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Datasets

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Cumulative and individual case data:

 

  1. Johns Hopkins data repository: https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data

  2. Individual case data from Singapore: https://co.vid19.sg/cases

  3. Summary of case lists/line lists for different countries: https://github.com/midas-network/COVID-19/wiki/Data-catalog#cases

  4. Case numbers for different countries: https://www.worldometers.info/coronavirus/

  5. Real-time fetching of data: https://github.com/datasets/covid-19

  6. Another global case summary: https://coronavirus-data.com/ 

  7. Global repository overview: https://github.com/stevenliuyi/covid19

  8. Case numbers Switzerland: https://github.com/openZH/covid_19 and https://www.corona-data.ch/

  9. Individual case data India: https://docs.google.com/spreadsheets/d/e/2PACX-1vSc_2y5N0I67wDU38DjDh35IZSIS30rQf7_NYZhtYYGU1jJYT6_kDx4YpF-qw0LSlGsBYP8pqM_a1Pd/pubhtml

  10. https://www.corona-data.ch

  11. Visualising JH data: https://rpruim.github.io/ds303/S20/hw/covid-19/covid-19.html

  12. Case data/SIR models Brazil: http://covid19br.org/ and https://github.com/cidacslab/Mathematical-and-Statistical-Modeling-of-COVID19-in-Brazil

 

Data on test numbers in different countries:

 

  1. https://ourworldindata.org/coronavirus (CSV files under “Current COVID-19 test coverage estimates”)

  2. https://en.wikipedia.org/wiki/COVID-19_testing

  3. https://docs.google.com/spreadsheets/u/2/d/e/2PACX-1vRwAqp96T9sYYq2-i7Tj0pvTf6XVHjDSMIKBdZHXiCGGdNC0ypEU9NbngS8mxea55JuCFuua1MUeOj5/pubhtml#

  4. https://github.com/COVID19Tracking

 

Please note that the number of confirmed cases also depends on the underlying sampling process (i.e., infection tests). You may decide to incorporate the “tests per capita” in your models to get better predictions and statistics of estimates. For more information on unreported cases, see Li, Ruiyun, et al. "Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (COVID-19)." Science (2020).

 

Please also note that behavioral changes (e.g., no handshakes, social distancing, quarantine, etc.) influence the spreading parameters and outbreak dynamics.

 

Local interactions, social networks, and travel activities also have a large influence on the resulting spreading dynamics (see e.g., https://www.nytimes.com/interactive/2020/03/22/world/coronavirus-spread.html).

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Mobility data:

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  1. Mobility data Italy: https://github.com/pcm-dpc/COVID-19

  2. Mobility estimates for different regions: https://citymapper.com/CMI

  3. Mobility data Switzerland: https://statistik.zh.ch/internet/justiz_inneres/statistik/de/aktuell/mitteilungen/2020/covid_mobilitaetsverhalten.html

  4. Additional mobility data Switzerland: https://ivtmobis.ethz.ch/mobis/covid19/reports/mobis_covid19_report_2020-06-04.html

  5. https://github.com/ActiveConclusion/COVID19_mobility

 

It might be important to account for spatial and surface-contact effects (see van Doremalen, Neeltje, et al. "Aerosol and surface stability of HCoV-19 (SARS-CoV-2) compared to SARS-CoV-1." medRxiv (2020)). Mean-field models and representative-agent models may lead to inaccuracies.

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Kaggle data:

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We also recommend UCLA's COVID-19 dataset clearinghouse as an excellent summary of various datasets and literature.

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Excess mortality data:

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  1. Historical and current mortality data Italy: https://www.istat.it/it/archivio/240401

  2. Mortality monitor Spain: https://momo.isciii.es/public/momo/dashboard/momo_dashboard.html\#datos

  3. Historical and current mortality data Switzerland: https://www.bfs.admin.ch/bfs/de/home/statistiken/gesundheit/gesundheitszustand/sterblichkeit-todesursachen.html

  4. Death statistics England/Wales: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/weeklyprovisionalfiguresondeathsregisteredinenglandandwales

  5. National Center for Health Statistics Mortality Surveillance System: https://gis.cdc.gov/grasp/fluview/mortality.html

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Government response data:

1. https://covidtracker.bsg.ox.ac.uk

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