ”social distancing” and ”frequent testing” phases, the fatality numbers increase again some time after these phases end; for all these scenarios the predicted final number of deaths lies between 3% and 5% of the initial population (not much different from the base case). It is interesting, however, that more frequent testing seems to be much more effective than social distancing and may cause less economic damage. The currently low detection rate is obviously linked to the long incubation period typical for Covid-19. Incubation for typical flu is much shorter, and as soon people fall ill and stay at home, they are effectively detected and do not further spread the virus.

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The COVID-19 pandemic outbreak is causing a dramatic worsening in the already complicated living conditions of blind and visually impaired individuals. Social distancing is the most effective strategy to limit virus spread, but is extremely difficult for blind people to actuate. Here we propose a deep-learning algorithm to recognize and locate in space people and other categories of objects from RGB-D images. The algorithm, based on Mask R-CNN, performs semantic segmentation on RGB images and uses depth maps to extract information about the relative distance of the instances. It was evaluated using Salient Person dataset and RGB-D Scenes Dataset v.2, and proved effective in segmenting and locating instances. This preliminary work could be a valuable starting point for developing a technology to assist the visually impaired in implementing social distancing.

IEEE2022

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Designing Online Multiplayer Games with Haptically and Virtually Linked Tangible Robots to Enhance Social Interaction in Therapy

Mehdi Akeddar, Pierre Dillenbourg, Arzu Güneysu Özgür, Hala Khodr

The social aspects of therapy and training are important for patients to avoid social isolation and must be considered when designing a platform, especially for home-based rehabilitation. We proposed an online version of the previously proposed tangible Pacman game for upper limb training with haptic-enabled tangible Cellulo robots. Our main objective is to enhance motivation and engagement through social integration and also to form a gamified multiplayer rehabilitation at a distance. Thus, allowing relatives, children, and friends to connect and play with their loved ones while also helping them with their training from anywhere in the world. As well as connecting therapists to their patients through haptically linking capabilities. This is especially relevant when there are social distancing measures which might isolate the elderly population, a majority of all rehabilitation patients.

IEEE2022

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Dynamic Model Predictions of Covid-19 Pandemic Mitigation Scenarios

David Jenny