Étiquette : visualisation (Page 1 of 8)

Vivre avec le coronavirus – Le Grand Continent

Vivre avec le coronavirus

“ Relâcher les contraintes mais pas l’attention, reprendre le cours de nos vies sans relancer l’épidémie : les pays européens cherchent désormais à définir les conventions d’une nouvelle normalité où le temps du gouvernement ne soit plus seulement l’urgence. Pour apprécier cette transition, ses étapes et ses dangers, nous suivons les indicateurs clefs de la circulation du virus, des politiques sanitaires et de leurs impacts géopolitiques. ”

Source : Vivre avec le coronavirus – Le Grand Continent

Remembering the Nearly 100,000 Lives Lost to Coronavirus in America – The New York Times

us coronavirus deaths 100000

« Toward the end of May in the year 2020, the number of people in the United States who have died from the coronavirus neared 100,000 — almost all of them within a three-month span. An average of more than 1,100 deaths a day ».

Source : Remembering the Nearly 100,000 Lives Lost to Coronavirus in America – The New York Times

Real-time data show virus’s hit to global economic activity

A graphic with no description

“A group of former IMF chief economists warned last weekend that a global recession had already begun, but although economic activity is slowing sharply, much official data are out of date before they are even published, given the time they take to collate.  To make up for the lack of official information, the FT has compiled a set of alternative, high-frequency measures of economic activity for different sectors which give an early indication of what to expect when official data start to become available in the coming weeks. ”

Source : Real-time data show virus’s hit to global economic activity | Financial Times

Epidemic Calculator – Gabriel Goh

Epidemic Calculator

“At the time of writing, the coronavirus disease of 2019 remains a global health crisis of grave and uncertain magnitude. To the non-expert (such as myself), contextualizing the numbers, forecasts and epidemiological parameters described in the media and literature can be challenging.
Gabriel Goh created this calculator as an attempt to address this gap in understanding. This calculator implements a classical infectious disease model — SEIR (Susceptible → Exposed → Infected → Removed), an idealized model of spread still used in frontlines of research e.g. [Wu, et. al, Kucharski et. al].”

Source : Epidemic Calculator

COVID19 Infodemics Observatory

Infodemics

“About 100M public messages have been collected and analyzed to understand the digital response in online social media to COVID-19 outbreak. Specifically, we used machine learning techniques to quantify: collective sentiment & psychology: lexicon-based and rule-based emotional and psychological state social bot pollution: The fraction of activities due to social bots and the exposure of the Twitterverse to unreliable news News reliability: the fraction of URLs pointing to reliable news and scientific sources”

Source : COVID19 Infodemics Observatory

Genomic epidemiology of novel coronavirus

Genomic epidemiology of novel coronavirus

“This phylogeny shows evolutionary relationships of hCoV-19 (or SARS-CoV-2) viruses from the ongoing novel coronavirus COVID-19 pandemic. This phylogeny shows an initial emergence in Wuhan, China, in Nov-Dec 2019 followed by sustained human-to-human transmission leading to sampled infections. Although the genetic relationships among sampled viruses are quite clear, there is considerable uncertainty surrounding estimates of transmission dates and in reconstruction of geographic spread. Please be aware that specific inferred transmission patterns are only a hypothesis.”

Source : Nextstrain / ncov

7 best practices for mapping a pandemic – Points of interest

“We are not epidemiologists—we are the design, engineering, and support teams at a mapping company. Just as we don’t spend every moment thinking about how to track and slow the spread of infectious disease, most public health teams we work with are not experts in spatial data visualization. In the environment of a growing epidemic, maps have a way of spreading fast, too — making it imperative that they are accurate, informative, and thoughtfully designed. To answer common questions and help our partners make thoughtful design decisions while mapping this and other health crises, we have put together a number of best practices and common pitfalls to avoid.”

Source : 7 best practices for mapping a pandemic – Points of interest

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

“Over the past few months, I have been collecting AI cheat sheets. From time to time I share them with friends and colleagues and recently I have been getting asked a lot, so I decided to organize and share the entire collection.”

Source : Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

Cartographie numérique: Visualiser les densités de population en 3D et à l’échelle mondiale

Matthew Daniels, qui a élaboré cette application, a représenté l’ensemble de la population mondiale sous forme de blocs 3D avec un dégradé de couleurs du vert clair au bleu foncé. Ce type de cartographie est surtout efficace pour représenter les « pics » de population dans les zones urbanisées.

Source : Cartographie numérique: Visualiser les densités de population en 3D et à l’échelle mondiale

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