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https://github.com/defeatcovid19/awesome-covid19-resources

Collection of awesome links about initiatives fighting COVID19 outbreak
https://github.com/defeatcovid19/awesome-covid19-resources

List: awesome-covid19-resources

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Collection of awesome links about initiatives fighting COVID19 outbreak

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README 

        
# awesome-covid19-resources [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)



> A curated list of awesome resources related to COVID19.



## Contents



- [Datasets and models](#datasets-and-models)

  - [Medical imaging](#medical-imaging)

    - [Imaging datasets](#imaging-datasets)

    - [Medical imaging models](#medical-imaging-models)

  - [Epidemic data and models](#epidemic-data-and-models)

    - [Case datasets](#case-datasets)

    - [Government pages](#government-pages)

    - [Dashboards](#dashboards)

    - [Statistical models](#statistical-models)

- [Selected scientific articles](#selected-scientific-articles)

  - [Medical imaging papers](#medical-imaging-papers)

  - [Epidemic papers](#epidemic-papers)

  - [Clinical record analysis](#clinical-record-analysis)

  - [Computational drug research](#computational-drug-research)

- [Contribute](#contribute)

- [License](#license)



## Datasets and models



Datasets and machine learning models related to COVID-19.



### Medical imaging



- [Pneumonia related Kaggle Datasets](https://www.kaggle.com/search?q=pneumonia+in%3Adatasets) - A collection of medical imaging (chest X-ray or CT) datasets and other resources.



- [Coronavirus disease 2019 (COVID-19)](https://radiopaedia.org/articles/coronavirus-disease-2019-covid-19-1) article on [Radiopaedia](https://radiopaedia.org)

- [Italian RX scans of COVID-19 cases](https://www.sirm.org/category/senza-categoria/covid-19/)



#### Imaging datasets



This section collects COVID-19 and pneumonia related chest x-ray datasets.



[TorchXrayVision](https://github.com/mlmed/torchxrayvision) is a python library of chest X-ray datasets and models providing a standardized interface for some of the datasets listed below.



| Name | Publisher | Type | Images | Classes | Download links |

|------|-----------|:----:|-------:|---------|----------------|

| [COVID-19 image data collection](https://github.com/ieee8023/covid-chestxray-dataset) | [Joseph Paul Cohen](https://josephpcohen.com/) | Chest x-ray / CT | 158 (updating)| COVID-19, SARS, Viral Pneumonia, etc. | [http](https://github.com/ieee8023/covid-chestxray-dataset/archive/master.zip) |

| [NIH Clinical Center chest x-ray datasets](https://www.nih.gov/news-events/news-releases/nih-clinical-center-provides-one-largest-publicly-available-chest-x-ray-datasets-scientific-community) | [National Institutes of Health](https://www.nih.gov) | Chest x-ray | 100,000+ | 14 categories including pneumonia | [http](https://nihcc.app.box.com/v/ChestXray-NIHCC); torrent: [full](https://academictorrents.com/details/557481faacd824c83fbf57dcf7b6da9383b3235a), [224x224](https://academictorrents.com/details/e615d3aebce373f1dc8bd9d11064da55bdadede0) |

| [BIMCV-COVID19](http://bimcv.cipf.es/bimcv-projects/bimcv-covid19/), [BIMCV-PadChest](http://bimcv.cipf.es/bimcv-projects/padchest/) | [Medical Imaging Bank of the Valencia Region](http://bimcv.cipf.es) | Chest x-ray | 160,000+ | 174 labels (no COVID-19 yet) | [http](http://ceib.bioinfo.cipf.es/covid19/padchest_neumonia.zip); torrent: [full](https://academictorrents.com/details/dec12db21d57e158f78621f06dcbe78248d14850), [224x224](https://academictorrents.com/details/e0aeda79626589f31e8bf016660da801f5add88e) |

| [RSNA Pneumonia Detection Challenge](https://www.kaggle.com/c/rsna-pneumonia-detection-challenge) | [Radiological Society of North America](https://www.rsna.org) | chest x-ray | 26,684 | pneumonia object detection bboxes | [kaggle](https://www.kaggle.com/c/10338/download-all) (DICOM), [torrent](https://academictorrents.com/details/95588a735c9ae4d123f3ca408e56570409bcf2a9) (jpeg) |

| [CheXpert](https://stanfordmlgroup.github.io/competitions/chexpert/) | [Stanford Machine Learning Group](https://stanfordmlgroup.github.io) | chest x-ray | 224,316 | 14 categories including pneumonia | [http](https://stanfordmlgroup.github.io/competitions/chexpert/) (registration needed) |

| [MIMIC-CXR-JPG](https://mimic-cxr.mit.edu) | [Johnson et al. (2019)](https://doi.org/10.13026/C2JT1Q) | chest x-ray | 300,000+ | 14 categories including pneumonia | [http](https://physionet.org/content/mimic-cxr/2.0.0/) (credentialing needed) |

| [Open-i](https://openi.nlm.nih.gov/faq)| [National Library of Medicine](https://www.nlm.nih.gov) | chest x-ray | 7,470 | | http: [png](https://openi.nlm.nih.gov/imgs/collections/NLMCXR_png.tgz), [DICOM](https://openi.nlm.nih.gov/imgs/collections/NLMCXR_dcm.tgz),  [labels](https://openi.nlm.nih.gov/imgs/collections/NLMCXR_reports.tgz) |

| [COVID19 High quality images](https://www.kaggle.com/theroyakash/covid19) | [theroyakash](https://www.kaggle.com/theroyakash) | Chest x-ray | 338 | COVID-19, Viral Pneumonia / Normal | [kaggle](https://www.kaggle.com/theroyakash/covid19/download) | 

| [Chest X-Ray Images (Pneumonia)](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia) | [Paul Mooney](https://www.kaggle.com/paultimothymooney) | Pediatric chest x-ray | 5,863 | Pneumonia / Normal | [kaggle](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia/download) |



#### Medical imaging models



Articles, blog posts describing a proposed model:

- [COVID-19 Detection Neural Network (COVNet)](https://github.com/bkong999/COVNet)

- [Detecting COVID-19 in X-ray images with Keras, TensorFlow, and Deep Learning](https://www.pyimagesearch.com/2020/03/16/detecting-covid-19-in-x-ray-images-with-keras-tensorflow-and-deep-learning/)

  - [COVID-10 detection in X-ray images using deep learning and Grad-CAM visualisation](https://www.linkedin.com/posts/sohaiblaraba_covid19-covid19-interpretability-activity-6645675878485409795-Y6gM/) - Derived work on the interpretability of the results.

  - [Deep Learning for Medical Imaging: COVID-19 Detection](https://blogs.mathworks.com/deep-learning/2020/03/18/deep-learning-for-medical-imaging-covid-19-detection/) - Ported the above model to MATLAB.

- [Using Deep Learning to detect Pneumonia caused by NCOV-19 from X-Ray Images](https://towardsdatascience.com/using-deep-learning-to-detect-ncov-19-from-x-ray-images-1a89701d1acd)

  - [COVID-19-Scanner](https://github.com/ajsanjoaquin/COVID-19-Scanner) model from the article

- [BIMCV-COVID-19 model](https://github.com/BIMCV-CSUSP/BIMCV-COVID-19)

- [COVID-Net Open Source Initiative](https://github.com/lindawangg/COVID-Net)



### Epidemic data and models



Collection of case datasets for analyzing the dynamics of the outbreak.



#### Case datasets



ScopePublisherGranularityUpdatedFields1FormatDataset



International level

worldwideJohns Hopkins CSSEcountries2daily1, 2, 3csvlink

worldwideEuropean Centre for Disease Prevention and Controlcountriesdaily1, 3xlslink



Country level

BrazilBrasil.iostate, citiesdaily1, 3csv, API, weblink

CanadaCOVID-19 Canada Open Data Working Groupprovincesdaily1, 2, 3, 7Google Sheetslink

ItalyProtezione Civilenational, regional, provincesdailyn, r: 1, 2, 3, 4, 5, 6, 7; p: 1csv, jsonlink

United StatesThe COVID Tracking Projectstatesdaily1, 3, 7Google Sheets, csv, json, GraphQLlink



[1] Fields explanation:

1. Positive cases

2. Recovered cases

3. Deaths

4. Hospitalized patients

5. Patients in intensive care unit

6. Cases in home confinement

7. COVID-19 tests made



[2] provinces for China, US, Canada, Australia



#### Government pages



Official pages for monitoring the national outbreaks with reported cases. English version is provided, if found.



*Americas*

- [Brazil](https://covid.saude.gov.br/)

- [Canada](https://www.canada.ca/en/public-health/services/diseases/2019-novel-coronavirus-infection.html)

- [Mexico](https://coronavirus.gob.mx/noticias/)

- [United States](https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html)



*Asia*

- [Isreal](https://www.health.gov.il/English/Topics/Diseases/corona/Pages/press-release.aspx)

- [Singapore](https://www.gov.sg/article/covid-19-cases-in-singapore)



*Australia and Oceania*

- [Australia](https://www.health.gov.au/news/health-alerts/novel-coronavirus-2019-ncov-health-alert/coronavirus-covid-19-current-situation-and-case-numbers)

- [New Zealand](https://www.health.govt.nz/our-work/diseases-and-conditions/covid-19-novel-coronavirus/covid-19-current-cases)



*Europe*

- [Austria](https://www.sozialministerium.at/coronavirus) (in German)

- [Belgium](https://www.info-coronavirus.be/en/news/)

- [Denmark](https://www.ssi.dk/aktuelt/sygdomsudbrud/coronavirus/covid-19-i-danmark-epidemiologisk-overvaagningsrapport) (in Danish)

- [Estonia](https://www.terviseamet.ee/en)

- [Finland](https://thl.fi/en/web/infectious-diseases/what-s-new/coronavirus-covid-19-latest-updates)

- [France](https://www.santepubliquefrance.fr/maladies-et-traumatismes/maladies-et-infections-respiratoires/infection-a-coronavirus/articles/infection-au-nouveau-coronavirus-sars-cov-2-covid-19-france-et-monde) (in French)

- [Germany](https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Fallzahlen.html) (in German, see [this page](https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Situationsberichte/Gesamt.html) for situation reports in English)

- [Hungary](http://abouthungary.hu/news-in-brief/coronavirus-heres-the-latest/)

- [Ireland](https://www.gov.ie/en/news/7e0924-latest-updates-on-covid-19-coronavirus/)

- [Italy](http://www.salute.gov.it/portale/nuovocoronavirus/dettaglioContenutiNuovoCoronavirus.jsp?id=5351&area=nuovoCoronavirus&menu=vuoto) (in Italian)

- [Netherlands](https://www.rivm.nl/en/news/current-information-about-novel-coronavirus-covid-19)

- [Norway](https://www.fhi.no/en/id/infectious-diseases/coronavirus/dags--og-ukerapporter/daily-reports-COVID19/)

- [Poland](https://www.gov.pl/web/koronawirus/wykaz-zarazen-koronawirusem-sars-cov-2) (in Polish)

- [Portugal](https://covid19.min-saude.pt/relatorio-de-situacao/) (in Portuguese)

- [Spain](https://www.mscbs.gob.es/en/profesionales/saludPublica/ccayes/alertasActual/nCov-China/situacionActual.htm) (in Spanish)

- [Sweden](https://www.folkhalsomyndigheten.se/smittskydd-beredskap/utbrott/aktuella-utbrott/covid-19/aktuellt-epidemiologiskt-lage/) (in Swedish)

- [Switzerland](https://www.bag.admin.ch/bag/en/home/krankheiten/ausbrueche-epidemien-pandemien/aktuelle-ausbrueche-epidemien/novel-cov/situation-schweiz-und-international.html)

- [United Kingdom](https://www.gov.uk/guidance/coronavirus-covid-19-information-for-the-public#number-of-cases)



#### Dashboards



Dashboards visualizing the dynamics of the outbreak in different geographic areas.



*Worldwide*

- [Johns Hopkins University](https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6)

- [WHO](https://experience.arcgis.com/experience/685d0ace521648f8a5beeeee1b9125cd)

- [Worldometer](https://www.worldometers.info/coronavirus/)



*Country level*

- [Canada](https://art-bd.shinyapps.io/covid19canada/) - Case level dashboard about the COVID-19 outbreak in Canada, curated by [COVID-19 Canada Open Data Working Group](https://github.com/ishaberry/Covid19Canada)

- [Isreal](https://imoh.maps.arcgis.com/apps/webappviewer/index.html?id=20ded58639ff4d47a2e2e36af464c36e&locale=he&/) - Government dashboard for monitoring the COVID-19 outbreak in Israel (in Hebrew)

- [Italy](http://opendatadpc.maps.arcgis.com/apps/opsdashboard/index.html#/b0c68bce2cce478eaac82fe38d4138b1) - Official dashboard for monitoring the COVID-19 outbreak in Italy, provided by [Civil Protection](http://www.protezionecivile.it) of Italy

- [Portugal](https://esriportugal.maps.arcgis.com/apps/opsdashboard/index.html#/acf023da9a0b4f9dbb2332c13f635829) - Official dashboard for monitoring the COVID-19 outbreak in Portugal, provided by the [Public Health Department](https://www.dgs.pt) of Portugal

- [Singapore](https://co.vid19.sg/dashboard) - Unoffical but extremly extensive dashboard for monitoring the COVID-19 outbreak in Singapore at case-level, provided by [@zp_uca](https://twitter.com/zp_uca)

- [Spain](https://covid19.isciii.es) - Official dashboard for monitoring the COVID-19 outbreak in Spain, provided by the [Instituto de Salud Carlos III](https://www.isciii.es)



#### Statistical models



- [COVID-19 Dashboards](https://covid19dashboards.com) - Extensive collection of dashboards, diagrams and other visualizations as well as statistical models of the COVID-19 outbreak.

- [COVID-19 Health System Capacity](https://github.com/daveluo/covid19-healthsystemcapacity) - Open geospatial work to support healthcare systems' capacity in the United States.

- [Epidemic calculator](http://gabgoh.github.io/COVID/index.html) - An interactive visual calculator demonstrating the relations between different epidemic variables.

- [COVID-19 Scenarios](https://neherlab.org/covid19/) - A planning tool for COVID-19 outbreaks in communities across the world.



## Selected scientific articles



A collection of scientific papers related to COVID-19 relevant from the data science point of view.



- [COVID-19 Open Research Dataset](https://pages.semanticscholar.org/coronavirus-research) - A free resource of over 29,000 scholarly articles about COVID-19 and the coronavirus family of viruses

  - [Call to action](https://www.whitehouse.gov/briefings-statements/call-action-tech-community-new-machine-readable-covid-19-dataset/) of the US government on this dataset

  - [Kaggle challenge](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge) associated with the dataset



### Medical imaging papers



*Computer Tomography (CT) images*

- [Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT](https://pubs.rsna.org/doi/10.1148/radiol.2020200905)

- [Lung Infection Quantification of COVID-19 in CT Images with Deep Learning](https://arxiv.org/abs/2003.04655v2)

- [Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic](https://arxiv.org/abs/2003.05037v1): Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis

- [Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia](https://arxiv.org/ftp/arxiv/papers/2002/2002.09334.pdf)

- [A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)](https://www.medrxiv.org/content/10.1101/2020.02.14.20023028v3)

- [Coronavirus Disease 2019 (COVID-19): A Perspective from China](https://pubs.rsna.org/doi/10.1148/radiol.2020200490) - A study discussing the use of different diagnosis tools (CT, x-ray) for early detection of COVID-19



### Epidemic papers



- [A Poisson Autoregressive Model to Understand COVID-19 Contagion Dynamics](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3551626)

- [Relationship between the ABO Blood Group and the COVID-19 Susceptibility](https://www.medrxiv.org/content/10.1101/2020.03.11.20031096v1)



#### Estimating the proportion of asymptomatic cases and transmissibility



- [Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2)](https://science.sciencemag.org/content/early/2020/03/13/science.abb3221)

- [Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020](https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.10.2000180)

- [Evolving Epidemiology and Impact of Non-pharmaceutical Interventions on the Outbreak of Coronavirus Disease 2019 in Wuhan, China](https://www.medrxiv.org/content/10.1101/2020.03.03.20030593v1)

- [Estimation of the asymptomatic ratio of novel coronavirus infections (COVID-19)](https://www.ijidonline.com/article/S1201-9712%2820%2930139-9/pdf)

- [Clinical presentation and virological assessment of hospitalized cases of coronavirus disease 2019 in a travel-associated transmission cluster](https://doi.org/10.1101/2020.03.05.20030502)

- [SARS-CoV-2 Viral Load in Upper Respiratory Specimens of Infected Patients](https://www.nejm.org/doi/10.1056/NEJMc2001737)



### Clinical record analysis



- [Prediction of criticality in patients with severe Covid-19 infection using three clinical features](https://www.medrxiv.org/content/10.1101/2020.02.27.20028027v2): a machine learning-based prognostic model with clinical data in Wuhan

- [Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner](https://arxiv.org/abs/2002.05534v1)



### Computational drug research



- [Repurposing Therapeutics for COVID-19](https://chemrxiv.org/articles/Repurposing_Therapeutics_for_the_Wuhan_Coronavirus_nCov-2019_Supercomputer-Based_Docking_to_the_Viral_S_Protein_and_Human_ACE2_Interface/11871402/4): Supercomputer-Based Docking to the SARS-CoV-2 Viral Spike Protein and Viral Spike Protein-Human ACE2 Interface



## Contribute



Contributions welcome! Read the [contribution guidelines](contributing.md) first.



## License



[![CC0](https://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg)](https://creativecommons.org/publicdomain/zero/1.0/)



To the extent possible under law, Neosperience and other contributors have waived all copyright and related or neighboring rights to this work.