⭐ [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!

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FEATURED CONTENT

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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: [CALL] Making COVID-19 Data Computable - Staff Picks - WOLFRAM COMMUNITY .

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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

Epidemic Data for Novel Coronavirus COVID-19 | Wolfram Data Repository

Genetic Sequences for the SARS-CoV-2 Coronavirus

Genetic Sequences for the SARS-CoV-2 Coronavirus | Wolfram Data Repository

Patient Medical Data for Novel Coronavirus COVID-19

Patient Medical Data for Novel Coronavirus COVID-19 | Wolfram Data Repository

COVID-19 Hospital Resource Use Projections

COVID-19 Hospital Resource Use Projections | Wolfram Data Repository

OECD Data: Hospital Beds Per Country

OECD Data: Hospital Beds Per Country | Wolfram Data Repository

Hospital Beds Per US State

Hospital Beds Per US State | Wolfram Data Repository

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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:

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FEATURED

COVID-19 Livestream Notebook March 24 by Stephen Wolfram

Exploring Pandemic Data

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

Agent-Based Network Models for COVID-19 - Staff Picks - WOLFRAM COMMUNITY

Epidemiological Models for Influenza and COVID-19 by Robert Nachbar

Epidemiological models for Influenza and COVID-19 - Staff Picks - WOLFRAM COMMUNITY

Epidemic simulation with a polygon container by Francisco Rodríguez

Epidemic simulation with a polygon container - Staff Picks - WOLFRAM COMMUNITY

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

[GIF] Distance to nearest confirmed US COVID-19 case - Staff Picks - WOLFRAM COMMUNITY

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EPIDEMIC MODELING: SIMULATION

Epidemic simulation with a polygon container by Francisco Rodríguez

Epidemic simulation with a polygon container - Staff Picks - WOLFRAM COMMUNITY

Agent based epidemic simulation by Jon McLoone

[NB] Agent based epidemic simulation - Staff Picks - WOLFRAM COMMUNITY

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

Modeling the spatial spread of infection diseases in the US - Staff Picks - WOLFRAM COMMUNITY

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

Geo-spatial-temporal COVID-19 simulations and visualizations over USA - Ideas - WOLFRAM COMMUNITY

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

Life, Liberty, and Lockdowns: cellular automaton approach - Staff Picks - WOLFRAM COMMUNITY

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EPIDEMIC MODELING: COMPARTMENTAL

Teaching notebook on disease models by Gareth Russell

Course models: Disease Ecology & Evolution, Evolutionary Medicine - Staff Picks - WOLFRAM COMMUNITY

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

Stochastic epidemiology models with applications to the COVID-19 - Staff Picks - WOLFRAM COMMUNITY

COVID19: Italian SIRD estimates and prediction by Christos Papahristodoulou

COVID19: Italian SIRD estimates and prediction - Staff Picks - WOLFRAM COMMUNITY

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

Solver for COVID-19 epidemic model with the Caputo fractional derivatives - Staff Picks - WOLFRAM COMMUNITY

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

EpiPlay: using Wolfram Language to gamify education in epidemiology - Staff Picks - WOLFRAM COMMUNITY

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

Epidemiological Model for repetitive rapid testing for COVID-19 - Staff Picks - WOLFRAM COMMUNITY

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

Phase transition of a SIR agent-based models - Staff Picks - WOLFRAM COMMUNITY

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

A simple estimate of covid-19 fatalities based on past data - Staff Picks - WOLFRAM COMMUNITY

SIR Model with Log-normal infected periods by Diego Zviovich

[NB] SIR Model with Log-normal infected periods - Staff Picks - WOLFRAM COMMUNITY

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

SEI2HR-Econ model with quarantine and supplies scenarios - Staff Picks - WOLFRAM COMMUNITY

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

COVID-19 policy simulator: can you find the perfect policy? - Staff Picks - WOLFRAM COMMUNITY

Epidemiological Models for Influenza and COVID-19 by Robert Nachbar

Epidemiological models for Influenza and COVID-19 - Staff Picks - WOLFRAM COMMUNITY

Exploring Epidemiological Modeling by Jordan Hasler

Exploring Epidemiological Modeling - Staff Picks - WOLFRAM COMMUNITY

SEI2HR model with quarantine scenarios by Anton Antonov

SEI2HR model with quarantine scenarios - Ideas - WOLFRAM COMMUNITY

The SIR Model for Spread of Disease by Arnoud Buzing

The SIR Model for Spread of Disease - Staff Picks - WOLFRAM COMMUNITY

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

COVID-19 - R0 and Herd Immunity - are we getting closer? - Staff Picks - WOLFRAM COMMUNITY

Basic experiments workflow for simple epidemiological models by Anton Antonov

https://community.wolfram.com/groups/-/m/t/1895675

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

https://community.wolfram.com/groups/-/m/t/1897377

WirVsVirus 2020 hackathon participation by Anton Antonov

https://community.wolfram.com/groups/-/m/t/1907256

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

https://community.wolfram.com/groups/-/m/t/1888335

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

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

A Neat Package for Compartmental Model Diagrams by Hamza Alsamraee

https://community.wolfram.com/groups/-/m/t/2078640

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

https://community.wolfram.com/groups/-/m/t/2004784

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

https://community.wolfram.com/groups/-/m/t/3008488

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EPIDEMIC MODELING: LOGISTIC

COVID-19 pandemic data in Italy by Riccardo Fantoni

https://community.wolfram.com/groups/-/m/t/1909687

Predicting Coronavirus Epidemic in United States by Robert Rimmer

https://community.wolfram.com/groups/-/m/t/1906954

Tracking Coronavirus Testing in the United States by Robert Rimmer

https://community.wolfram.com/groups/-/m/t/1902302

Logistic Model for Quarantine Controlled Epidemics by Robert Rimmer

https://community.wolfram.com/groups/-/m/t/1900530

Updated: coronavirus logistic growth model: China by Robert Rimmer

https://community.wolfram.com/groups/-/m/t/1890271

Coronavirus logistic growth model: China by Robert Rimmer

https://community.wolfram.com/groups/-/m/t/1887435

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

https://community.wolfram.com/groups/-/m/t/1887823

Coronavirus logistic growth model: South Korea by Robert Rimmer

https://community.wolfram.com/groups/-/m/t/1894561

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

https://community.wolfram.com/groups/-/m/t/2092786

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GENOMICS

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

https://community.wolfram.com/groups/-/m/t/2205357

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

https://community.wolfram.com/groups/-/m/t/2206874

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

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

Finding and analyzing a COVID subvariant in Australia by Daniel Lichtblau

https://community.wolfram.com/groups/-/m/t/2342489

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

https://blog.wolfram.com/2021/08/19/newick-trees-proximity-resources-and-accessions-analyzing-sars-cov-2-genetic-sequences/

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

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

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

https://community.wolfram.com/groups/-/m/t/1961461

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

https://community.wolfram.com/groups/-/m/t/1874816

Visualizing Sequence Alignments from the COVID-19 by Jessica Shi

https://community.wolfram.com/groups/-/m/t/1875352

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

https://community.wolfram.com/groups/-/m/t/1887456

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

https://community.wolfram.com/groups/-/m/t/1878824

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

https://community.wolfram.com/groups/-/m/t/1875994

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DATA ANALYSIS

Optimal Annual COVID-19 Vaccine Boosting Dates Following Previous Booster Vaccination or Breakthrough Infection by Jeffrey Townsend

https://community.wolfram.com/groups/-/m/t/3341399

Probability of early infection extinction depends linearly on the virus clearance rate by Nóra Juhász

https://community.wolfram.com/groups/-/m/t/3307502

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

https://community.wolfram.com/groups/-/m/t/3056172

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

https://community.wolfram.com/groups/-/m/t/2569395

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

https://community.wolfram.com/groups/-/m/t/2389110

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

https://community.wolfram.com/groups/-/m/t/1974412

A simple COVID-19 spread model by Daniel Lichtblau

https://community.wolfram.com/groups/-/m/t/1945196

COVID19: The performance of the Swedish strategy by Christos Papahristodoulou

https://community.wolfram.com/groups/-/m/t/1990972

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

https://community.wolfram.com/groups/-/m/t/1931508

Google Mobility Data by Mads Bahrami

https://community.wolfram.com/groups/-/m/t/1946686

Understanding Aggregate COVID Curves by Christopher Wolfram

https://community.wolfram.com/groups/-/m/t/2068457

Apple mobility trends data visualization by Anton Antonov

https://community.wolfram.com/groups/-/m/t/1942813

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

https://community.wolfram.com/groups/-/m/t/1930261

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

https://community.wolfram.com/groups/-/m/t/1913908

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

https://community.wolfram.com/groups/-/m/t/1912710

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

https://community.wolfram.com/groups/-/m/t/1904507

COVID19 in Iran: under-diagnosis issue by Mads Bahrami

https://community.wolfram.com/groups/-/m/t/1891140

Coronavirus analysis: descriptive statistics with SQL functions by Damian Calin

https://community.wolfram.com/groups/-/m/t/2206078

Covid-19 vaccine campaigns efficacy analysis by Damian Calin

https://community.wolfram.com/groups/-/m/t/2383314

Argentina: COVID-19 Data Analysis by Tobias Canavesi

https://community.wolfram.com/groups/-/m/t/1932910

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

https://community.wolfram.com/groups/-/m/t/2055704

COVID wave alert: statistical analysis and visualization by Antonio Neves

https://community.wolfram.com/groups/-/m/t/2115658

Predicting COVID-19 using cough sounds classification by Siria Sadeddin

https://community.wolfram.com/groups/-/m/t/2166833

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

https://community.wolfram.com/groups/-/m/t/2324474

Analyzing COVID-19 vaccine sentiment over time by Arshaan Sayed

https://community.wolfram.com/groups/-/m/t/2317293

VAERS data analysis using SQL functions by Damian Calin

https://community.wolfram.com/groups/-/m/t/2351726

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

https://community.wolfram.com/groups/-/m/t/2362327

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

https://community.wolfram.com/groups/-/m/t/2388236

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DATA VISUALIZATIONS

CDC COVID19 vaccination data across US counties by Mads Bahrami

https://community.wolfram.com/groups/-/m/t/2282418

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

https://community.wolfram.com/groups/-/m/t/2004800

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

https://community.wolfram.com/groups/-/m/t/2473065

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

https://community.wolfram.com/groups/-/m/t/1992898

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

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

California COVID19 Data by Mads Bahrami

https://community.wolfram.com/groups/-/m/t/2132204

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

https://community.wolfram.com/groups/-/m/t/1965079

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

https://community.wolfram.com/groups/-/m/t/1962615

100 Days of COVID19 Over US Counties by Mads Bahrami

https://community.wolfram.com/groups/-/m/t/1956368

Population Density Map by Mads Bahrami

https://community.wolfram.com/groups/-/m/t/1955760

Google Mobility Data by Mads Bahrami

https://community.wolfram.com/groups/-/m/t/1946686

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

https://community.wolfram.com/groups/-/m/t/1939045

Data Analysis of Coronavirus in Mexico by Ivan Martinez

https://community.wolfram.com/groups/-/m/t/1927657

Confirmed COVID-19 Cases in Catalonia by Bernat Espigulé Pons

https://community.wolfram.com/groups/-/m/t/1919468

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

https://community.wolfram.com/groups/-/m/t/1911583

COVID19 Confirmed Cases: US Counties by Mads Bahrami

https://community.wolfram.com/groups/-/m/t/1950980

COVID19 data visualization across US counties by Mads Bahrami

https://community.wolfram.com/groups/-/m/t/2119049

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

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

US Counties COVID-19 deaths plot by Bob Sandheinrich

https://community.wolfram.com/groups/-/m/t/1918332

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

https://community.wolfram.com/groups/-/m/t/1905992

NY Times COVID-19 data visualization by Anton Antonov

https://community.wolfram.com/groups/-/m/t/1911668

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

https://community.wolfram.com/groups/-/m/t/1910116

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

https://community.wolfram.com/groups/-/m/t/1898126

Visualizing the Pandemic Data COVID-19 by Martijn Froeling

https://community.wolfram.com/groups/-/m/t/1899870

COVID-19 visualization of turning point by Isao Maruyama

https://community.wolfram.com/groups/-/m/t/1899911

Mapping “Live” COVID Data on a Globe by Gabriel Lemieux

https://community.wolfram.com/groups/-/m/t/1902102

Novel Coronavirus COVID-19 in Brazil by Estevao Teixeira

https://community.wolfram.com/groups/-/m/t/1905950

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

https://wolfram.discourse.group/t/15313/1

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

https://community.wolfram.com/groups/-/m/t/1962739

General and COVID-19 deaths in Sweden by Oscar Rodriguez

https://community.wolfram.com/groups/-/m/t/2006377

COVID19 Tokyo per days of the week Isao Maruyama

https://community.wolfram.com/groups/-/m/t/2133807

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DATA PROCESSING

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

https://community.wolfram.com/groups/-/m/t/1958307

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

https://community.wolfram.com/groups/-/m/t/1919770

Scraping OpenTable’s “State of the Industry” page by Aaron Enright

https://community.wolfram.com/groups/-/m/t/1911043

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

https://community.wolfram.com/groups/-/m/t/1913247

Web Scraper: New York Times Coronavirus Data by Robert Rimmer

https://community.wolfram.com/groups/-/m/t/1894426

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

https://community.wolfram.com/groups/-/m/t/1977700

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

https://community.wolfram.com/groups/-/m/t/2160386

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MASKS

Effect of mandatory mask usage in COVID cases by Diego Zviovich

https://community.wolfram.com/groups/-/m/t/1919060

Face mask detection: classifying image data by Siria Sadeddin

https://community.wolfram.com/groups/-/m/t/2139499

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Livestream Archives (link)

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Other useful resources

66 Likes
Coronavirus propagation modeling tutorial presentation, OMLDS March 2020
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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.

2021-06-04T14_16_43corona.nb (209 KB)

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

20200205-2019-nCoV.png
20200205-2019-nCoV.png2438×1362 73.5 KB

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]
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Very neat, thanks for sharing!

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It would be neat to see a SEIR type analysis

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

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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

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Many thanks for this.

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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.

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

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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)

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

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

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

province-graph.png
province-graph.png1341×838 94.7 KB

Might be useful with your IGraph package?

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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!

7 Likes
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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 :slight_smile:

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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:

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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.

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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.

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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.

7015iran.png
7015iran.png540×389 4.29 KB

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.

6 Likes