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https://github.com/fnielsen/awesome-covid-19-resources

A curated list of awesome resources for COVID-19
https://github.com/fnielsen/awesome-covid-19-resources

List: awesome-covid-19-resources

awesome-list coronavirus covid-19

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A curated list of awesome resources for COVID-19

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README 

        
# Awesome COVID-19 resources

A curated list of awesome data science, analytics and computer programming resources for COVID-19.



## Datasets 

- [COVID-19 DATABASE: COVID-19: CASISTICA RADIOLOGICA ITALIANA](https://www.sirm.org/category/senza-categoria/covid-19/) - Italian database with CT images of lungs.

- [Coronavirus-Dataset: Dataset of COVID-19 in South Korea](https://www.kaggle.com/kimjihoo/coronavirusdataset) - South Korean dataset detailing virus spread routes.

- [COVID-19 Open Research Dataset (CORD-19)](https://pages.semanticscholar.org/coronavirus-research) - dataset with scholarly articles about COVID-19.

- [European Centre for Disease Prevention and Control](https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide) - geographic distribution of COVID-19 cases worldwide

- [Data Repository by Johns Hopkins CSSE](https://github.com/CSSEGISandData/COVID-19)



### Bioinformatics

- [SARS-CoV-2 and COVID-19 Pathway (Homo sapiens)](https://www.wikipathways.org/index.php/Pathway:WP4846) - WikiPathways model



### Archives

- [nCovMemory](https://github.com/2019ncovmemory/nCovMemory) - Memory of coronavirus: Media Coverage, Non-fiction Writings, and Individual Narratives.

- [COVID-19-TweetIDs](https://github.com/echen102/COVID-19-TweetIDs) - Twitter posts related to COVID-19. Described in *[COVID-19: The First Public Coronavirus Twitter Dataset](https://arxiv.org/abs/2003.07372)*.



## Tools 

- [cord-19-tools](https://pypi.org/project/cord-19-tools/) - David Josephs' pip-installable python package to load the *COVID-19 Open Research Dataset (CORD-19)* dataset. A full load take 5 GB of memory.



## Visualizations



### Count visualizations 

- [Visualization of Covid-19 confirmed cases](http://shinyapps.org/apps/corona/) - Interactive ShinyApp visualization by Felix Schönbrodt (@nicebread) et al. with longitudinal confirmed cumulative cases and deaths. Development from https://github.com/nicebread/corona and data from ECDC and Johns Hopkins University.

- [COVID-19 case counts](https://covid-stats.theo.io/dashboard_draggable.php) - Interactive visualization by [Theo Sanderson](https://twitter.com/theosanderson) of longitudinal COVID-19 cases by country with interactive date offset. Data from Johns Hopkins University.

- [EuroMOMO](https://www.euromomo.eu/graphs-and-maps) - European monitoring of excess mortality for public health action weekly mortality as deviations from the baseline.

- [IHME COVID-19 Projections](https://covid19.healthdata.org) - Deaths per day per country and projection as well as hospital beds statistics. From Institute for Health Metrics and Evaluation.



### Visualizations on maps

- [Visual Dashboard](https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6) - Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) described in *[An interactive web-based dashboard to track COVID-19 in real time](https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30120-1/fulltext)*.



### Epidemiological modeling

- [Modeling COVID-19 Spread vs Healthcare Capacity](https://alhill.shinyapps.io/COVID19seir/) - Interactive ShinyApps by Alison Hill with interactive settings of epidemiological parameters and longitudinal visualization.

- [Effekten af kontakt restriktioner og rejseforbud på COVID-19 udbrud i DK](https://kagr.shinyapps.io/COVID19/) - Interactive ShinyApps in Danish by Kaare Græsbøll and Elisabeth Ottesen Bangsgaard with various interactive epidemiological variables.



### Collections of visualizations

- [Observable Topic: "Coronavirus"](https://observablehq.com/collection/@observablehq/coronavirus) - A selection of interactive Observable notebooks related to COVID-19 and SARS-CoV-2



## Notebooks

- [Estimating COVID-19's Rt in Real-Time](https://github.com/k-sys/covid-19/blob/master/Realtime%20R0.ipynb) - Kevin Systrom's notebook

- [Hierarchical Logistic Growth Curves](https://rpubs.com/bgautijonsson/588811) - Brynjólfur Gauti Jónsson's Bayesian modeling of daily COVID-19 counts with output at https://bgautijonsson.shinyapps.io/Hierarchical_Report/.

- [Stochastic epidemic models](https://github.com/MichaelRiis/stochastic_epidemic_models/blob/master/SIR.ipynb) - Michael Riis Andersen. 



## Papers 

### Epidemiological modeling

- [Effective containment explains sub-exponential growth in confirmed cases of recent COVID-19 outbreak in Mainland China](https://arxiv.org/abs/2002.07572) - Model that captures both, quarantine of symptomatic infected individuals as well as population wide isolation in response to mitigation policies or behavioral changes.



### Lung-CT deep learning

- [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)

- [Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study in 27 patients](https://www.medrxiv.org/content/10.1101/2020.02.25.20021568v2)

- [Deep Learning-Based Quantitative Computed Tomography Model in Predicting the Severity of COVID-19: A Retrospective Study in 196 Patients](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3546089)



### Other 

- [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/2002.05534) - Description of recurrent neural network predicting 6 respiratory patterns from depth camera data ([Scholia](https://scholia.toolforge.org/Q87745200))