====== Épidémie du coronavirus COVID-19 ====== Références : * [[wp>Coronavirus_disease_2019|Coronavirus disease 2019]] * [[wp>fr:Maladie_à_coronavirus_2019|Maladie à coronavirus 2019]] * [[https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6|Coronavirus COVID-19 Global Cases by Johns Hopkins CSSE]] * [[https://www.worldometers.info/coronavirus/coronavirus-death-rate/|Coronavirus (COVID-19) Mortality Rate]] * data : [[https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data]] ===== Programmes de représentations ===== FIXME Quelques simulations SEIR effectuées par des scientifiques : * Marius Gilbert (ULB/FNRS, Spatial Epidemiology lab ([[http://spell.ulb.be/|SpELL]]), [[https://twitter.com/mariusgilbert/status/1244564877882114048]],... * Nicolas Vandewalle (ULiège, thermodynamique statistique) [[https://twitter.com/vdwnico]] * [[https://twitter.com/T_Fiolet/status/1239658681995796485]] ===== Simulations numériques ===== Quelques modèles simplifiés sont analogues de schémas réactionnels en chimie (réactions en chaîne notamment). Des approches déterministes permettent d'obtenir des systèmes d'équations différentielles ordinaires assez simples. Par exemple le modèle SEIR : Susceptible, Exposed ("porteur contaminé, sain, en incubation), Infectious (contagieux), Recovered (guéri). La mortalité due à la maladie n'est pas considérée dans ce modèle simplifié. * modèle SIR = modèle encore plus simple * [[https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SEIR_model|SEIR model]] * [[http://www.modelinginfectiousdiseases.org/]] * [[http://homepages.warwick.ac.uk/~masfz/ModelingInfectiousDiseases/Chapter2/Program_2.6/index.html|chapitre 2, programme 2.6]] * [[https://www.nature.com/articles/s41421-020-0148-0|Phase-adjusted estimation of the number of Coronavirus Disease 2019 cases in Wuhan, China]] → paramètres pour le COVID-19 * paramètres FIXME * average incubation period a * transmission rate : β * infection rate : σ → 1/a = 1/5.2 * recovery rate : γ → 1/18 * le "basic reproduction number" R0 (approximativement β/γ ??) → 2.6 ?? * natalité et mortalité de base négligée dans l'article utilisé * valeurs initiales (par exemple) : * S = 11 millions * E = 20 * I * I = 40 * R = 0 * lien à d'autres nombres : * basic reproduction number La relaxation collisionnelle, traitée par la "master équation de Pauli" pourrait être comparée, voire appliquée aux épidémies. ===== Représentations et simulations existantes ===== * [[https://towardsdatascience.com/understanding-the-coronavirus-epidemic-data-44d2fb356ecb|Understanding the Coronavirus Epidemic Data - Towards Data Science]] * [[https://github.com/BlankerL/DXY-COVID-19-Data/blob/master/README.en.md|DXY-COVID-19-Data/README.en.md at master · BlankerL/DXY-COVID-19-Data · GitHub]] * [[https://towardsdatascience.com/behind-the-coronavirus-mortality-rate-4501ef3c0724|Behind the Coronavirus Mortality Rate - Towards Data Science]] * [[https://art-bd.shinyapps.io/nCov_control/|Reporting, epidemic growth, and reproduction numbers for the 2019-nCoV epidemic: understanding control]] → simulation assez simple (modèle SEIR) qui introduit un "Effective reproductive number with control", c'est à dire le nombre de personnes infectées par une personne ayant chopé la maladie, en tenant compte de mesures de contrôle (les consignes, y compris le confinement). Si vous faites glisser ce paramètre en le diminuant, ou en l'augmentant de simplement 0.5 (une "demi" personne en plus ou en moins), vous observerez des effets colossaux sur le nombre de cas, donc le nombre de mort, de personnes gravement atteintes, et la durée de la crise et de ses conséquences sur la situation économique * [[https://www.washingtonpost.com/graphics/2020/health/coronavirus-how-epidemics-spread-and-end/|How epidemics like COVID-19 end (and how to end them faster)]], 19/02/2020, Washington Post * [[https://pythonprogramming.altervista.org/getting-data-about-coronavirus-with-python-in-italy/?doing_wp_cron=1582794641.9605190753936767578125|Getting data about Coronavirus with Python in Italy]] Posted by pythonprogramming on 26/02/2020 * [[https://github.com/pdtyreus/coronavirus-ds|Coronavirus Data Science]] Jupyter notebooks and python scripts, * [[https://github.com/ExpDev07/coronavirus-tracker-api|ExpDev07/coronavirus-tracker-api: 🦠 A simple and fast (< 200ms) API for tracking the global coronavirus (2019-nCoV) outbreak. It’s written in python using the 🍼 Flask framework.]] * [[https://github.com/YiranJing/Coronavirus-Epidemic-2019-nCov|YiranJing/Coronavirus-Epidemic-2019-nCov: 👩🏻‍⚕️Covid-19 estimation and forecast using statistical model; 新型冠状病毒武汉肺炎统计模型预测]] * [[https://github.com/temp3rr0r/CellularAutomataEpidemicModels|temp3rr0r/CellularAutomataEpidemicModels: Stochastic Cellular Automata epidemic models in Python with 2D simulations]] * [[https://github.com/GiulioRossetti/ndlib|GiulioRossetti/ndlib: Network Diffusion Library - (for NetworkX and iGraph)]] * [[https://github.com/zafarali/disease-network-model|zafarali/disease-network-model: A Model To Simulate Diseases on a Network Structure]] * [[https://github.com/branchwelder/KillAllAgents|branchwelder/KillAllAgents: An agent-based model of infectious disease spread.]] * [[https://github.com/j-i-l/EndemicPy|j-i-l/EndemicPy: Python package to simulate a vast range of transmission processes on various structures]] * Jupyter notebooks : * [[https://github.com/pdtyreus/coronavirus-ds]] * [[https://towardsdatascience.com/coronavirus-data-visualizations-using-plotly-cfbdb8fcfc3d|Coronavirus data visualizations using Plotly]] * Modèle SEIR appliqué à l'épidémie en Chine : [[https://www.nature.com/articles/s41421-020-0148-0|Phase-adjusted estimation of the number of Coronavirus Disease 2019 cases in Wuhan, China]] Wang, H., Wang, Z., Dong, Y. et al. Cell Discov 6, 10 (2020) DOI: 10.1038/s41421-020-0148-0 * [[http://gabgoh.github.io/COVID/index.html]] * [[https://github.com/gabgoh/gabgoh.github.io/tree/master/COVID]] * [[https://cream.io/|COVID Rules Everything Around Me]] Understand COVID growth rates between different countries * [[https://www.technologyreview.com/s/615414/the-covid-19-pandemic-in-two-animated-charts/|The Covid-19 pandemic in two animated charts]] by Bobbie Johnson, Mar 27, 2020, MIT Technology Review * [[https://towardsdatascience.com/exploring-the-corona-virus-dataset-781de3a636e2|Exploring the Coronavirus Dataset]] Exploratory Data Analysis of the Novel Coronavirus 2019 Dataset, Sadrach Pierre, Medium, 06/03/2020 * [[https://towardsdatascience.com/visualizing-covid-19-data-beautifully-in-python-in-5-minutes-or-less-affc361b2c6a|Visualizing COVID-19 Data Beautifully in Python (in 5 Minutes or Less!!) Making Matplotlib a Little Less Painful!]], Nik Piepenbreier, Medium, 06/04/2020 * Graphiques et évolutions par Nicolas Vandewalle (ULiège) et collaborateurs : [[https://github.com/glouppe/covid19be]] * Simulations de Marc Ducobu (avec du code en Python) : [[https://gitlab.com/colibre-19/epidemic-simulation]] (licence MIT) * [[https://towardsdatascience.com/building-an-interactive-dashboard-to-simulate-coronavirus-scenarios-in-python-ed23100e0046|Build an interactive dashboard to simulate Coronavirus scenarios in Python]] (avec librairies Plotly, Dash,...) * [[https://schlaganfallbegleitung.de/corona/lage-deutschland]] représentations de données concernant l'Allemagne * [[https://towardsdatascience.com/covid-19-map-animation-with-python-in-5-minutes-2d6246c32e54|Create COVID-19 Map Animation with Python in 5 Minutes]] (Using Python with Plotly to Create a COVID-19 Map Animation) * [[https://www.statsandr.com/blog/covid-19-in-belgium/|COVID-19 in Belgium - Stats and R]] * [[https://renkulab.shinyapps.io/COVID-19-Epidemic-Forecasting/|COVID-19 Forecasting]] * [[https://towardsdatascience.com/top-5-r-resources-on-covid-19-coronavirus-1d4c8df6d85f|Top 100 R resources on Novel COVID-19 Coronavirus | by Antoine Soetewey | Towards Data Science]] * [[https://behroozh.shinyapps.io/COVID19/|COVID-19 Application]] * [[http://shinyapps.org/apps/corona/|Experience Statistics]] ===== Tracking, tracing & géolocalisation ===== * [[https://blog.mapbox.com/mobility-data-to-track-risk-in-re-opening-739e5c20f3ed|Mobility data to track risk in re-opening]] ===== Références ===== * [[https://dial.uclouvain.be/memoire/ucl/en/object/thesis:4627]] * [[https://iopscience.iop.org/article/10.1088/1742-6596/1392/1/012047/meta|Numerical simulation of a spatial – temporal model of epidemic distribution - IOPscience]] * [[https://www.epfl.ch/labs/lamp/wp-content/uploads/2019/01/simulations-epfl.html|Epidemic Simulation]] * [[https://www.ceremade.dauphine.fr/~turinici/index.php/en/research/researchtopics/23-simulationsinbiologyandmedicine|Mathematical simulations in medicine and biology]] * [[https://pythonhosted.org/epydemic/|epydemic: Epidemic simulations on networks in Python — epydemic 0.1.0 documentation]] [[https://arxiv.org/abs/1503.04066|[1503.04066] Compensating for population sampling in simulations of epidemic spread on temporal contact networks]] * [[https://royalsocietypublishing.org/doi/full/10.1098/rstb.2018.0279|Perfect counterfactuals for epidemic simulations | Philosophical Transactions of the Royal Society B: Biological Sciences]] * [[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6050668/|High-resolution epidemic simulation using within-host infection and contact data]] * [[https://drphilbe.blogspot.com/2020/03/les-chiffres-que-vous-trouverez-ici.html|Coronavirus : Armageddon ou Foutaise ?]] Dr Philippe Devos, président du Syndicat Belge des Médecins ABSYM, 02/03/2020 * [[wp>Mathematical_modelling_of_infectious_disease|Mathematical modelling of infectious disease]] * [[wp>Basic_reproduction_number|Basic reproduction number]] * [[wp>Compartmental_models_in_epidemiology|Compartmental models in epidemiology]] * **[[https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SEIR_model|SEIR model]]** * [[wp>fr:Modèles_compartimentaux_en_épidémiologie|Modèles compartimentaux en épidémiologie]] * [[https://lejournal.cnrs.fr/articles/covid-19-comment-sont-concus-les-modeles-des-epidemies|Covid-19 : comment sont conçus les modèles des épidémies ?]], 20.03.2020, par Martin Koppe, CNRS Le journal * **Données pour la Belgique :** * [[https://epistat.wiv-isp.be/Covid/]] (Sciensano - COVID-19) * [[https://www.covidata.be/index]], sous licence [[https://opencovidpledge.org/license/v1-0/|Open COVID License 1.0]] (licence pas vraiment libre, puisque limitée dans le temps)