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⭐ [COVID] Computational Research HUB For Novel Coronavirus: Data, Code, Visualizations, Notebooks

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This post is intended to be the hub for Wolfram resources related to novel coronavirus disease COVID-19 that originated in Wuhan, China. The larger aim is to provide a forum for disseminating ways in which Wolfram technologies and coding can be utilized to shed light on the virus and pandemic. Possibilities include using the Wolfram Language for data-mining, modeling, analysis, visualizations, and so forth. Among other things, we encourage comments and feedback on these resources. Please note that this is intended for technical analysis and discussion supported by computation. Aspects outside this scope and better suited for different forums should be avoided. Thank you for your contribution!




CALL for Making COVID-19 Data Computable (link)

More pandemic-related information and data sets emerging every day. We invite people in the community to contribute to making more data surrounding this topic computable. 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: .


Curated Computable Data (link)

FOLLOW THIS LINK to see all available COVID-19 data repositories ready for computation in the Wolfram Language .

Changes in Updates to SARS-CoV-2 Sequences in the Wolfram Data Repository

We have published and are continuously updating the Wolfram Data Repository entries. Below are a few key ones. Follow the link above to browse all repositories. We encourage you to make your own contributions of curated data relevant to COVID-19.

Pandemic Data for Novel Coronavirus COVID-19

Genetic Sequences for the SARS-CoV-2 Coronavirus

Patient Medical Data for Novel Coronavirus COVID-19

COVID-19 Hospital Resource Use Projections

OECD Data: Hospital Beds Per Country

Hospital Beds Per US State


Computational Publications (link)

We encourage you to share your computational explorations relevant to coronavirus on Wolfram Community as stand-alone articles and then comment with their URL links on this discussion thread. We will summarize these articles in the following list:



COVID-19 Livestream Notebook March 24 by Stephen Wolfram

Agent-Based Networks Models for COVID-19 by Christopher Wolfram

Epidemiological Models for Influenza and COVID-19 by Robert Nachbar

Epidemic simulation with a polygon container by Francisco Rodríguez

Distance to nearest confirmed US COVID-19 case by Chip Hurst



Epidemic simulation with a polygon container by Francisco Rodríguez

Agent based epidemic simulation by Jon McLoone

Modeling the spatial spread of infection diseases in the US by Diego Zviovich

Geo-spatial-temporal COVID-19 simulations and visualizations over USA by Diego Zviovich

Life, Liberty, and Lockdowns: cellular automaton approach by Philip Maymin



Teaching notebook on disease models by Gareth Russell

Stochastic Epidemiology Models with Applications to the COVID-19 by Robert Nachbar

COVID19: Italian SIRD estimates and prediction by Christos Papahristodoulou

Solver for COVID-19 epidemic model with the Caputo fractional derivatives by Alexander Trounev

EpiPlay: using Mathematica to gamify education in epidemiology by Rui Alves

Epidemiological Model for repetitive rapid testing for COVID-19 by Diego Zviovich

Phase transition of a SIR agent-based models by Diego Zviovich

A simple estimate of covid-19 fatalities based on past data by Kay Herbert

SIR Model with Log-normal infected periods by Diego Zviovich

SEI2HR-Econ model with quarantine and supplies scenarios by Anton Antonov

COVID-19 - Policy Simulator - Can you find the perfect policy? by Jan Brugard

Epidemiological Models for Influenza and COVID-19 by Robert Nachbar

Exploring Epidemiological Modeling by Jordan Hasler

SEI2HR model with quarantine scenarios by Anton Antonov

The SIR Model for Spread of Disease by Arnoud Buzing

COVID-19 - R0 and Herd Immunity - are we getting closer? by Jan Brugard

Basic experiments workflow for simple epidemiological models by Anton Antonov

Scaling of epidemiology models with multi-site compartments by Anton Antonov

WirVsVirus 2020 hackathon participation by Anton Antonov

An SEIR like model that fits the coronavirus infection data by Enrique Garcia Moreno

A SEIRD Model For COVID-19 Using DDEs by Luis Borgonovo

A Neat Package for Compartmental Model Diagrams by Hamza Alsamraee

Redesign of didactics of S(E)IR(D) -> SI(EY)A(CD) models of epidemics by Thomas Colignatus

COVID-19 SIR models: transmission, vaccination, herd immunity dynamics revealed by Athanasios Paraskevopoulos



COVID-19 pandemic data in Italy by Riccardo Fantoni

Predicting Coronavirus Epidemic in United States by Robert Rimmer

Tracking Coronavirus Testing in the United States by Robert Rimmer

Logistic Model for Quarantine Controlled Epidemics by Robert Rimmer

Updated: coronavirus logistic growth model: China by Robert Rimmer

Coronavirus logistic growth model: China by Robert Rimmer

Coronavirus logistic growth model: Italy and South Korea by Robert Rimmer

Coronavirus logistic growth model: South Korea by Robert Rimmer

Logistic growth model for epidemic Covid-19 in Colombia by Diego Ramos



Analyzing the spread of SARS-CoV-2 variants in California by Daniel Lichtblau

Analyzing the spread of SARS-CoV-2 variants in Florida by Daniel Lichtblau

Analyzing Nextstrain Data with WFR Newick Functions (COVID-19/SARS-CoV-2) by John Cassel

Finding and analyzing a COVID subvariant in Australia by Daniel Lichtblau

Analyzing SARS-CoV-2 Genetic Sequences by John Cassel & Daniel Lichtblau

Estimating the number of times the SARS CoV-2 virus has replicated by Carlos Munoz

From sequenced SARS-CoV-2 genomes to a phylogenetic tree by Daniel Lichtblau

Genome analysis and the SARS-nCoV-2 by Daniel Lichtblau

Visualizing Sequence Alignments from the COVID-19 by Jessica Shi

A walk-through of the SARS-CoV-2 nucleotide Wolfram resource by John Cassel

Geometrical analysis of genome for COVID-19 vs SARS-like viruses by Mads Bahrami

Chaos Game For Clustering of Novel Coronavirus COVID-19 by Mads Bahrami



Detecting Global Community Structure in a COVID-19 Activity Correlation Network by Hiroki Sayama

Analyzing trends of COVID-19 through public news feeds by Silvia Hao

Deep neural network detection & clinical staging of COVID-19 chest X-rays by Peter Riley

COVID-19 - The Swedish Experiment - Is it working? by Jan Brugard

A simple COVID-19 spread model by Daniel Lichtblau

COVID19: The performance of the Swedish strategy by Christos Papahristodoulou

Exploring social trends on Covid-19 pandemic using WikipediaData by Jofre Espigule-Pons

Google Mobility Data by Mads Bahrami

Understanding Aggregate COVID Curves by Christopher Wolfram

Apple mobility trends data visualization by Anton Antonov

Computing COVID-19 Spread Rates in US Cities by Daniel Lichtblau

COVID-19 data and the Newcomb Benford Distribution by Gustavo Delfino

Short-time trends for COVID-19, by Fabian Wenger

What countries are hit hard by COVID19 outbreak? by Mads Bahrami

COVID19 in Iran: under-diagnosis issue by Mads Bahrami

Coronavirus analysis: descriptive statistics with SQL functions by Damian Calin

Covid-19 vaccine campaigns efficacy analysis by Damian Calin

Argentina: COVID-19 Data Analysis by Tobias Canavesi

Analysis of the Change in Phillips Curve After COVID-19 with Regression by Seojin Yoon

COVID wave alert: statistical analysis and visualization by Antonio Neves

Predicting COVID-19 using cough sounds classification by Siria Sadeddin

Covid-19 vaccination data analysis using SQL functions by Damian Calin

Analyzing COVID-19 vaccine sentiment over time by Arshaan Sayed

VAERS data analysis using SQL functions by Damian Calin

Correlating COVID-19 government measures to biweekly/daily outbreaks by Arshaan Sayed

Plotting Covid19 sentiment in different regions of Chennai by Aditya Sairam Prakash



CDC COVID19 vaccination data across US counties by Mads Bahrami

Top 20 COVID countries HeatMap by absolute death and death in ppm by Rodrigo Murta

COVIDWORLD app: current data and visualizations for SARS-CoV2 pandemic by Rui Alves

US Counties COVID-19 confirmed cases by population density timelines by Bob Sandheinrich

3D Modeling of the SARS-CoV-2 Virus in the Wolfram Language by Jeff Bryant

California COVID19 Data by Mads Bahrami

COVID-19 progress in Peru macro regions: coast vs mountain vs jungle by Francisco Rodríguez

COVID-19 reopening criterion: a simple visualization by Mads Bahrami

100 Days of COVID19 Over US Counties by Mads Bahrami

Population Density Map by Mads Bahrami

Google Mobility Data by Mads Bahrami

COVID19 Case-Fatality Ratio, Income, and Age: Simple Visualization by Mads Bahrami

Data Analysis of Coronavirus in Mexico by Ivan Martinez

Confirmed COVID-19 Cases in Catalonia by Bernat Espigulé Pons

Distance to nearest confirmed US COVID-19 case by Chip Hurst

COVID19 Confirmed Cases: US Counties by Mads Bahrami

COVID19 data visualization across US counties by Mads Bahrami

Maps for Visualizing Covid-19's Effect by Eric Mockensturm

US Counties COVID-19 deaths plot by Bob Sandheinrich

Comparing the spread of COVID-19 between countries, Jan Brugard

NY Times COVID-19 data visualization by Anton Antonov

COVID-19 cases for each administrative division in Spain by Bernat Espigulé Pons

Propagation risk of COVID-19 by local contact in Spain (10 - 14 March) by Bernat Espigulé Pons

Visualizing the Pandemic Data COVID-19 by Martijn Froeling

COVID-19 visualization of turning point by Isao Maruyama

Mapping "Live" COVID Data on a Globe by Gabriel Lemieux

Novel Coronavirus COVID-19 in Brazil by Estevao Teixeira

Mapping Novel Coronavirus COVID-19 Outbreak by Jofre Espigule-Pons

Ways to visualize COVID-19 simulation results? by Kyle Keane

General and COVID-19 deaths in Sweden by Oscar Rodriguez

COVID19 Tokyo per days of the week Isao Maruyama



Cov-Tell: Daily COVID-19 Updates with Alexa (made with Wolfram APIFunction) by Jessica Shi

Build a COVID-19 Chest X-Ray Image Uploader with Cloud & Data Drop by Jofre Espigule-Pons

Scraping OpenTable's "State of the Industry" page by Aaron Enright

City-level Search Tool for Coronavirus (COVID-19) Confirmed Cases by David Lomiashvili

Web Scraper: New York Times Coronavirus Data by Robert Rimmer

TraCOV: Personalized COVID-19 Risk Analysis Tool by Jessica Shi

Mobility changes data: transforming to Wolfram Language dataset by Mads Bahrami



Effect of mandatory mask usage in COVID cases by Diego Zviovich

Face mask detection: classifying image data by Siria Sadeddin


Livestream Archives (link)


Other useful resources

POSTED BY: Vitaliy Kaurov
138 Replies
POSTED BY: Arnoud Buzing

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.

POSTED BY: Seth Chandler

I did a simple chart how 2019-nCoV aligns against SARS, MERS. Here results and source code.

2019-nCoV vs SARS, MERS

ChartLabels->{Placed[{"2019-nCoV","SARS","MERS","Avian Flu"},{{0.5,0},{0.8,1.2}},Rotate[#,(1.75/7) Pi]&],Placed[{"",""},Above]},
LabelingFunction->(Placed[Rotate[#,0 Pi],If[#1>1,Center,Above]]&),
ChartLegends->Placed [{"Infections","Fatalities"},Right],
PlotLabel->Style["2019-nCoV Infections",FontFamily->"Helvetica",Thin,24],
POSTED BY: Stefan Parvu

Very neat, thanks for sharing!

POSTED BY: Arnoud Buzing
Posted 4 years ago

It would be neat to see a SEIR type analysis

POSTED BY: Renay Oshop

Update: I made a livestream recording on Twitch, related to data analysis techniques for the coronavirus in the Wolfram Language:

POSTED BY: Arnoud Buzing

It is very nice to see how fast Wolfram Inc is moving in gathering and curating data on the corona virus outbreak. Thank you very much!

Still, for me to use, e.g. the

ResourceObject["Patient Medical Data for Novel Coronavirus 2019-nCoV from Wuhan, China"]

it is paramount the I can trust the data source, especially in this Age of Misinformation. You give a name and a link to a Google Sheet, but who is behind that? Which organization? How have you curated that specific data set?

Best, Per Møldrup-Dalum

Many thanks for this.

POSTED BY: Bernardine Wong

Hi @Per Møldrup-Dalum, I am glad you like our resources and we highly appreciate user feedback, thank you! For this specific type of question I recommend reaching out directly to our Wolfram Data Repository team at: Please note, Wolfram Data Repository entries are continuously updated and new information can appear on their pages in the future.

POSTED BY: Vitaliy Kaurov

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

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.

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)

POSTED BY: Szabolcs Horvát

Extremely interesting. Thanks for the original work and for sharing your model.

POSTED BY: Seth Chandler

I have a new notebook titled 'china-province-graph.nb' here:

It contains the 'bordering provinces graph' (not a built-in dataset).

enter image description here

Might be useful with your IGraph package?

POSTED BY: Arnoud Buzing

I have compiled some of the work done so far into a compact cloud dashboard:

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.)


POSTED BY: Brian Wood

I have studied the genetic sequences of COVID-19 and SARS-like viruses, using Chaos Game Representation and Z-curve methods (hyperlinks to my Community posts). Z-curves provide a fascinating visualization of genomes that helps a lot for classification and clustering. The hierarchical clustering of viruses identifies Bat coronavirus RaTG13 as the most-likely culprit of COVID-19. My results strongly support the hypothesis of a Bat origin of COVID-19. I appreciate any comment or feedback :-)

POSTED BY: Mads Bahrami
Posted 4 years ago

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:

POSTED BY: Robert Rimmer

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

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.

POSTED BY: Silvia Hao

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.

POSTED BY: Mads Bahrami
Posted 4 years ago

I suspect the problem is early data collection. Iran probably does not have the resources to detect whether all exposed cases have become infected. Thus the cumulative case data will lag the actual cases until the backlog of cases in the community is discovered. The log plot of cumulative cases from the JHU data is still showing exponential growth.


When quarantine measures start to work or susceptible population significantly declines, the graph should start to show growth slower than exponential. Until that happens it won't be possible to predict the end of the epidemic.

POSTED BY: Robert Rimmer
Posted 4 years ago

Yaneer Bar Yam, from the New England Complexity Institute set a challenge for volunteers to join and coordinate efforts in several fronts to raise awareness on the coronavirus challenge.

If you join, there is a channel in slack at the workgroup that is for Mathematica users. It feels quite lonely right now (Mads and I). Additional volunteers welcomed

POSTED BY: Diego Zviovich

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?


POSTED BY: Hee-Young Shin

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):

enter image description here

  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]]]
POSTED BY: Vitaliy Kaurov

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.

POSTED BY: Hee-Young Shin
Posted 4 years ago

Notebook for the South Korea JHU CSSE data in case that helps:

POSTED BY: Robert Rimmer

Dear @Vitaliy Kaurov I would like to share the attached data for Korea, in order to help stimulate the related research. Please take a look at and feel free to use whereever necessary. There might be some errors in the spread, which I will continue to update and correct in the near future.

POSTED BY: Hee-Young Shin

Thank you @Hee-Young ! You perhaps would be interested to take a look at the work by @Yu-Sung Chang:

POSTED BY: Vitaliy Kaurov

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:

enter image description here

Am I doing something wrong?

POSTED BY: Szabolcs Horvát

It is supposed to update automatically. If it does not, you can always delete it manually with

POSTED BY: Arnoud Buzing
Posted 4 years ago


In the attached notebook, I've fitted a Logistic model to Wolfram repository data for CoV deaths from Italy through March11, with 90% confidence bands out to March 15. Since we're still in an early phase of the outbreak, the bands diverge relatively rapidly as expected.

In the NLM fit, I've added a constraint to "L" based on the a-priori information that the number of deaths cannot be less than a number close to the present value. However, when I print out the ParameterConfidenceIntervalTable, the 90% CI for L is: {-3453.81, 15896.9}, with expected value 6221. I also get the warning: FittedModel::constr: The property values {ParameterConfidenceIntervalTable} assume an unconstrained model. The results for these properties may not be valid, particularly if the fitted parameters are near a constraint boundary.

Now, I expect there to be a very wide interval for L given the early phase of the outbreak. But, the Confidence Intervals are not taking into account my a-priori information, and the warning explains it. It seems to me there must be a method where the CI of the fit parameters can take into account this a-priori info (in my case, L > 1200). Is this a missing feature in Mathematica?

Stephen Rector

POSTED BY: Steve Rector
Posted 4 years ago

Please see notebook in Wolfram Cloud or attached below.

POSTED BY: Robert Rimmer
Posted 4 years ago


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

POSTED BY: Steve Rector
Posted 4 years ago

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.

POSTED BY: Robert Rimmer

I noticed that there is a clear correlation in case counts in the last few days between European countries. Why would this be? Does anyone know if the data is normalized or post-processed in any way that could cause this effect? Or is the effect present in the true numbers?

enter image description here


  • March 13: all European countries have a bigger than usual increase
  • March 12: all of them have a smaller than usual increase
  • The non-European countries in the plot (Iran, USA, Korea) don't follow this pattern

Any opinions on this?

I can't believe it's not some data post-processing effect. That's the only thing that makes sense. Can anyone confirm this?

These countries (or even regions of the same country) are too far to influence each other directly. The pattern makes no sense in the context of weekend/weekday (why would Thursday have a smaller increase than Wednesday?)

Edited for clarity.

POSTED BY: Szabolcs Horvát
Posted 4 years ago

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.

POSTED BY: Robert Rimmer

I am not talking about all of them having an exponential growth (I thought this would be obvious, but next time I'll spell it out :-) ) I am talking about the fluctuations in the last few days, which are common to the European countries in the plot, but not to Iran, the US or Korea.

If testing is currently limited by the manufacturing of kits, and most come from the same source, then you could be right.

Another suggestion I got is that the data reporting deadline (for this particular dataset) has changed, shifting some reported cases from March 12 to March 13. This seems the most plausible to me, so far.

POSTED BY: Szabolcs Horvát
Posted 4 years ago

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.

POSTED BY: Douglas Kubler
Posted 4 years ago

Attn: Szabolcs Horvát <--- Robert, please forward, Thanks, Sam Daniel

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. In contrast, the South Korea curve is flattening out, very likely due to its aggressively and extensive testing, showing that they rate of new cases is diminishing. On the other hand, the Iran and Germany curves show large case numbers, but it appears that they have many more cases to discover. Clearly all the other curves are yet to catch up...

It would be more interesting, if the data existed, to plot case-naiver curves against age and prior health conditions. That is, a 3D plot with dimensions {x,y,z} = {cases, age, all underlying conditions}. Medical professionals might also be interested is specific underlying conditions, such as pulmonary hypertension, etc.

POSTED BY: Sam Daniel
Posted 4 years ago

Please join us in our discussion on Geo-spatial-temporal COVID-19 simulations and visualizations over USA next Tuesday March 17 5:30 PM EST. We'll take Anton's framework and apply it to create a model for the US.

enter image description here

POSTED BY: Diego Zviovich

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

POSTED BY: Seth Chandler
Posted 4 years ago

Thanks Seth!

POSTED BY: Diego Zviovich

We have a livestream planned today at 5pm EST with Anton Atonov and Diego Zviovich to explore epidemiological models and geo-spatial-temporal #COVID-19 simulations and visualizations:

Come check it out!

POSTED BY: Avery Davis

Late reply. Probably you already figured it out.

There were no updates in those days for some countries. JHU have many issues in their datasets.

caseData[[{17, 12, 201, 210, 405, 463, 32, 19, 21},    
{"Country/Region", "3/11/20", "3/12/20", "3/13/20"}]]


POSTED BY: Richard Neumann

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.

POSTED BY: Szabolcs Horvát

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

POSTED BY: Avery Davis

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

POSTED BY: Sam Carrettie

Hi, Yes, you can see them on our YouTube channel here: With novel coronavirus specific videos here:

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.

POSTED BY: Avery Davis

Hi all, Thank you so much for the insightful data and analysis!Impressive work! Could someone please share with me (notebook?) how the Ribbon and Surface model are created? ~ Best regards, Saar Hersonsky

POSTED BY: Sa'ar Hersonsky

I included the total number of hospitals per country and make a simple comparison between countries with the largest number of positive COVID19 cases.

POSTED BY: Mads Bahrami

Hey, Stephen will be doing a live exploration of some COVID-19 data this afternoon (1:30pm CST, US) on his twitch channel: and will be simulcast on the Wolfram Research Youtube Channel. Thought the folks on this thread may want to join :) Stay well

POSTED BY: Danielle Rommel

The 3D models in the dashboard are STL files we grabbed from Arnoud's GitHub:

You can also find several models from NIH:

You should be able to pull any of the models into a notebook using Import.

Ribbon model:


Surface model:

POSTED BY: Brian Wood

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.

POSTED BY: Mads Bahrami

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:


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

POSTED BY: Sa'ar Hersonsky

Yes, GitHub can be a bit tricky on that. To get the direct link, you can browse to the object (e.g. ) and copy the link address from the "Download" button.

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

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

POSTED BY: Brian Wood


I used your path and could not figure out why I failed...Indeed, I dowloaded and use Import on my local machine. Thanks so much and I will be back with more after I study the algorithms to produce these surfaces.


POSTED BY: Sa'ar Hersonsky

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

with some requests and 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 Wolfram Language users to explore.

POSTED BY: Alan Joyce

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:

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.

POSTED BY: Szabolcs Horvát

Dear Szabolcs,

I have personally compiled the various data for Korea, where you can find the estimated number of daily testing: (COVID19 Korean data Updates) I hope you can find it useful. Best,

POSTED BY: Hee-Young Shin
Posted 4 years ago

Here's a heat map showing distance to the nearest confirmed COVID-19 case:

POSTED BY: Greg Hurst

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

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Again - huge thanks!

POSTED BY: Vitaliy Kaurov

I added a post about US county-level timeline plots here:

POSTED BY: Bob Sandheinrich

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.

Confirmed Cases in Catalonia, March 2020

Map of March's total cases with Tooltip info displayed for each municipality:

POSTED BY: Bernat Espigulé

Hello everyone, I leave you information about the number of cases in Argentina and some tests of the Predict function with different methods. Greetings!

POSTED BY: Tobias Canavesi
Posted 4 years ago

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????

enter image description here

POSTED BY: Robert Rimmer

Any idea how many tests they are doing each day now? Do you know if this includes imported cases?

POSTED BY: Bob Sandheinrich
Posted 4 years ago

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.

POSTED BY: Robert Rimmer

@Robert Rimmer, nice observation! I think it fits very well with SIR model. So maybe SIR model can explains it

Please see notebook in Wolfram Cloud or attached below.

POSTED BY: Mads Bahrami
Posted 4 years ago

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.

POSTED BY: Robert Rimmer

This is an issue several of us have been struggling with. Enrique Garcia Moreno E., University of Helsinki uses an effective population size. I have found that the only way to get the model close to the data is to 1) use standard incidence, and 2) use an effective population size that is much smaller than the actual population.

The smaller smaller effective population size is justified because the assumption of complete and rapid mixing of the population does not hold. However, I would like to find an appropriate way to model the actual mixing while retaining the full population size.

A second method of reducing the effective population size is to include a quarantine compartment in equilibrium with the susceptible compartment to model the lock-down mitigation strategy used in many countries. I have not had much success with it however, most likely because the influx and efflux rates need to be time-dependent.

POSTED BY: Robert Nachbar
Posted 4 years ago

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. NewYork 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

POSTED BY: Robert Rimmer
Posted 4 years ago

Has anyone used or analyzed the popular projections and data from

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). Also the US total_death_mean has dropped from 81,000 to 61,000.

POSTED BY: Douglas Kubler

I was interested in this as well. Additionally thoughts on curve fitting to hospitalizations versus death rate.

POSTED BY: Chiara Beckner

Coincidentally, I just took a stab at putting this into a data repository item:

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

enter image description here

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).

POSTED BY: Alan Joyce

Here's an overview of some compartment based models used in epidemiological modeling:

POSTED BY: Jordan Hasler
Posted 4 years ago

I posted "Computing COVID-19 Spread Rates in US Cities" here:

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.

POSTED BY: Daniel Lichtblau

In the post "An SEIR like model that fits the coronavirus infection data"

listed above under epidemic modeling, I discuss how to fit an SEIR-like model to the coronavirus data. I argue that there is value in doing this regardless of how good the data might be in that a good fit nevertheless allows one to understand the dynamics of an outbreak and make forecasts (how many cases, how long of an outbreak). The key to being able to find a good fit for the data is to estimate initial susceptibility correctly. I argue that the main effect of a lockdown is to lower this number drastically. In the discussions of the post I provide a thought experiment which illustrates why this is so. Susceptibility thus understood is also used to illustrate the effect of lifting containment restrictions too early. In the post there are models for several countries, including China, USA, Austria, Finland, France, Germany, Italy, Spain, and the UK. There is additional material, including a pdf document with smoothened curves of daily case tallies for several countries. I attach some pictures from the post. The models are updated on a daily basis. Please visit the post for the updates, they will not be reflected in this response. I attach a notebook with the simplest version of the model that needs to be updated but is fully functional.





There has been considerable controversy in the United States (and elsewhere0 about the true incidence of COVID-19. The lack of certainty is significantly due to the difficulty in testing the entire relevant population. I've create a notebook that suggests an algebraic method for estimating the incidence in the entire population. Basically, the user inputs six facts: (1) the population of the nation in question (pop); (2) the number of tests conducted in the relevant time period (perhaps the period in which an individual may have the disease) (tested); (3) the sensitivity of the test used to detect the disease (sensitivity); (4) the specificity of the test used to detect the disease (specificity); (5) the fraction of persons with the disease who seek out testing (s1) ; and (6) the fraction of tests coming back positive (pos). We now have six equations with six unknowns, which can yield an exact solution. Comments welcome.

The default values in the Manipulate are pegged to United States data and attempt to reflect approximately the last 10 days.

Please see notebook in Wolfram Cloud or attached below.

POSTED BY: Seth Chandler
Posted 4 years ago

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.

POSTED BY: Sam Daniel

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

Change of the last two weeks.

POSTED BY: Vitaliy Kaurov

In a very short essay, I’ve explored COVID19 fatality vs population age and income over US counties. My analysis suggests that the case-fatality ratio decreases by a factor of about 2 when the median household income goes from $30,000 to $60,000. However, it shows less sensitivity to incomes of $60,000 or more. Additionally, the case-fatality ratio increases slightly with the median age, as expected. 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). The visualization of median house hold income vs population of 65yrs old and over suggests that the income increases by this population, which may imply better health care services and ultimately less fatality.

enter image description here

POSTED BY: Mads Bahrami

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

POSTED BY: Carlos Munoz

I posted a simple model for assessing new caseloads in US cities.

This builds on work from a prior post that works with an estimate of the logarithmic derivative of tested-positive counts as a function of time. The extrapolation gives what amount to testable hypotheses in the sense that it predicts near-term caseloads (so in a few weeks I get to find out whether or not I completely missed the mark).

Should this model turn out to have some predictive power, an important virtue is that it is remarkably simple. 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). Models this spare on underlying requirements are always good to have. When they work, that is.

POSTED BY: Daniel Lichtblau

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

POSTED BY: Mads Bahrami

The behavior on weekends is very interesting! It looks like people are doing their grocery and retail shopping on weekdays, and "sneak" out on weekends to the office.

POSTED BY: Robert Nachbar

Americans go to work on weekends during lockdown!!! of course, not folks from Nevada! GoogleDateMobility: Workplaces for US States

POSTED BY: Mads Bahrami

You can now get the COVID19 data at the US County level from WDR. Check out my short post on that. enter image description here

POSTED BY: Mads Bahrami

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: enter image description here

POSTED BY: Mads Bahrami
Posted 4 years ago

Mads, This is great! Normalizing the Cases map by Population over counties for a given state would be interesting see. Sam Daniel, Tucson,

POSTED BY: Sam Daniel
Posted 4 years ago

I've added a post demonstrating a few ways to analyze the Nextstrain COVID-19 data with Newick functions available in the Wolfram Function Repository

POSTED BY: John Cassel

I've just submitted a 3D modeling approach to the SARS-CoV-2 virus here:

POSTED BY: Jeffrey Bryant

While all the initial major outbreaks in the US were in major cities there have been lots of news stories about outbreaks in rural areas, especially around meat processing plants. So I wanted to see if the lower population areas in the US are catching up to the denser areas.

tldr, No, not yet at least.

POSTED BY: Bob Sandheinrich

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.

POSTED BY: Luis Borgonovo

Don't forget the upcoming free study group devoted to analyzing COVID-19 data. I look forward to joining you online on September 28, @Hamza Alsamraee. Here is the sign up link for anyone who is interested:

POSTED BY: Jamie Peterson

I thought some of you might be interested in our recent work:

We have tried to come up with an understanding of the precise influences of human demographics and settlement, as well as the dynamic factors of climate, susceptible depletion, and intervention, on the spread of localized epidemics.

We've also designed a dashboard for it (based on US counties):

POSTED BY: Mads Bahrami

A resource by Daniel Lichtblau was added:

From sequenced SARS-CoV-2 genomes to a phylogenetic tree

POSTED BY: Moderation Team

A post by Kyle Keane was added. As an additional benefit, it includes valuable replies :

Ways to visualize COVID-19 simulation results?

POSTED BY: Moderation Team

A resource by Jessica Shi was added:

TraCOV: Personalized COVID-19 Risk Analysis Tool

POSTED BY: Moderation Team
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