In this post, I discussed how to get computable population density maps (for any desired shape and size of grid tiles) from Facebook datasets (claimed to be the most detailed population density map available anywhere). I think the solution to the epidemiological modeling is to use the logistic model which only assumes quarantine first. I suspect the problem is early data collection. I attach a notebook with the simplest version of the model that needs to be updated but is fully functional. listed above under epidemic modeling, I discuss how to fit an SEIR-like model to the coronavirus data. Thought the folks on this thread may want to join :) Jan Brugard of Wolfram MathCore provides a comparison of data from several countries to determine how early reactions to the outbreak can help “flatten the curve” and reduce the spread of COVID-19. The models are updated on a daily basis. You can now get the COVID19 data at the US County level from WDR. Folks have presented computations comparing the spread in different countries, analyzing the SARS-CoV-2 genome and explaining mathematical models for tracking epidemics. The epidemiological models are useful for understanding different levels of dynamics, but it is hard to get all the rates right until the epidemic is over. Dark Capital. I used your path and could not figure out why I failed...Indeed, I dowloaded and use Import on my local machine. Szabolcs Horvát's case counts curves show that most countries have yet to gear up their testing and the rate of cases discovered remains steep. As a California resident I find it very interesting that the projected total_death_mean for August 4 has dropped from 6,108 (projected on 3/27/2020) to 1,611 (projected on 4/7/2020). Looking at the logistic model, early on in the epidemic there is little evidence that quarantine is working, until it fully kicks in, then control should be rapid. Instant deployment across cloud, desktop, mobile, and more. 30 Under 30 2020. I do know that people have been tested in areas with no confirmed cases. It relies on data abstracted by a human (a work study student at the University of Houston operating under my supervision) from the daily reports being produced by the World Health Organization. it is paramount the I can trust the data source, especially in this Age of Misinformation. That might be a big assumption for Italy where the death rate seems too high. That report didn't mention any imported cases. This collection currently includes estimated COVID-19 cases from around the world, nucleotide sequences for the SARS-CoV-2 virus and medical records of infected patients. I've analyzed the data disparity of Iran (case-fatality ratio) and predicted the number of diagnosed cases. Attn: Szabolcs Horvát <--- Robert, please forward, Thanks, Sam Daniel. The hierarchical clustering of viruses identifies Bat coronavirus RaTG13 as the most-likely culprit of COVID-19. Technology-enabling science of the computational universe. https://community.wolfram.com/groups/-/m/t/1996374. Here is a call to action with some recommendations for people who want to do more, whether it's just pointing out relevant data sources, or taking the time to make some of that data computable and more instantly ready for other people to explore: https://wolfr.am/COVID-19-DATA . I also get the warning: FittedModel::constr: The property values {ParameterConfidenceIntervalTable} assume an unconstrained model. But I can't get anything later than March 4: It is supposed to update automatically. Asthma 3. Stay well, Computational Explorations with COVID-19 Data, Join us for a free webinar on Wolfram data resources for COVID-19, showcasing computational analyses and visualizations relating to the pandemic. In the post "An SEIR like model that fits the coronavirus infection data". Make sure the option box is checked in Profile => Email Notifications => "There is a new post in one of your groups". https://register.gotowebinar.com/register/8545942438021315596?source=community, There's been so much great work from the Wolfram Community around this topic, and more information and data sets emerging every day than we can reasonably expect to digest within the company — I added a post at. by Jan Brugard, https://community.wolfram.com/groups/-/m/t/1911422, Basic experiments workflow for simple epidemiological models by Anton Antonov, https://community.wolfram.com/groups/-/m/t/1895675, Scaling of epidemiology models with multi-site compartments by Anton Antonov, https://community.wolfram.com/groups/-/m/t/1897377, WirVsVirus 2020 hackathon participation by Anton Antonov, https://community.wolfram.com/groups/-/m/t/1907256, An SEIR like model that fits the coronavirus infection data by Enrique Garcia Moreno, https://community.wolfram.com/groups/-/m/t/1888335, COVID-19 pandemic data in Italy by Riccardo Fantoni, https://community.wolfram.com/groups/-/m/t/1909687, Predicting Coronavirus Epidemic in United States by Robert Rimmer, https://community.wolfram.com/groups/-/m/t/1906954, Tracking Coronavirus Testing in the United States by Robert Rimmer, https://community.wolfram.com/groups/-/m/t/1902302, Logistic Model for Quarantine Controlled Epidemics by Robert Rimmer, https://community.wolfram.com/groups/-/m/t/1900530, Updated: coronavirus logistic growth model: China by Robert Rimmer, https://community.wolfram.com/groups/-/m/t/1890271, Coronavirus logistic growth model: China by Robert Rimmer, https://community.wolfram.com/groups/-/m/t/1887435, Coronavirus logistic growth model: Italy and South Korea by Robert Rimmer, https://community.wolfram.com/groups/-/m/t/1887823, Coronavirus logistic growth model: South Korea by Robert Rimmer, https://community.wolfram.com/groups/-/m/t/1894561, Analyzing Nextstrain Data with WFR Newick Functions (COVID-19/SARS-CoV-2) by John Cassel, https://community.wolfram.com/groups/-/m/t/1958952, Estimating the number of times the SARS CoV-2 virus has replicated by Carlos Munoz, https://community.wolfram.com/groups/-/m/t/1943243, Genome analysis and the SARS-nCoV-2 by Daniel Lichtblau, https://community.wolfram.com/groups/-/m/t/1874816, Visualizing Sequence Alignments from the COVID-19 by Jessica Shi, https://community.wolfram.com/groups/-/m/t/1875352, A walk-through of the SARS-CoV-2 nucleotide Wolfram resource by John Cassel, https://community.wolfram.com/groups/-/m/t/1887456, Geometrical analysis of genome for COVID-19 vs SARS-like viruses by Mads Bahrami, https://community.wolfram.com/groups/-/m/t/1878824, Chaos Game For Clustering of Novel Coronavirus COVID-19 by Mads Bahrami, https://community.wolfram.com/groups/-/m/t/1875994, COVID-19 - The Swedish Experiment - Is it working? John explains the creation of the curated genetic sequences dataset and demonstrates several use cases. Additionally, the case-fatality ratio increases slightly with the median age, as expected. Thanks for the original work and for sharing your model. Instant deployment across cloud, desktop, mobile, and more. I have published 2 notebooks on the Wolfram Could which uses a logistic growth model to track the coronavirus epidemic with the data from the GitHub repository: In case it's not covered in data resources in OP, here is a history data source someone crawled from Ding Xiang Yuan (DXY), down to every cities of every provinces in China.