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Introduction

This is an attempt to aggregate as many covid-19 analytical resources online. Ranging from data sources, dashboards, maps, charts, algorithms, and published papers to social media channels and blog posts. If you find a resource not here, please consider contributing by reaching out to Catherine or Michael, or submitting a pull request.

Table of Contents

Epi Datasets

Non-Traditional Datasets

Genomic Datasets

Tools

Visualizations

Epi Models Code

Country Papers

Papers with Code

Journals

Social Media

Deep Learning Models

badges refer to new as of this week. The most recent updates are at the bottom of the enumerated list.

Epi Datasets

  1. Novel Coronavirus (COVID-19) Cases, provided by Johns Hopkins University CSSE https://github.com/CSSEGISandData/COVID-19
  2. Midas Data and Research Portal - https://github.com/midas-network/COVID-19
  1. Raw data in Wuhan, Hubei, and Guangzhou for serious COVID-19 cases, and Wuhan hospitalization data - https://github.com/c2-d2/COVID-19-wuhan-guangzhou-data for Ruoran, Li, Caitlin Rivers, Qi Tan, Megan B Murray, Eric Toner, and Marc Lipsitch. The Demand for Inpatient and ICU Beds for COVID-19 in the US: Lessons From Chinese Cities (March 2020). https://dash.harvard.edu/handle/1/42599304; data at https://github.com/c2-d2/COVID-19-wuhan-guangzhou-data
  2. Google Sheets From DXY.cn Google Sheets - https://docs.google.com/spreadsheets/d/1jS24DjSPVWa4iuxuD4OAXrE3QeI8c9BC1hSlqr-NMiU/edit#gid=1187587451
  3. Kaggle Dataset - https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset, Johns Hopkins University has made an excellent dashboard using the affected cases data. Data is extracted from the google sheets associated and made available here.
  4. ECDC Download today’s data on the geographic distribution of COVID-19 cases worldwide - https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide
  5. BNO - https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/
  6. U.S. Centers for Disease Control and Prevention (CDC) - https://www.cdc.gov/media/dpk/diseases-and-conditions/coronavirus/coronavirus-2020.html
  7. Covid19 News Tracker b Scops - https://covid19.scops.ai/superset/dashboard/home/
  8. 2019 new coronavirus epidemic time series data warehouse (Chinese and English) - https://github.com/BlankerL/DXY-COVID-19-Data
  9. The Covid Tracking Project - https://covidtracking.com/, with real-time API: https://covidtracking.com/api/
  10. COVID Tracking Data (CSV) https://github.com/COVID19Tracking/covid-tracking-data - Developed by Julia Kodysh. GitHub backup for versioning the contents of our public Google spreadsheet data in CSV format.
  11. COVID Post man APIs - During the present novel coronavirus (COVID-19) pandemic, those on the front lines—including health care professionals, researchers, and government experts—need quick, easy access to real-time critical data. This type of information exchange is what APIs do best, and as an API-first company, Postman is committed to providing whatever assistance we can in this area.
  12. New York Times Coronavirus (Covid-19) Data in the United States - https://github.com/nytimes/covid-19-data - The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak. We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak. The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

Unstructured and Non-Traditional Datasets

  1. Chinese nCov Memory - Memory of 2020 nCoV: Media Coverage, Non-fiction Writings, and Individual Narratives (Continuously updating) https://github.com/2019ncovmemory/nCovMemory, About - https://qz.com/1811018/chinese-citizens-use-github-to-save-coronavirus-memories/ here: https://2019ncovmemory.github.io/nCovMemory/
  2. Chen, Emily, Kristina Lerman, and Emilio Ferrara. "COVID-19: The First Public Coronavirus Twitter Dataset." arXiv preprint arXiv:2003.07372 (2020). https://arxiv.org/pdf/2003.07372.pdf Github https://github.com/echen102/COVID-19-TweetIDs
  3. Smartphone data reveal which Americans are social distancing (and not) - https://www.washingtonpost.com/technology/2020/03/24/social-distancing-maps-cellphone-location/, data available by UnaCast; data free for non-for-profit groups - https://www.unacast.com/covid19
  4. Covid Medical Radiology Datasets https://github.com/ieee8023/covid-chestxray-dataset; https://github.com/lindawangg/COVID-Net by @lindawangg, and https://github.com/ieee8023/covid-chestxray-dataset by @ieee8023
  5. Kinsa temperature data https://www.nytimes.com/2019/02/14/health/kinsa-flu-tracking.html - Kinsa's fever map https://techcrunch.com/2020/03/23/kinsas-fever-map-could-show-just-how-crucial-it-is-to-stay-home-to-stop-covid-19-spread/
  6. Global research on coronavirus disease (COVID-19) - https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov
  7. COVID-19 Open Research Dataset (CORD-19) - https://pages.semanticscholar.org/coronavirus-research - In response to the COVID-19 pandemic, the Allen Institute for AI has partnered with leading research groups to prepare and distribute the COVID-19 Open Research Dataset (CORD-19), a free resource of over 45,000 scholarly articles, including over 33,000 with full text, about COVID-19 and the coronavirus family of viruses for use by the global research community.
  8. Data Against COVID-19 - https://www.data-against-covid.org/
  9. COVID-19 and Computer Audition: An Overview on What Speech & Sound Analysis Could Contribute in the SARS-CoV-2 Corona Crisis Schuller, Björn W., et al. "Covid-19 and computer audition: An overview on what speech & sound analysis could contribute in the SARS-CoV-2 Corona crisis." arXiv preprint arXiv:2003.11117 (2020). https://arxiv.org/pdf/2003.11117.pdf
  10. Mobile phone data and COVID-19: Missing an opportunity? Oliver, Nuria, et al. "Mobile phone data and COVID-19: Missing an opportunity?." arXiv preprint arXiv:2003.12347 (2020). https://arxiv.org/pdf/2003.12347.pdf
  11. Mobile phone location data reveal the effect and geographic variation of social distancing on the spread of the COVID-19 epidemic Gao, Song, et al. "Mobile phone location data reveal the effect and geographic variation of social distancing on the spread of the COVID-19 epidemic." arXiv preprint arXiv:2004.11430 (2020). https://arxiv.org/pdf/2004.11430.pdf
  12. A First Instagram Dataset on COVID-19 Zarei, Koosha, et al. "A first Instagram dataset on COVID-19." arXiv preprint arXiv:2004.12226 (2020). https://arxiv.org/pdf/2004.12226.pdf
  13. Changes in electricity demand pattern in Europe due to COVID-19 shutdowns Narajewski, Michał, and Florian Ziel. "Changes in electricity demand pattern in Europe due to COVID-19 shutdowns." arXiv preprint arXiv:2004.14864 (2020). https://arxiv.org/pdf/2004.14864.pdf
  14. Rapidly Bootstrapping a Question Answering Dataset for COVID-19 Tang, Raphael, et al. "Rapidly Bootstrapping a Question Answering Dataset for COVID-19." arXiv preprint arXiv:2004.11339 (2020). https://arxiv.org/pdf/2004.11339.pdf
  15. CORD-19: The COVID-19 Open Research Dataset Wang, Lucy Lu, et al. "CORD-19: The Covid-19 Open Research Dataset." arXiv preprint arXiv:2004.10706 (2020).https://arxiv.org/pdf/2004.10706.pdf

Genomic Datasets

  1. Nextstrain - https://github.com/nextstrain/ncov - The hCoV-19 / SARS-CoV-2 genomes were generously shared via GISAID. We gratefully acknowledge the Authors, Originating and Submitting laboratories of the genetic sequence and metadata made available through GISAID on which this research is based. For a full list of attributions please see the metadata file.
  2. Genomic epidemiology of novel coronavirus - https://nextstrain.org/ncov?c=country - Showing 838 of 838 genomes sampled between Dec 2019 and Mar 2020 | the NextStrain Team | nextstrain
  3. Genomic epidemiology of hCoV-19 - https://www.gisaid.org/epiflu-applications/next-hcov-19-app/ - Showing 838 of 838 genomes sampled between Dec 2019 and Mar 2020.| GISAID
  4. Genomic epidemiology of novel coronavirus - https://nextstrain.org/ncov?c=country - Showing 838 of 838 genomes sampled between Dec 2019 and Mar 2020 | the NextStrain Team | nextstrain
  5. Phylodynamic Analysis - http://virological.org/ - Novel 2019 coronavirus category| virological
  6. Genomic epidemiology of hCoV-19 - https://www.gisaid.org/epiflu-applications/next-hcov-19-app/ - Showing 838 of 838 genomes sampled between Dec 2019 and Mar 2020.| GISAID
  7. Innophore protein modeling https://innophore.com/2019-ncov/ - Validating the protease sequence | Innophore
  8. Wuhan coronavirus 2019-nCoV protease homology model - https://3dprint.nih.gov/discover/3DPX-012867| - Homolgy model by Phyre2 of the Wuhan coronavirus 2019-nCoV protease, https://innophore.com/2019-ncov From a PDB file in the PyMol session linked in that article.| NIH

Tools

Search Engines, Tools

  1. DISCOVER COVID - https://discovid.ai/search | In response to the COVID-19 pandemic, the White House and a coalition of leading research groups have prepared the COVID-19 Open Research Dataset (CORD-19), which contains over 57,000 scholarly articles about COVID-19 and related coronaviruses. In a call to action, the world's artificial intelligence experts have been asked to develop text and data mining tools that can help the medical community keep up with the rapid increase in literature. So, that’s what we did! Our search engine assists researchers to easily browse through the latest literature, explore related articles and thus helps to discover new avenues for research. We apply a machine learning approach called topic modeling, that helps us discover underlying topics in the whole set of publications. Each paper can then be seen as a mixture of these topics. This allows us to find related papers with a similar topic-mixture (Daniel Wolffram).
  2. COVID-19 Primer - https://covid19primer.com/dashboard | Quickly understand the scientific progress in the fight against COVID-19. Using the most advanced NLP algorithms, read summaries and discover trends in the latest research papers and the conversations around them. Every 24hrs.
  3. Review and Listing of Covid19 Open Datasets https://arxiv.org/pdf/2004.06111.pdf

Visualizations

Maps, Descriptive Charts, Dashboards

  1. Mapping 2019-nCoV - https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30120-1/fulltext (Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis; published online Feb 19. https://doi.org/10.1016/S1473-3099(20)30120-1),
  2. U.S. Centers for Disease Control and Prevention - https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/world-map.html

  1. HealthMap alert notifications - https://healthmap.org/wuhan/

  2. HealthMap/John Brownstein Covid-19 Map - https://www.healthmap.org/covid-19/

  3. Covid-19 spread, Chinese Disease Control - http://2019ncov.chinacdc.cn/2019-nCoV/

  4. New York Times/Lai R KK, et al., Coronavirus Map: Tracking the Global Outbreak - https://www.nytimes.com/interactive/2020/world/coronavirus-maps.html

  5. European Centre for Disease Prevention and Control, https://darwinanddavis.github.io/worldmaps/coronavirus.html (Github: https://github.com/darwinanddavis/worldmaps)

  6. University of Virginia - COVID-19 Surveillance Dashboard, http://ncov.bii.virginia.edu/dashboard/

  7. University of Virginia - COVID-19 Cases and Clusters Outside of China, https://datastudio.google.com/u/0/reporting/f6ad0988-f203-45f8-8d18-5d726c1d2d8b/page/MGzDB

  8. University of Washington HGIS Lab - https://hgis.uw.edu/virus/ (Github: https://github.com/jakobzhao/virus)

  9. nCov2019 for studying COVID-19 coronavirus outbreak, Tianzhi Wu, Erqiang Hu, Patrick Tung, Xijin Ge, Guangchuang Yu - nCov2019: An R package with real-time data, historical data and Shiny app (https://guangchuangyu.github.io/nCov2019/)

  10. Dipartimento della Protezione Civile COVID-19 Italia - Monitoraggio della situazione - http://opendatadpc.maps.arcgis.com/apps/opsdashboard/index.html#/b0c68bce2cce478eaac82fe38d4138b1

  11. Esri Story Map Mapping the novel coronavirus outbreak - https://storymaps.arcgis.com/stories/4fdc0d03d3a34aa485de1fb0d2650ee0

  12. World Health Organization. Novel coronavirus (COVID-19) situation (public dashboard) - https://who.maps.arcgis.com/apps/opsdashboard/index.html#/c88e37cfc43b4ed3baf977d77e4a0667

  13. Crowdsourced Google Map by covid-2019 Reddit Map Community - https://www.google.com/maps/d/u/0/viewer?mid=1yCPR-ukAgE55sROnmBUFmtLN6riVLTu3&ll=30.359193252484147%2C0&z=2 17.COVID19 Infodemics Observatory - https://covid19obs.fbk.eu/, CoMuNe Labs

  14. Bing COVID Tracker - https://www.bing.com/covid

  15. E-Tracking map of the #CoViD19 in Africa - http://umap.openstreetmap.fr/fr/map/e-tracking-map-of-the-covid19-in-africa_411333#3/10.13/45.34

  16. Vox - 11 coronavirus pandemic charts - https://www.vox.com/future-perfect/2020/3/12/21172040/coronavirus-covid-19-virus-charts

  17. Early Alert - https://early-alert.maps.arcgis.com/apps/opsdashboard/index.html#/20bfbf89c8e74c0494c90b1ae0fa7b78

  18. EpiRisk - link here

  19. WorldoMeters- https://www.worldometers.info/coronavirus/

  20. Covid2019app Live Site - https://covid2019app.live/

  21. Here’s how coronavirus spreads on a plane—and the safest place to sit - https://www.nationalgeographic.com/science/2020/01/how-coronavirus-spreads-on-a-plane/

  22. How Much Worse the Coronavirus Could Get, in Charts- https://www.nytimes.com/interactive/2020/03/13/opinion/coronavirus-trump-response.html, By Nicholas Kristof and Stuart A. Thompson

  23. COVID-19 Mobility Monitoring project, ISI Foundation and Cuebiq - https://covid19mm.github.io/in-progress/2020/03/13/first-report-assessment.html

  24. How the Virus Got Out - link here

  25. COVID-19 Scenarios - https://neherlab.org/covid19/?fbclid=IwAR3yjOLs7zCdT7mI_OpBiB1-1Kgaz1-V8MqpBQZE6dMFiXC46d-8UWUOEQc

  26. An interactive visualization of the exponential spread of COVID-19 A project to explore the global growth of COVID-19. Updated daily. Inspired by the work of John Burn-Murdoch. - http://91-divoc.com/pages/covid-visualization/

  27. 1point3acres - https://coronavirus.1point3acres.com/en -

  28. Example of State county-by-county TN model - https://jefferson-county-tn-coronavirus-response-jcgiscb.hub.arcgis.com/app/79afca92fb024131af8322da5fd6ee80

  29. Skyris Odin-Covid19 - https://odin-covid19.com/ Welcome to our ODIN Lite access portal providing data from around the globe related to the currently emerging COVID-19 infectious disease. This data is fully identified and processed using Artificial Intelligence and Natural Language Processing to provide a better understanding of the effects and scope of the disease. This portal provides a very basic, scaled down version of our standard ODIN portal to facilitate free and open access to as many users as might need this data.

  30. Covid19 Healthcare Projections* by University Washington IHME - https://covid19.healthdata.org/projections, The charts below show projected hospital resource use based on COVID-19 deaths. Downloadable data.

  31. Covid Act Now - https://covidactnow.org/ - CovidActNow.org was created by a team of data scientists, engineers, and designers in partnership with epidemiologists, public health officials, and political leaders to help understand how the COVID-19 pandemic will affect their region.

  32. Covid19 R Shiny Dashboard from NYT by @_RCharlie Charlie Thompson - https://rcharlie.shinyapps.io/covid-19-data/

  33. Social Distancing Scoreboard - https://www.unacast.com/covid19/social-distancing-scoreboard - We created this interactive Scoreboard, updated daily, to empower organizations to measure and understand the efficacy of social distancing initiatives at the local level.

  34. COVID-19 Global Economic Impact on Online Retail Emarsys created this platform to help and guide our customers, partners, and the business community by drawing from online engagement data of more than one billion consumers worldwide, interacting with approximately 2500 brands to provide an up-to-date view and trends of the economic impact of the COVID-19 pandemic on online retailershttps://ccinsight.org/

  35. Coronavirus Interactive Dashboard (Tweaked) on Google Data Studio - https://www.gohkokhan.com/corona-virus-interactive-dashboard-tweaked/

  36. Government of Canada Coronavirus disease (COVID-19): Outbreak update https://www.canada.ca/en/public-health/services/diseases/2019-novel-coronavirus-infection.html

  37. **Covid vs. US Daily Average Cause of Death" by Flourish - https://public.flourish.studio/visualisation/1712761/

  38. Bay Area Counties - Bay Area COVID-19 Cases - https://www.stanforddaily.com/2020/03/24/visualized-covid-19-cases-in-santa-clara-county/

  39. Covidcounties.org - https://covidcounties.org/ The Butte Lab at UCSF has partnered with Paul Bleicher MD, PhD, former CEO of OptumLabs, to calculate and visualize COVID-19 statistics for every county in the United States.

  40. COVID-19 Community Mobility Reports - https://www.google.com/covid19/mobility/ - These Community Mobility Reports aim to provide insights into what has changed in response to policies aimed at combating COVID-19. The reports chart movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

  41. Coronavirus in the United States: Mapping the COVID-19 outbreak in the states and counties https://usafacts.org/visualizations/coronavirus-covid-19-spread-map/ Use these maps to track the COVID-19 outbreak coast to coast. Follow US counties to see cases on a local level, including confirmed cases and deaths.

  42. Google News Coronavirus stats, news, maps - https://news.google.com/covid19/map?hl=en-US&gl=US&ceid=US:en

  43. NPR https://www.npr.org/sections/health-shots/2020/03/16/816707182/map-tracking-the-spread-of-the-coronavirus-in-the-u-s

  44. COVID-19 LITERATURE CLUSTERING Given the large number of literature and the rapid spread of COVID-19, it is difficult for health professionals to keep up with new information on the virus. Can clustering similar research articles together simplify the search for related publications? How can the content of the clusters be qualified? By using clustering for labelling in combination with dimensionality reduction for visualization, the collection of literature can be represented by a scatter plot. On this plot, publications of highly similar topic will share a label and will be plotted near each other. In order, to find meaning in the clusters, topic modelling will be performed to find the keywords of each cluster. This project is done for Kaggle's COVID-19 Open Research Dataset Challenge (CORD-19). https://maksimekin.github.io/COVID19-Literature-Clustering/plots/t-sne_covid-19_interactive.html

Country-Specific Models and Papers

Papers modeling disease transmission and impact in non-U.S. countries.

  1. Modeling the Epidemic Outbreak and Dynamics of COVID-19 in Croatia Lojić Kapetanović, Ante; Poljak, Dragan - The paper deals with a modeling of the ongoing epidemic caused by Coronavirus disease 2019 (COVID-19) on the closed territory of the Republic of Croatia. https://ui.adsabs.harvard.edu/abs/2020arXiv200501434L/abstract
  2. Simulations of the spread of COVID-19 and control policies in Tunisia Ben Miled, Slimane; Kebir, Amira - We develop and analyze in this work an epidemiological model for COVID-19 using Tunisian data. https://ui.adsabs.harvard.edu/abs/2020arXiv200500750B/abstract
  3. A Note on the Evolution of Covid-19 in Italy Dattoli, Giuseppe, et al. "A Note on the Evolution of Covid-19 in Italy." arXiv preprint arXiv:2003.08684 (2020). - https://arxiv.org/pdf/2003.08684.pdf
  4. Comparative analysis of the diffusion of Covid-19 infection in different countries Granozio, Fabio Miletto. "Comparative analysis of the diffusion of Covid-19 infection in different countries." arXiv preprint arXiv:2003.08661 (2020). https://ui.adsabs.harvard.edu/abs/2020arXiv200308661M/abstract
  5. Spatial-Temporal Dataset of COVID-19 Outbreak in China Liu, Wenyuan, Peter Tsung-Wen Yen, and Siew Ann Cheong. "Coronavirus disease 2019 (COVID-19) outbreak in China, spatial temporal dataset." arXiv preprint arXiv:2003.11716 (2020). https://arxiv.org/pdf/2003.11716.pdf
  6. Some numerical observations about the COVID-19 epidemic in Italy Zullo, Federico. "Some numerical observations about the COVID-19 epidemic in Italy." arXiv preprint arXiv:2003.11363 (2020). https://arxiv.org/pdf/2003.11363.pdf
  7. Preliminary analysis of COVID-19 spread in Italy with an adaptive SEIRD model Piccolomini, Elena Loli, and Fabiana Zama. "Preliminary analysis of COVID-19 spread in Italy with an adaptive SEIRD model." arXiv preprint arXiv:2003.09909 (2020). https://ui.adsabs.harvard.edu/abs/2020arXiv200309909L/abstract
  8. Data analysis and modeling of the evolution of COVID-19 in Brazil Crokidakis, Nuno. "Data analysis and modeling of the evolution of COVID-19 in Brazil." arXiv preprint arXiv:2003.12150 (2020). https://arxiv.org/pdf/2003.12150.pdf
  9. On the Evolution of Covid-19 in Italy: a Follow up Note Dattoli, Giuseppe, et al. "On the Evolution of Covid-19 in Italy: a Follow up Note." arXiv preprint arXiv:2003.12667 (2020). https://arxiv.org/pdf/2003.12667.pdf
  10. COVID - 19: A model for studying the evolution of contamination in Brazil Schulz, Rodrigo A., Carlos H. Coimbra-Araújo, and Samuel WS Costiche. "COVID-19: A model for studying the evolution of contamination in Brazil." arXiv preprint arXiv:2003.13932 (2020). https://arxiv.org/pdf/2003.13932.pdf
  11. Model studies on the COVID-19 pandemic in Sweden Qi, Chong, et al. "Model studies on the COVID-19 pandemic in Sweden." arXiv preprint arXiv:2004.01575 (2020).https://arxiv.org/pdf/2004.01575.pdf
  12. Comparison of different exit scenarios from the lock-down for COVID-19 epidemic in the UK and assessing uncertainty of the predictions Zhigljavsky, Anatoly, et al. "Comparison of different exit scenarios from the lock-down for COVID-19 epidemic in the UK and assessing uncertainty of the predictions." arXiv preprint arXiv:2004.04583 (2020). https://arxiv.org/pdf/2004.04583.pdf
  13. A critique of the Covid-19 analysis for India by Singh and Adhikari Dhar, Abhishek. "A critique of the Covid-19 analysis for India by Singh and Adhikari." arXiv preprint arXiv:2004.05373 (2020). https://arxiv.org/pdf/2004.05373.pdf
  14. Limited containment options of COVID-19 outbreak revealed by regional agent-based simulations for South Africa Bossert, Andreas, et al. "Limited containment options of COVID-19 outbreak revealed by regional agent-based simulations for South Africa." arXiv preprint arXiv:2004.05513 (2020) https://arxiv.org/pdf/2004.05513.pdf
  15. Impact of intervention on the spread of COVID-19 in India: A model based study Senapati, Abhishek, et al. "Impact of intervention on the spread of COVID-19 in India: A model based study." arXiv preprint arXiv:2004.04950 (2020). https://arxiv.org/pdf/2004.04950.pdf
  16. A data driven analysis and forecast of an SEIARD epidemic model for COVID-19 in Mexico de León, Ugo Avila-Ponce, Ángel GC Pérez, and Eric Avila-Vales. "A data driven analysis and forecast of an SEIARD epidemic model for COVID-19 in Mexico." arXiv preprint arXiv:2004.08288 (2020). https://arxiv.org/pdf/2004.08288.pdf
  17. Predicting Infection of COVID-19 in Japan: State Space Modeling Approach Kobayashi, Genya, et al. "Predicting Infection of COVID-19 in Japan: State Space Modeling Approach." arXiv preprint arXiv:2004.13483 (2020). https://arxiv.org/pdf/2004.13483.pdf
  18. COVID-19 in Italy: An app for a province-based analysis Ferrari, Luisa, et al. "COVID-19 in Italy: An app for a province-based analysis." arXiv preprint arXiv:2004.12779 (2020).https://arxiv.org/pdf/2004.12779.pdf
  19. Investigating the dynamics of COVID-19 pandemic in India under lockdown Pai, Chintamani, Ankush Bhaskar, and Vaibhav Rawoot. "Investigating the dynamics of COVID-19 pandemic in India under lockdown." arXiv preprint arXiv:2004.13337 (2020).https://arxiv.org/pdf/2004.13337.pdf
  20. The resumption of sports competitions after COVID-19 lockdown: The case of the Spanish football league. Buldú, Javier M., Daniel R. Antequera, and Jacobo Aguirre. "The resumption of sports competitions after COVID-19 lockdown: The case of the Spanish football league." arXiv preprint arXiv:2004.14940 (2020). https://arxiv.org/pdf/2004.14940.pdf
  21. COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach Pinter, Gergo, et al. "COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach." A Hybrid Machine Learning Approach (May 2, 2020) (2020). https://www.medrxiv.org/content/medrxiv/early/2020/05/06/2020.05.02.20088427.full.pdf
  22. Minor covid-19 association with crime in Sweden, a five week follow up Gerell, Manne. "Minor covid-19 association with crime in Sweden, a five week follow up." (2020). https://osf.io/preprints/socarxiv/w7gka/

Epi Models Code

  1. COVID-19 Growth Rate Prediction- https://covid19dashboards.com/growth-bayes/ - We assume a negative binomial likelihood as we are dealing with count data. A Poisson could also be used but the negative binomial allows us to also model the variance separately to give more flexibility. | Thomas Wiecki, @HamelHusain
  2. Estimating The Mortality Rate For COVID-19- https://covid19dashboards.com/covid-19-mortality-estimation/#Interpretation-of-Country-Level-Parameters - Using Country-Level Covariates To Correct For Testing & Reporting Biases And Estimate a True Mortality Rate. (Github model: https://github.com/jwrichar/COVID19-mortality, full analysis https://github.com/jwrichar/COVID19-mortality/blob/master/COVID-19%20Mortality%20Rate.ipynb) | @HamelHusain, @jwrichar
  3. NobBS: Nowcasting by Bayesian Smoothing - https://github.com/sarahhbellum/NobBS - NobBS is Bayesian approach to estimate the number of occurred-but-not-yet-reported cases from incomplete, time-stamped reporting data for disease outbreaks. NobBS learns the reporting delay distribution and the time evolution of the epidemic curve to produce smoothed nowcasts in both stable and time-varying case reporting settings. | sarahhbellum
  4. Scenario analysis for the transmission of COVID-19 in Georgia - http://2019-coronavirus-tracker.com/stochastic-GA.html - The epidemiology of COVID-19 in the United States is poorly understood. To better understand the potential range of epidemic outcomes in the state of Georgia, we developed a model based on data from Hubei Province, China calibrated to regionally specific conditions in Georgia and observations of the number of reported cases in Georgia in early March. Github - https://github.com/CEIDatUGA/ncov-wuhan-stochastic-model | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia
  5. Probability of widespread transmission - http://2019-coronavirus-tracker.com/final-size.html; Github (private) - https://github.com/CEIDatUGA/ncov-coupled-outbreaks | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia
  6. Spatial Spread of 2019 novel coronavirus in China - http://2019-coronavirus-tracker.com/spatial-china.html - We developed a gravity-based model to better understand the risk of spatial spread of the 2019-nCov at the prefecture level in China, and to determine the efficacy of quarantines imposed in Wuhan and other prefectures. Github (private) - https://github.com/CEIDatUGA/CoronavirusSpatial | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia
  7. Effect of early intervention on outbreak size of COVID-19 in China - http://2019-coronavirus-tracker.com/early-intervention.html - The epidemic of COVID-19 reached different areas of China at different times. This means that different locations were at different phases of outbreak at the time of the Wuhan lockdown (23 January) and other provincial and national actions. This provides what is sometimes called a “natural experiment” becuase it is as if replicate epidemics had been induced and then intervened on at different times. By looking at the effect of timing on outbreak size, we can draw conclusions about the effect of delaying intervention, which may be informative to other countries that are considering taking action. Github - https://github.com/CEIDatUGA/ncov-early-intervention | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia
  8. Effect of mass testing - http://2019-coronavirus-tracker.com/mass_testing.html - A symptom-based mass screening and testing intervention (MSTI) can identify a large fraction of infected individuals during an infectious disease outbreak. China is currently using this strategy for the COVID-19 outbreak. However, MSTI might lead to increased transmission if not properly implemented. We investigate under which conditions MSTI is beneficial. Github (private) - https://github.com/CEIDatUGA/CoV_MassTesting | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia
  9. Epidemic Data Curves, Maps - http://2019-coronavirus-tracker.com/data.html - Github (private) - https://github.com/CEIDatUGA/ncov-data-summary | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia
  10. Nowcasting the current size of the COVID-19 outbreak in the United States - http://2019-coronavirus-tracker.com/nowcast.html - At any given time, most COVID-19 cases are circulating in the community and not known to us. We wish to estimate the total current size of the COVID-19 outbreak (the total number of unnotified individuals currently infected with SARS-CoV2). Github (private) https://github.com/CEIDatUGA/ncov-nowcast, Global and US Parameters http://2019-coronavirus-tracker.com/parameters | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia
  11. Speed of Spread of COVID-19 - http://2019-coronavirus-tracker.com/speed-of-spread.html By US State and Global - Epidemics of COVID-19 are occuring at different times across the United States so it is important to compare the spread of an epidemic in a given state with the appropriate stage in other countries. The following figures show the cumulative number of cases in a state by number of days since the 100th case, number of days since the 1st case, and by calendar date, respectively. Github (private) https://github.com/CEIDatUGA/ncov-data-summary | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia
  12. COVID-19 in Context - http://2019-coronavirus-tracker.com/context.html - How does the 2019 novel coronavirus disease (COVID-19) epidemic compare in severity to other recent disease outbreaks? We gathered data from existing studies to put COVID-19 into context. Github (private) https://github.com/CEIDatUGA/ncov-context | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia
  13. Estimating $R_0$ and other parameters for the 2019-nCov epidemic -The epidemiology of the global 2019-nCov is poorly understood. Identifying the key processes that shape transmission and estimating the relevant model parameters is therefore an important task. This document presents arguments and analysis to support the estimation of a number of key quantities - Epidemic curve, Basic reproduction number ($R_0$), Case detection rate (q), Incubation period ($\frac{1}{\sigma}$), Lag between symptom onset and isolation, Transmissibility ($\beta$), Additional parameters; http://2019-coronavirus-tracker.com/parameters-supplement.html | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia
  14. Estimation of the effective reproduction number of COVID-19 outside China - http://2019-coronavirus-tracker.com/reff-outside.html - What is the average $R_{eff}$ outside of China? | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia; Github https://github.com/CEIDatUGA/ncov-Reff-outside-China
  15. COVID-19 Growth Rate Prediction - http://2019-coronavirus-tracker.com/stochastic.html - We developed a stochastic model to better understand the transmission of 2019-nCov in Hubei (primarily Wuhan). The model includes several features of the Wuhan outbreak that are absent from most compartmental models that otherwise confound the interpretation of data, including time-varying rates of case detection, patient isolation, and case notification. Github - https://github.com/CEIDatUGA/ncov-wuhan-stochastic-model, HTML http://2019-coronavirus-tracker.com/stochastic-model.html | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia
  16. Extended state-space SIR epidemiological models - https://github.com/lilywang1988/eSIR - R package eSIR: extended state-space SIR epidemiological models. The standard SIR model has three components: susceptible, infected, and removed (including the recovery and dead). In the following sections, we will introduce the other extended state-space SIR models and their implementation in the package. The results provided below are based on relatively short chains. | @lilywang1988
  17. JSON time-series of coronavirus cases (confirmed, deaths and recovered) per country - updated daily - https://github.com/pomber/covid19- Transforms the data from CSSEGISandData/COVID-19 into a json file. Available at https://pomber.github.io/covid19/timeseries.json. Updated three times a day using GitHub Actions. | @pomber
  18. Don’t “Flatten the Curve,” squash it!, with simulations Modeling COVID-19 Spread vs Healthcare Capacity - https://alhill.shinyapps.io/COVID19seir/?fbclid=IwAR2aXJT79M2AmZxMdy8jsiEuSC4i7ijU8Av6oB4dmlZIeJ2VQgL7Tt3QGxA - The graph shows the expected numbers of individuals over time who are infected, recovered, susceptible, or dead over time. Infected individuals first pass through an exposed/incubation phase where they are asymptomatic and not infectious, and then move into a symptomatic and infections stage classified by the clinical status of infection (mild, severe, or critical). | Alison Hill, Joscha Bach
  19. R library (coronavirus) https://ramikrispin.github.io/coronavirus/ - Github repo is here https://github.com/RamiKrispin/coronavirus | @RamiKrispin
  20. Coronavirus Simulator - https://www.washingtonpost.com/graphics/2020/world/corona-simulator/, Harry Stevens, Washington Post
  21. Fighting Fatal Coronavirus Using Knowledge Graph - https://community.neo4j.com/t/fighting-fatal-coronavirus-using-knowledge-graph/14634 - uses neo4j http://v.we-yun.com:2020/browser/ | Zhi Zhang from we-yun.com
  22. Using Nebula graph - Detect Corona Virus Spreading With Graph Database Based on a Real Case https://nebula-graph.io/en/posts/detect-corona-virus-spreading-with-graph-database/
  23. Mesa: Agent-based modeling in Python 3+ https://github.com/projectmesa/mesa
  24. COVID-2020 SIR - https://github.com/amita-kapoor/COVID-2020 @amita-kapoor
  25. Evidation Health - https://evidation.com/news/covid-19-pulse-first-data-evidation/ - To understand how Americans are coping with the spread of COVID-19, Evidation Health, the health and measurement company, has launched a nationwide initiative tracking people’s attitudes toward and experiences during the pandemic, alongside their health. Over 140,000 (as of March 22) people from across all 50 states and the District of Columbia have agreed to participate, recruited in less than seven days from the nearly 4 million people who use Evidation’s Achievement app—the largest, most diverse virtual research site in the U.S.
  26. FluTE, an influenza epidemic simulation model - https://www.cs.unm.edu/~dlchao/flute/ - FluTE generates text output files, which can be easily processed with use-supplied scripts. In this example, we import the results from a US simulation into ArcGIS to generate a heatmap showing illness prevalence.
  27. CDC Forecasting - Overview of Models Intervention assumptions and methods for a variety of published, well-known deterministic models. (no code) https://www.cdc.gov/coronavirus/2019-ncov/covid-data/forecasting-us.html
  28. FiveThirtyEight Where The Latest COVID-19 Models Think We're Headed — And Why They Disagree https://projects.fivethirtyeight.com/covid-forecasts/ (no code)

Papers (Some with Code)

  1. Raw data in Wuhan, Hubei, and Guangzhou for serious COVID-19 cases, and Wuhan hospitalization data - https://github.com/c2-d2/COVID-19-wuhan-guangzhou-data for Ruoran, Li, Caitlin Rivers, Qi Tan, Megan B Murray, Eric Toner, and Marc Lipsitch. The Demand for Inpatient and ICU Beds for COVID-19 in the US: Lessons From Chinese Cities (March 2020). https://dash.harvard.edu/handle/1/42599304; data at https://github.com/c2-d2/COVID-19-wuhan-guangzhou-data

  2. Chinazzi, Matteo, et al. "The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak." Science (2020). - https://science.sciencemag.org/content/sci/early/2020/03/05/science.aba9757.full.pdf?casa_token=oH5UDZMQnKoAAAAA:YOfrQGSjNkTOqVmgAjp-O4sbGbdS__ihgi_pr9y_O8E3QyVQlpcvoSkx1Oasp8sz3Ep6Qq49VdlJHzU - Motivated by the rapid spread of COVID-19 in Mainland China, we use a global metapopulation disease transmission model to project the impact of travel limitations on the national and international spread of the epidemic. The model is calibrated based on internationally reported cases, and shows that at the start of the travel ban from Wuhan on 23 January 2020, most Chinese cities had already received many infected travelers.

  3. Mapping hospital demand: demographics, spatial variation, and the risk of “hospital deserts” during COVID-19 in England and Wales - https://osf.io/g8s96/?fbclid=IwAR1hnVIYboDrFL9aZqg-F2js7Bs4JxGf3GT_uYV4KUIApR4kAggP78jsdiE

  4. CHIME (COVID-19 Hospital Impact Model for Epidemics) Application - https://github.com/CodeForPhilly/chime - The CHIME (COVID-19 Hospital Impact Model for Epidemics) Application is designed to assist hospitals and public health officials with understanding hospital capacity needs as they relate to the COVID pandemic. CHIME enables capacity planning by providing estimates of total daily (i.e. new) and running totals of (i.e. census) inpatient hospitalizations, ICU admissions, and patients requiring ventilation. These estimates are generated using a SIR (Susceptible, Infected, Recovered) model, a standard epidemiological modeling technique.

  5. Array Advisors’ Model Validates Fears of ICU Bed Shortage Due to Coronavirus Pandemic - https://array-architects.com/press-release/array-advisors-model-validates-fears-of-icu-bed-shortage-due-to-coronavirus-pandemic/ Array Advisors has built a model that projects the availability of U.S. hospital beds as the coronavirus pandemic grows. The model validates fears that a shortage of beds may occur unless efforts to expand hospital capacity are implemented immediately. Runnable model in Excel.

  6. CoronaTracker: World-wide COVID-19 Outbreak Data Analysis and Prediction CoronaTracker Community Research Group - https://www.who.int/bulletin/online_first/20-255695.pdf CoronaTracker was born as the online platform that provides latest and reliable news development, as well as statistics and analysis on COVID-19. This paper is done by the research team in the CoronaTracker community and aims to predict and forecast COVID19 cases, deaths, and recoveries through predictive modelling. The model helps to interpret patterns of public sentiment on disseminating related health information, and assess political and economic influence of the spread of the virus.

  7. Excess cases of Influenza like illnesses in France synchronous with COVID19 invasion. Pierre-Yves Boëlle1 and the Sentinelles syndromic and viral surveillance group, Sorbonne Université, Institut Pierre Louis d’Epidemiologie et de Santé Publique, Paris, France - https://www.epicx-lab.com/uploads/9/6/9/4/9694133/sentinelles-2020-03-11.pdf

  8. Severe Outcomes Among Patients with Coronavirus Disease 2019 (COVID-19) — United States, February 12–March 16, 2020 - https://www.cdc.gov/mmwr/volumes/69/wr/mm6912e2.htm?s_cid=mm6912e2_w - This first preliminary description of outcomes among patients with COVID-19 in the United States indicates that fatality was highest in persons aged ≥85, ranging from 10% to 27%, followed by 3% to 11% among persons aged 65–84 years, 1% to 3% among persons aged 55-64 years, <1% among persons aged 20–54 years, and no fatalities among persons aged ≤19 years.

  9. Remuzzi, Andrea, and Giuseppe Remuzzi. "COVID-19 and Italy: what next?." The Lancet (2020). - https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30627-9/fulltext?fbclid=IwAR3ke0W7zk58fdCwz_FJGw8VzAiVUveYng6mZmeHPsfBVW5814xlDd_yNgE - The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already taken on pandemic proportions, affecting over 100 countries in a matter of weeks. A global response to prepare health systems worldwide is imperative. Although containment measures in China have reduced new cases by more than 90%, this reduction is not the case elsewhere, and Italy has been particularly affected.

  10. Wu, Ke, et al. "Generalized logistic growth modeling of the COVID-19 outbreak in 29 provinces in China and in the rest of the world." arXiv preprint arXiv:2003.05681 (2020). - https://arxiv.org/pdf/2003.05681.pdf - Background: the COVID-19 has been successfully contained in China but is spreading all over the world. We use phenomenological models to dissect the development of the epidemics in China and the impact of the drastic control measures both at the aggregate level and within each province. We use the experience from China to analyze the calibration results on Japan, South Korea, Iran, Italy and Europe, and make future scenario projections. The datasets generated and analysed during the current study are available in the Github repository, https://github.com/kezida/covid-19-logistic-paper 11.Visual Data Analysis and Simulation Prediction for COVID-19 - Baoquan Chen, Mingyi Shi, Xingyu Ni, Liangwang Ruan, Hongda Jiang, Heyuan Yao, Mengdi Wang, Zhenhua Song, Qiang Zhou, Tong Ge - In this study, we seek to answer a few questions: How did the virus get spread from the epicenter Wuhan city to the rest of the country? To what extent did the measures, such as, city closure and community quarantine, help controlling the situation? More importantly, can we forecast any significant future development of the event had some of the conditions changed? https://arxiv.org/abs/2002.07096, with code https://github.com/NCP-VIS

  11. Peng, Liangrong, et al. "Epidemic analysis of COVID-19 in China by dynamical modeling." arXiv preprint arXiv:2002.06563 (2020). - https://arxiv.org/pdf/2002.06563.pdf Based on the public data of National Health Commission of China from Jan. 20th to Feb. 9th, 2020, we reliably estimate key epidemic parameters and make predictions on the inflection point and possible ending time for 5 different regions.

  12. Li, Ming, Jie Chen, and Youjin Deng. "Scaling features in the spreading of COVID-19." arXiv preprint arXiv:2002.09199 (2020) - https://arxiv.org/pdf/2002.09199.pdf Since the outbreak of COVID-19, many data analysis have been done. Some of them are based on the classical epidemiological approach that assumes an exponential growth, but a few studies report that a power-law scaling may provide a better fitting to the currently available data. Hereby, we examine the epidemic data in China mainland (01/20/2020–02/24/2020) in a log-log scale, and indeed find that the growth closely follows a power-law kinetics over a significantly wide time period.

  13. Biswas, Kathakali, Abdul Khaleque, and Parongama Sen. "Covid-19 spread: Reproduction of data and prediction using a SIR model on Euclidean network." arXiv preprint arXiv:2003.07063 (2020). - https://arxiv.org/pdf/2003.07063.pdf We study the data for the cumulative as well as daily number of cases in the Covid-19 outbreak in China. The cumulative data can be fit to an empirical form obtained from a Susceptible-InfectedRemoved (SIR) model studied on an Euclidean network previously. Plotting the number of cases against the distance from the epicenter for both China and Italy, we find an approximate power law variation with an exponent ∼ 1.85 showing strongly that the spatial dependence plays a key role, a factor included in the model.

  14. Maier, Benjamin F., and Dirk Brockmann. "Effective containment explains sub-exponential growth in confirmed cases of recent COVID-19 outbreak in Mainland China." arXiv preprint arXiv:2002.07572 (2020). - https://arxiv.org/pdf/2002.07572.pdf

  15. Chinazzi, Matteo, et al. "The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak." Science (2020).- https://science.sciencemag.org/content/early/2020/03/05/science.aba9757 - Motivated by the rapid spread of COVID-19 in Mainland China, we use a global metapopulation disease transmission model to project the impact of travel limitations on the national and international spread of the epidemic. | Chinazzi, et al

  16. Li, Qun, et al. "Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia." New England Journal of Medicine (2020) - https://www.nejm.org/doi/full/10.1056/NEJMoa2001316 - We analyzed data on the first 425 confirmed cases in Wuhan to determine the epidemiologic characteristics of NCIP. | Qun Li, et al

  17. Li, Ruiyun, et al. "Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (COVID-19)." medRxiv (2020) - https://www.medrxiv.org/content/medrxiv/early/2020/02/17/2020.02.14.20023127.full.pdf - Estimation of the fraction and contagiousness of undocumented novel coronavirus (COVID-19) infections is critical for understanding the overall prevalence and pandemic potential of this disease. Github - https://github.com/SenPei-CU/COVID-19 | Li, Pen, et al.

  18. Ferguson, Neil M., et al. "Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand." Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand - https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf - Here we present the results of epidemiological modelling which has informed policymaking in the UK and other countries in recent weeks. | Imperial College COVID-19 Response Team, Neil M Ferguson et al.

  19. Li, Ruiyun, et al. "Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2)." Science (2020) https://science.sciencemag.org/content/early/2020/03/13/science.abb3221 - Here we use observations of reported infection within China, in conjunction with mobility data, a networked dynamic metapopulation model and Bayesian inference, to infer critical epidemiological characteristics associated with SARS-CoV2, including the fraction of undocumented infections and their contagiousness. | Li, Ruiyun, et al

  20. Chan, Jasper Fuk-Woo, et al. "A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster." The Lancet 395.10223 (2020): 514-523 https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30154-9/fulltext?fbclid=IwAR1YTPBtlNUrZRvcE9sSBnOzJTOUR8sVK4nc54le5k4xXF3_WvjSuKW5BBU - In this study, we report the epidemiological, clinical, laboratory, radiological, and microbiological findings of five patients in a family cluster who presented with unexplained pneumonia after returning to Shenzhen, Guangdong province, China, after a visit to Wuhan, and an additional family member who did not travel to Wuhan. | Chan, et al.

  21. COVID-19 attack rate increases with city size AJ Stier, MG Berman, L Bettencourt - arXiv preprint arXiv:2003.10376, 2020 - https://arxiv.org/pdf/2003.10376.pdf - Here, we estimate the growth rates and reproductive numbers of COVID-19 in US cities from March 14th through March 19th to reveal a power-law scaling relationship to city population size. This means that COVID-19 is spreading faster on average in larger cities with the additional implication that, in an uncontrolled outbreak, larger fractions of the population are expected to become infected in more populous urban areas.

  22. A time series method to analyze incidence pattern and estimate reproduction number of COVID-19 S Deb, M Majumdar - arXiv preprint arXiv:2003.10655, 2020 - https://arxiv.org/pdf/2003.10655.pdf- In this study, we propose a time series model to analyze the trend pattern of the incidence of COVID-19 outbreak. We also incorporate information on total or partial lockdown, wherever available, into the model. The model is concise in structure, and using appropriate diagnostic measures, we showed that a time-dependent quadratic trend successfully captures the incidence pattern of the disease. We also estimate the basic reproduction number across different countries, and find that it is consistent except for the United States of America.

  23. Early in the epidemic: impact of preprints on global discourse about COVID-19 transmissibility MS Majumder, KD Mandl - The Lancet Global Health, 2020- https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(20)30113-3/fulltext Since it was first reported by WHO in Jan 5, 2020, over 80 000 cases of a novel coronavirus disease (COVID-19) have been diagnosed in China, with exportation events to nearly 90 countries, as of March 6, 2020.1 Given the novelty of the causative pathogen (named SARS-CoV-2), scientists have rushed to fill epidemiological, virological, and clinical knowledge gaps—resulting in over 50 new studies about the virus between January 10 and January 30 alone.2 However, in an era where the immediacy of information has become an expectation of decision makers and the general public alike, many of these studies have been shared first in the form of preprint papers—before peer review.

  24. Eliminating COVID-19: A Community-based Analysis AF Siegenfeld, Y Bar-Yam - arXiv preprint arXiv:2003.10086, 2020 - https://arxiv.org/pdf/2003.10086.pdf We analyze the spread of COVID-19 by considering the transmission of the disease among individuals both within and between communities. A set of communities can be defined as any partition of a population such that travel/social contact within each community far exceeds that between them (e.g. the U.S. could be partitioned by state or commuting zone boundaries). COVID-19 can be eliminated if the community-to-community reproductive number—i.e. the expected/average number off other communities to which a single infected community will transmit the virus—is reduced to less than one. We find that this community-to-community reproductive number is proportional to the travel rate between communities and exponential in the length of the time-delay before communitylevel action is taken.

  25. MAPPING THE LANDSCAPE OF ARTIFICIAL INTELLIGENCE APPLICATIONS AGAINST COVID-19 COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, with over 294,000 cases as of March 22, 2020 (WHO, 2020). In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID-19 crisis at different scales including molecular, medical and epidemiological applications. We finish with a discussion of promising future directions of research and the tools and resources needed to facilitate AI research. https://arxiv.org/pdf/2003.11336.pdf

  26. Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: a modelling study in Hubei, China and northern Italy Anthony Hauser, Michel J Counotte, Charles C Margossian, Garyfallos Konstantinoudis, Nicola Low, Christian L Althaus, Julien Riou. https://www.medrxiv.org/content/10.1101/2020.03.04.20031104v2 Our model accounts for two biases; preferential ascertainment of severe cases and delayed mortality (right-censoring). We fitted our transmission model to surveillance data from Hubei province (1 January to 11 February 2020) and northern Italy (8 February to 3 March 2020). Overall mortality among all symptomatic and asymptomatic infections was estimated to be 3.0% (95% credible interval: 2.6-3.4%) in Hubei province and 3.3% (2.0-4.7%) in northern Italy.

  27. "Exploratory Analysis of Covid-19 Tweets using Topic Modeling, UMAP, and DiGraphs Ordun, Catherine, Sanjay Purushotham, and Edward Raff. arXiv preprint arXiv:2005.03082 (2020). https://arxiv.org/abs/2005.03082 - This paper illustrates five different techniques to assess the distinctiveness of topics, key terms and features, speed of information dissemination, and network behaviors for Covid19 tweets. Reach out to author Catherine Ordun for code, if interested.

  28. Mutations on COVID-19 diagnostic targets Wang, Rui, et al. "Mutations on COVID-19 diagnostic targets." arXiv preprint arXiv:2005.02188 (2020). Effective, sensitive, and reliable diagnostic reagents are of paramount importance for combating the ongoing coronavirus disease 2019 (COVID-19) pandemic at a time there is no preventive vaccine nor specific drug available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It would be an absolute tragedy if currently used diagnostic reagents are undermined in any manner. https://arxiv.org/pdf/2005.02188.pdf

  29. Quantifying Projected Impact of Social Distancing Policies on COVID-19 Outcomes in the US Yang, Chaoqi, et al. "Quantifying Projected Impact of Social Distancing Policies on COVID-19 Outcomes in the US." arXiv preprint arXiv:2005.00112 (2020). - Current social distancing measures to impede COVID-19 (such as shelter-in-place) are economically unsustainable in the long term. Models are needed to understand the implications of possible relaxation options for these measures. We report such models, together with corresponding parameter estimation techniques and prediction outcomes, borrowing insights from another domain; namely, information cascades. https://arxiv.org/pdf/2005.00112.pdf

  30. Human Mobility Trends during the COVID-19 Pandemic in the United States We aim to provide tangible evidence of the human mobility trends by comparing the day-by-day variations across the U.S. Large-scale public mobility at an aggregated level is observed by leveraging mobile device location data and the measures related to social distancing. Our study captures spatial and temporal heterogeneity as well as the sociodemographic variations regarding the pandemic propagation and the non-pharmaceutical interventions. Lee, Minha, et al. "Human Mobility Trends during the COVID-19 Pandemic in the United States." arXiv preprint arXiv:2005.01215 (2020).

Link to Journals via Google Scholar

There are a variety of papers being published every day. Check out the below to keep up to date with the latest articles.

Channels and Social Media

  1. Covid-2019 Reddit Map Community - https://www.reddit.com/r/CovidMapping/

  2. Coronavirus: Why You Must Act Now - https://medium.com/@tomaspueyo/coronavirus-act-today-or-people-will-die-f4d3d9cd99ca

  3. Estimating the Number of Future Coronavirus Cases in the United States - https://towardsdatascience.com/estimating-the-number-of-future-coronavirus-cases-in-the-united-states-a0ce17df029a

  4. https://threadreaderapp.com/thread/1237347774951305216.html, @mlipsitch, with https://github.com/c2-d2/COVID-19-wuhan-guangzhou-data

  5. https://threadreaderapp.com/thread/1238972082756648960.html, @@davidasinclair

  6. Modelling the coronavirus epidemic in a city with Python - https://towardsdatascience.com/modelling-the-coronavirus-epidemic-spreading-in-a-city-with-python-babd14d82fa2

  7. Top 15 R resources on Novel COVID-19 Coronavirus - https://towardsdatascience.com/top-5-r-resources-on-covid-19-coronavirus-1d4c8df6d85f

  8. CoViz: Helping scientists visualize and explore COVID-19 literature with AI - https://medium.com/ai2-blog/coviz-helping-scientists-visualize-and-explore-covid-19-literature-with-ai-9359559368e5

Deep Learning Models

Deep learning approaches, so far focused on computer vision algorithms (i.e. image classification, object detection, instance segmentation) of Chest X-ray and CT scans.

  1. Wang, Yunlu, et al. "Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner." arXiv preprint arXiv:2002.05534 (2020). - https://arxiv.org/pdf/2002.05534.pdf
  2. Xu, Xiaowei, et al. "Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia." arXiv preprint arXiv:2002.09334 (2020). - https://arxiv.org/pdf/2002.09334.pdf
  3. Chen, Jun, et al. "Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study." medRxiv (2020). - https://www.medrxiv.org/content/medrxiv/early/2020/02/26/2020.02.25.20021568.full.pdf
  4. A Deep Convolutional Neural Network for COVID-19 Detection Using Chest X-Rays Bassi, Pedro RAS, and Romis Attux. "A Deep Convolutional Neural Network for COVID-19 Detection Using Chest X-Rays." arXiv preprint arXiv:2005.01578 (2020). https://arxiv.org/pdf/2005.01578.pdf
  5. A Light CNN for detecting COVID-19 from CT scans of the chest Polsinelli, Matteo, Luigi Cinque, and Giuseppe Placidi. "A Light CNN for detecting COVID-19 from CT scans of the chest." arXiv preprint arXiv:2004.12837 (2020). https://arxiv.org/pdf/2004.12837.pdf
  6. A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening Yeh, Chun-Fu, et al. "A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening." arXiv preprint arXiv:2004.12786 (2020). https://arxiv.org/pdf/2004.12786.pdf
  7. Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks Punn, Narinder Singh, and Sonali Agarwal. "Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks." arXiv preprint arXiv:2004.11676 (2020). https://arxiv.org/pdf/2004.11676.pdf
  8. AI Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT Bai, Harrison X., et al. "AI Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT." Radiology (2020): 201491. https://pubs.rsna.org/doi/full/10.1148/radiol.2020201491

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