A resource by Jessica Shi was added:

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

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

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: https://wolfr.am/COVID19StudyGroup

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

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

https://community.wolfram.com/groups/-/m/t/1989540

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

https://community.wolfram.com/groups/-/m/t/1958952

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:

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

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

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

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

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

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.

Attachments:

2021-06-04T14_39...nb

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.

Here's an overview of some compartment based models used in epidemiological modeling: https://community.wolfram.com/groups/-/m/t/1920119

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

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

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.

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.

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????

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

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!

Dear Szabolcs,

I have personally compiled the various data for Korea, where you can find the estimated number of daily testing: https://github.com/interglobe07/COVID-19-Korean-Data (COVID19 Korean data Updates) I hope you can find it useful. Best,

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

https://wolfr.am/COVID-19-DATA

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.

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.

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

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

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

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

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.

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: https://twitch.tv/wolfram

Come check it out!

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

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.

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.

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?

Specifically:

• 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.

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

HI.

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

Attachments:

ConstrainedExample.nb

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?

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.

Attachments:

COVID19 Korean Data.xlsx

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.

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,

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.

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.

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 :-)

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

https://github.com/arnoudbuzing/wolfram-coronavirus/tree/master/notebooks

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

Might be useful with your IGraph package?

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)

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: https://datarepository.wolframcloud.com/contact-us Please note, Wolfram Data Repository entries are continuously updated and new information can appear on their pages in the future.

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

It would be neat to see a SEIR type analysis

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

https://gitlab.com/sparvu/mathematica

data={{24527,490},{8098,774},{2500,858},{1568,616}};
BarChart[data,
PlotTheme->"Business",
ChartLayout->"Stacked",
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],
AspectRatio->0.55]

• I have a few contributions here: https://github.com/arnoudbuzing/wolfram-coronavirus
• The notebooks from this repo can be viewed from this location: https://wolfr.am/JZNRriEE
• This notebook gallery was created with this tool: https://wolfr.am/githubviewer