There is also raw data being collected here in the form of a Google Sheet. 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. I attach a notebook that shows how the data can be sucked in from the Google Sheet and turned into a Wolfram Language Dataset. From there, I run a few basic queries.

Please see notebook in Wolfram Cloud or attached below.

Attachments:

2021-06-04T14_16...nb

I just wanted to note for anyone who might be interested that the latest release of IGraph/M from a few days ago now exposes the igraph C library's SIR modelling functionality. It is fairly simple at the moment. It can run several simultaneous stochastic SIR simulations on a network, and only returns the S, I, R values at each timestep (not individual node states). It can be used to study the effect of network structure on the spreading.

http://szhorvat.net/mathematica/IGDocumentation/#epidemic-models

UPDATE: I just added another example to the documentation to clarify what this functionality is good for. If you've opened the above link before, please do a hard-refresh of the page (Shift-F5 on Linux/Windows or Command-Shift-R on Mac)

I have compiled some of the work done so far into a compact cloud dashboard: https://wolfr.am/COVID19Dashboard

It is mainly built to give an overview of some information from our WDR resources, with corresponding daily updates. It is still a work in progress; I will be adding more visualizations and interactivity in the coming days. (The code is rather messy, but I'll also be publishing a cleaned-up notebook with some sample code for creating similar elements.)

Aside from the visual elements, folks here might find the "Resources" tab helpful. It includes several of the Wolfram resources listed here, but also has some external resources I've seen floating around in several threads about the outbreak. I'll be continuously adding to that section as well.

Feel free to comment if you think of anything you'd like to see added! (Or if you see something that isn't working--e.g. the tooltips for the world map, which I'm looking to fix.)

Enjoy!

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.

COVID-19/2019-nCoV Infection Data Realtime Crawler

https://github.com/BlankerL/DXY-COVID-19-Crawler/blob/master/README.en.md

Note the data source is non-official. DXY, as I know it, is an online non-gov society of doctors and nurses from mainland china. Their data could be different from officially published one.

Dear @Hee-Young,

Not sure about (1), but I passed your post to our team. It’s an interesting question. I do know that people have been tested in areas with no confirmed cases. Finding out more about that could give an interesting look at the effectiveness of containment/prevention in those areas.

For (2), the source data gives the region information, which is actually the mixture of AdministrativeDivision, City, County as well as Air force base location. In the latest Wolfram Data Repository (WDR) item, we have the AdministrativeDivision column as well as more specific location (which gives the city or country information). So far most of the cities are for the US but I see some Canadian cities as well so it looks like there is possibility that more city information (outside the US) will be added in the future. Also note that the dataset has GeoPosition column, which gives more details and was used to create this additional example with geo bubbles (details at WDR):

Show[GeoRegionValuePlot[
  Normal@ResourceData["Epidemic Data for Novel Coronavirus COVID-19"][
     Select[MatchQ[Entity["Country", "UnitedStates"], #Country] &]][
    GroupBy["AdministrativeDivision"], Total, #ConfirmedCases["LastValue"] &]], GeoBubbleChart[
  Normal@ResourceData["Epidemic Data for Novel Coronavirus COVID-19"][
     Select[MatchQ[Entity["Country", "UnitedStates"], #Country] && ! MissingQ[#AdministrativeDivision] &]][
    All, {#GeoPosition, #ConfirmedCases["LastValue"]} &], ChartStyle -> ColorData[8, 3]]]

Update: I made a livestream recording on Twitch, related to data analysis techniques for the coronavirus in the Wolfram Language: https://www.twitch.tv/videos/547321229

Hi Vitaliy, thank you for answering. I can see that the dataset now has a link to source and metadata information! Fantastic!

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:

• Coronavirus logistic growth model: China
• Coronavirus logistic growth model: Italy and South Korea
• Updated: coronavirus logistic growth model: China

Here are a couple of other ideas. The case data for Italy is starting to converge, so you could use the L from that and estimate that the deaths stay a relatively stable ratio to cases. That might be a big assumption for Italy where the death rate seems too high. You can also track parameter convergence--this is dramatically convincing for South Korea. Code for these functions using case data for Italy and South Korea are in the attached files as well as code to fit to the differential equation, from the first derivative of the logistic function. Rather than using interpolation to get the derivatives you could manually draw approximate ideal slope lines to the data to get an estimate for k and L from the equation for the parabola. Also when the log plot starts to show downward concavity, use only the most recent points which will be in the logistic phase.

Attachments:

Italy.nb

South Korea.nb

Interesting GiF animation by Eric Mockensturm: Maps for Visualizing Covid-19's Effect

https://community.wolfram.com/groups/-/m/t/1934457

Dear Vitaliy and many other experts,

1. Is it possible to compile the data for the number of testing? So that we can get the ratio of the confirmed cases relative to those who get tested?
2. Is it also possible to redesign the data set to include the 'City', in addition to the current geographical classification, namely, country/region and administrative region? That way, perhaps we can get more detailed information about the containment and spread of the COVID-19 inside and outside of the city?

Best,

Hi, Yes, you can see them on our YouTube channel here: https://www.youtube.com/user/WolframResearch/videos With novel coronavirus specific videos here: https://wolfr.am/LaDVTtsu

I'm not really sure what you mean by Twitch videos disappearing? Twitch removes past broadcasts after 30 days, so we also upload the video to Twitch.

Here's a heat map showing distance to the nearest confirmed COVID-19 case: https://community.wolfram.com/groups/-/m/t/1911583

Very neat, thanks for sharing!

I've analyzed the data disparity of Iran (case-fatality ratio) and predicted the number of diagnosed cases. Interestingly (or sadly), it was confirmed by new data. I welcome any suggestion on how to normalize data or better approaches to tackle this issue.

Notebook for the South Korea JHU CSSE data in case that helps: https://community.wolfram.com/groups/-/m/t/1894561

How can we update to the latest version of a resource object?

Here it says, "Updated: 8 March 2020".

But I can't get anything later than March 4:

Am I doing something wrong?

Robert:

Thank you so much - that was a very helpful discussion, and I will go through the other discussion thread that you linked to.

The one problem with choosing to fit to an exponential model (because the outbreak is in its exponential phase), is that it eliminates L as a parameter, and I am interested in that value. Nevertheless, it's also a useful thing to have better short term estimates as you showed.

If I insist that my model also produces a value for L, I must accept that the estimate of this value will be poor while the outbreak is in its exponential phase. And that makes sense. I had hoped that a constraint might add some extra information to the model, but apparently this damages the parameter estimation. I assume L estimation gets better when the phase reaches its midpoint.

Thanks for your very helpful demonstration!

Steve Rector

Why is this surprising? The rate of spread of the infection depends upon the virus and quarantine methods, which have been known for centuries. The virus doesn't discriminate by nationality, and all European countries should know basic epidemiology. The one day difference, could simply be Roche shipping more test kits on the same day.

South Korea, the outlier, got a lucky break all their index cases belonged to a religious group that had visited Wuhan together. The cases were all known instantly and easy to isolate.

Could you double check the date in your post. March 13 is in the past and was not a Tuesday

Another stream today! This one featuring Robert Nachbar discussing Epidemiological Models for Influenza and COVID-19. Will be livestreamed at 3pm EST on https://twitch.tv/wolfram

Computational Explorations with COVID-19 Data

Wed, Apr 1, 2020 10:00 AM - 11:00 AM PDT

Join us for a free webinar on Wolfram data resources for COVID-19, showcasing computational analyses and visualizations relating to the pandemic. https://register.gotowebinar.com/register/8545942438021315596?source=community

Dear All, thank you very much for your comments and astonishing contributions. Here are a couple of things that may be useful to you.

• Check the "Computational Publications" section in the head post above. It is continuously updated and now features a stunning number of articles from you and your fellow community members. Our deepest gratitude for that!

• When you publish a new coronavirus-relevant article on Wolfram Community, please make sure you also come here and make an announcement comment with the article's URL link and it's summary so everyone following this thread is aware.

• Join the group Medical Sciences: https://wolfr.am/MedicalSciences to get automatic email notifications when new articles are added in that group. Make sure the option box is checked in Profile => Email Notifications => "There is a new post in one of your groups".

• Join our Staff Picks group http://wolfr.am/StaffPicks to get automatic email notifications when the best content is awarded Staff Picks recognition.

• To share this discussion with friends and colleagues use short URL: https://wolfr.am/coronavirus

• Control notifications from this forum via settings in Profile => Email Notifications

• Please continue contributing your wonderful ideas and encourage others.

Again - huge thanks!

Hi, here is an attempt to calculate the number of times the virus has replicated since it's "origin":

https://community.wolfram.com/groups/-/m/t/1943243

I've wrangled the Google mobility data of USA and Canada. For example, here is the visualization for LA county, California

Please find a post I just shared about a SEIRD Compartmental Model for the COVID-19 pandemic using Delayed Differential Equations following the link below. Data Fits for Austria, Germany, Iceland, Israel, New Zealand, Switzerland, and South Korea are shown.

https://community.wolfram.com/groups/-/m/t/1996374

Thank you very much. I am also compiling some data for South Korea (where my parents are living). Once I am done, let me send you (share with other experts) the data set.

Thanks, @Avery Davis, but soome Twitch videos seem to disappear? Are there more stable links like YouTube?

Yes, GitHub can be a bit tricky on that. To get the direct link, you can browse to the object (e.g. https://github.com/arnoudbuzing/wolfram-coronavirus/blob/master/data-files/QHD43415_1-putative-protease_cleaned-ribbon.stl ) and copy the link address from the "Download" button.

In Chrome, this is simply a left/alt click followed by "Copy Link Address":

Alternatively, you could just click "Download" to download the STL file and import it from your local machine.

Thank you for gathering in one place all these COVID19 resources from Wolfram Community!

I've just added a new version of my map at Confirmed COVID-19 Cases in Catalonia.

Map of March's total cases with Tooltip info displayed for each municipality: www.wolframcloud.com/obj/bernate/covidcat

What Is Going on in South Korea?

A country which controlled exponential spread of the virus more quickly than any other country in the world, now seems to be in a phase of linear growth of cases at about 100 per day. Linear growth should have been easy to control over several mean incubation periods. Something else must be going on. Perhaps a mutation? Persistent carrier spread with low transmissibility? Given the realities of medical testing, a significant false positive rate is another possibility, which might also explain the low death rate. Any other ideas????

But that model implies an odd method of quarantine. The population of South Korea is over 50 million. The model you show takes 17000 people, presumably infected and contacts, and puts them into a camp away from the 50 million population and then lets the infection run its course. Ideally a good quarantine model should keep initially healthy people at risk away from known infected people, with the goal that many of the people at risk will not become infected because of the quarantine. With effective quarantine new cases should stop within the few incubation periods it takes to trace the contacts.

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. Since coronavirus is assumed to be new to the human race, almost everybody should be susceptible. And since there is no treatment nor vaccination, then quarantine becomes the only effective method of control. The logistic model only requires that at some point quarantine becomes effective. At the outset, the infection can spread exponentially because almost everybody is susceptible, the exponential phase can go to complete diffusion because of easy international travel so a few hundreds of infected people can seed most of the world. Thus the only way to break the spread is effective quarantine. And the spread of quarantine methodology has to be faster than the spread of the infection. So with coronavirus now that there is effective testing to enhance quarantine, the quarantine method should halt the spread. 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. This is a graph I am finding useful. It is for the state of New York. The orange dots are the differences of the logarithms of the daily total cases. Thus it is a continuous growth rate. If growth were exponential it would be a horizontal line. And at the outset there is no pattern to the daily rates--they could be fit to any model, The blue dots are the course of daily rates predicted by the logistic model using all the orange dots for the fit. There is apparent convergence to the logistic model as the epidemic progresses. The default fitting method using Norm also favors the more recent points. This is a log plot of the cumulative cases in orange fit to the logistic model in blue. Since the squares of the later much higher values dominate, the early data are effectively ignored.

But in this case ignoring the early data is a good thing because early on there was not adequate testing and only the most obvious cases could be discovered. Since the counts are cumulative, cases which are eventually found end up in the cumulative totals, and it only takes three points with accurate information to fit the curve.

Since the quarantine effect can easily dominate, by spread of information almost instantly, quarantine could potentially break the epidemic in a maximum incubation period. 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.

I think the solution to the epidemiological modeling is to use the logistic model which only assumes quarantine first. Once the logistic model kicks in, it should give an accurate prediction of the susceptible population, as its limiting population, L. The rate parameter, k, is at the dashed line in the first graph, and this is likely a good estimate of the maximum exponential rate of spread for the virus. See Logistic Model for Quarantine Controlled Epidemics

Coincidentally, I just took a stab at putting this into a data repository item: https://datarepository.wolframcloud.com/resources/COVID-19-Hospital-Resource-Use-Projections

DateListPlot[
 ResourceData["COVID-19 Hospital Resource Use Projections"][
  Entity["AdministrativeDivision", {"California", 
    "UnitedStates"}], {"DeathsMean", "DeathsLower", "DeathsUpper"}], 
 PlotRange -> All]

I'd be really interested to see what people can do with this (suggestions of more sophisticated examples for the WDR page would be most welcome).

I posted "Computing COVID-19 Spread Rates in US Cities" here: https://community.wolfram.com/groups/-/m/t/1930261

I used the daily tabulated infections data from the New York Times site to quantify how well cities are faring in fighting the spread of the disease. I work with a certain ratio, based only on numbers of reported cases, that is more easily computed than R0 or related values from the epidemiology literature.

Americans go to work on weekends during lockdown!!! of course, not folks from Nevada!

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). It can be useful for modeling the spread of infectious diseases (e.g., contact rate and etc).

Qatar as an example:

Some simple visualizations:

100 Days of COVID19 Over US Counties

COVID-19 reopening criterion

Thought I'd share a couple resources here:

• COVID-19 Data Analysis and Visualization Video Course
• A Neat Package for Compartmental Model Diagrams Community Post

I hope these are helpful!

Many thanks for this.

Thanks for the response. Yes, you are correct. I finally noticed too. I kept staring at Germany mostly, which did have a small increase, which is part of the reason why I was confused.

There was some news last night that the new cases had dropped to 47 from 81 the day before. That report didn't mention any imported cases. The pattern to suddenly have a long stretch of linear growth doesn't really fit any of the models.

Are there data of deaths due to COVID-19 over time and age, separated by individuals' heath conditions, such as: 1. None 2. Asthma 3. Pulmonary 4. Heart and combinations thereof. Also, in view of Dr. Oz's observation that many thousands Lupus patients and others taking Hydroxychloquine, it would be very informative to determine their vulnerability to the virus collectively, or compartmentalized by other heath conditions as stated above. This granularity may add to a meaningful analysis and prognosis for a given population and enable improved measures to recommend activities at commensurate levels with respect to other associated imperatives.

Yesterday the John Hopkins data for the US was about 1000 cases too low but the last two digits 68 matched the worldometer numbers (I didn't track when it was corrected). There must be a lot of human intervention to produce the numbers so they can be misleading.

Thanks Seth!

Hey Brian,

Thank you so much for the information! I was able to Import both files with the full path you kindly provided in your reply:

Import["https://github.com/arnoudbuzing/wolfram-coronavirus/raw/master/data-files/QHD434151-putative-proteasecleaned-ribbon.stl"].

Could you kindly explain how you got the correct path above? I am not sure how the "raw" (just before the /data-files/) shows up in your message. It definitely does not show up when I use "copy path" appearing in my browser and therefore the Import did not work.

Best regards, Saar

Is it possible to compile the data for the number of testing? So that we can get the ratio of the confirmed cases relative to those who get tested?

Three weeks have passed and I am curious if anyone has found any testing data for any other countries than the US.

I am looking for resources that would help us determine if the case numbers we see reflect true cases, or are at this point mostly bottlenecked by testing capacity.

There is testing data here:

https://ourworldindata.org/covid-testing https://ourworldindata.org/coronavirus-testing-source-data

But these are the total number of tests performed. I am looking for more granular day-by-day (or any longer time period by time period) data.

https://community.wolfram.com/groups/-/m/t/1930697

A resource by Daniel Lichtblau was added:

From sequenced SARS-CoV-2 genomes to a phylogenetic tree
https://community.wolfram.com/groups/-/m/t/1961461

A resource by Jessica Shi was added:

TraCOV: Personalized COVID-19 Risk Analysis Tool
https://community.wolfram.com/groups/-/m/t/1977700