Use the options to choose the level of anonymization. Are there more stable links like YouTube? For instance if a distance of six feet between each person for two weeks maximum incubation would break the transmission, then the epidemic could end in two weeks if everybody actually complied, and there were no people with carrier states. Could someone please share with me (notebook?) We will summarize these articles in the following list: COVID-19 Livestream Notebook March 24 by Stephen Wolfram, https://www.wolframcloud.com/obj/s.wolfram/Published/COVID-19-Livestream-March-24.nb, Agent-Based Networks Models for COVID-19 by Christopher Wolfram, https://community.wolfram.com/groups/-/m/t/1907703, Epidemiological Models for Influenza and COVID-19 by Robert Nachbar, https://community.wolfram.com/groups/-/m/t/1896178, Epidemic simulation with a polygon container by Francisco Rodríguez, https://community.wolfram.com/groups/-/m/t/1901002, Distance to nearest confirmed US COVID-19 case by Chip Hurst, https://community.wolfram.com/groups/-/m/t/1911583, Agent based epidemic simulation by Jon McLoone, https://community.wolfram.com/groups/-/m/t/1900481, Modeling the spatial spread of infection diseases in the US by Diego Zviovich, https://community.wolfram.com/groups/-/m/t/1889072, Geo-spatial-temporal COVID-19 simulations and visualizations over USA by Diego Zviovich, https://community.wolfram.com/groups/-/m/t/1900514, Stochastic Epidemiology Models with Applications to the COVID-19 by Robert Nachbar, https://community.wolfram.com/groups/-/m/t/1980051, COVID19: Italian SIRD estimates and prediction by Christos Papahristodoulou, https://community.wolfram.com/groups/-/m/t/1984320, Solver for COVID-19 epidemic model with the Caputo fractional derivatives by Alexander Trounev, https://community.wolfram.com/groups/-/m/t/1976589, Phase transition of a SIR agent-based models by Diego Zviovich, https://community.wolfram.com/groups/-/m/t/1977230, A simple estimate of covid-19 fatalities based on past data by Kay Herbert, https://community.wolfram.com/groups/-/m/t/1959438, SIR Model with Log-normal infected periods by Diego Zviovich, https://community.wolfram.com/groups/-/m/t/1946292, SEI2HR-Econ model with quarantine and supplies scenarios by Anton Antonov, https://community.wolfram.com/groups/-/m/t/1937880, COVID-19 - Policy Simulator - Can you find the perfect policy? Nevertheless, it's also a useful thing to have better short term estimates as you showed. ~ Best regards, Saar Hersonsky, The 3D models in the dashboard are STL files we grabbed from Arnoud's GitHub: https://github.com/arnoudbuzing/wolfram-coronavirus/tree/master/data-files, You can also find several models from NIH: https://3dprint.nih.gov/discover/coronavirus. And since there is no treatment nor vaccination, then quarantine becomes the only effective method of control. I was interested in this as well. We have published and are continuously updating the Wolfram Data Repository entries below. That is, a 3D plot with dimensions {x,y,z} = {cases, age, all underlying conditions}. This is a graph I am finding useful. @Robert Rimmer, nice observation! As more information becomes available regarding COVID-19, we urge you to take advantage of Wolfram Community’s ever-expanding coronavirus resource hub. I've wrangled the Google mobility data of USA and Canada. The log plot of cumulative cases from the JHU data is still showing exponential growth. We're doing a variety of livestreams daily, on a variety of platforms. On the other hand, if one considers the population of 65-yrs old and over, the case-fatality ratio shows some counterintuitive features, e.g., it decreases by this population, which is not what one expected (in fact, one may expect the reserve). COVID-19 data dashboard. Central infrastructure for Wolfram's cloud products & services. Here are a couple of other ideas. I attach some pictures from the post. Note the data source is non-official. Asthma 3. Perhaps a mutation? Or is the effect present in the true numbers? When they work, that is. Here’s a sampling of the neat projects and activities that have been produced. The lack of certainty is significantly due to the difficulty in testing the entire relevant population. These countries (or even regions of the same country) are too far to influence each other directly. Revolutionary knowledge-based programming language. Starting with the basic susceptible–infected–recovered (SIR) model, Robert introduces additional parameters to build more accurate models for regional COVID-19 outbreaks. Wolfram employees and users have produced several coronavirus-related livestreams, with more to come. Knowledge-based broadly deployed natural language. Thanks for the original work and for sharing your model. In addition to using a only straightforward differential equation, it requires but few hypotheses and no fitting of parameters (all values come from existing data). Not sure about (1), but I passed your post to our team. So I wanted to see if the lower population areas in the US are catching up to the denser areas. Thus the only way to break the spread is effective quarantine. In the post there are models for several countries, including China, USA, Austria, Finland, France, Germany, Italy, Spain, and the UK. The preeminent environment for any technical workflows. I attach a notebook that shows how the data can be sucked in from the Google Sheet and turned into a Wolfram Language Dataset. Featured. (The code is rather messy, but I'll also be publishing a cleaned-up notebook with some sample code for creating similar elements.). Any idea how many tests they are doing each day now?