CovidVisualizeCountry

! THE MOST RECENT WEEKLY SITUATION REPORT, EVERY MONDAY MORNING, HERE:

! Read the Canada COVID-19 epidemic models situation report No 59 - 2022-08-26 here

Combine and visualize predictions of international periodically updated COVID-19 pandemic models

for countries with subnational level estimates

Canada 🇨🇦

and provinces

`NATIONAL`

`Provinces together`

`Alberta` `British Columbia` `Manitoba` `New Brunswick` `Newfoundland and Labrador` `Nova Scotia` `Ontario` `Quebec` `Saskatchewan`

Journal articles published for this work:

.

Pourmalek F. CovidVisualized: Visualized compilation of international updated models' estimates of COVID-19 pandemic at global and country levels. BMC Res Notes. 2022 Apr 9;15(1):136. doi: 10.1186/s13104-022-06020-4. PMID: 35397567.

Publisher || PubMed || PDF

.

Pourmalek F, Rezaei Hemami M, Janani L, Moradi-Lakeh M. Rapid review of COVID-19 epidemic estimation studies for Iran. BMC Public Health. 2021 Feb 1;21(1):257. doi: 10.1186/s12889-021-10183-3. PMID: 33522928

Publisher || PubMed || PDF

.

I. SELECTED GRAPHS FROM LATEST UPTAKE

LATEST UPTAKE: uptake `20220826`

Study update dates in uptake 20220826

DELP 20220721, IHME 20220719, IMPE 20220808, SRIV 20220824

Days old: DELP 37, IHME 39, IMPE 19, SRIV 3

As the IHME and IMPE models’ estimates are released monthly and the DELP and SRIV models’ estimates are released almost biweekly, the uptakes of the current repository are changed from weekly to biweekly.

The only new model update (compared to the previous uptake here) is SRIV 20220824.

Abbreviations used in graphs:

(See Methods and Results for full details.)

DELP: DELPHI. Differential Equations Lead to Predictions of Hospitalizations and Infections. COVID-19 pandemic model named DELPHI developed by Massachusetts Institute of Technology, Cambridge

IHME: Institute for Health Metrics and Evaluation. COVID-19 pandemic model by developed Institute for Health Metrics and Evaluation, Seattle

IMPE: Imperial. COVID-19 pandemic model developed by Imperial College, London

JOHN: Johns Hopkins. Coronavirus resource center, Johns Hopkins University, Baltimore

LANL: Los Alamos National Laboratories. COVID-19 pandemic model developed by Los Alamos National Laboratories, Los Alamos

SRIV: Srivastava, Ajitesh. COVID-19 pandemic model developed by Ajitesh Srivastava, University of Southern California, Los Angeles

Selected graphs - Canada, National

(1) Canada Daily deaths, Reference scenarios, 2020 on

(2) Canada National Daily deaths, All scenarios, 2021 on

(2b) Canada National Daily deaths, All scenarios, 2022 on

(3) Canada National Daily cases or infections, Reference scenarios, 2020 on

(4) Canada National Daily cases or infections, All scenarios, 2021 on

(4b) Canada National Daily cases or infections, All scenarios, 2022 on

(4b2) Canada National Daily cases, All scenarios, DELP and SRIV, 2022 on

(5) Canada National Daily hospital-related outcomes, Reference scenarios, 2020 on

(6) Canada National Daily hospital-related outcomes, without IHME Bed need, Reference scenarios, 2021 on

(7) Canada National Daily Infection hospitalization and fatality ratios, Reference scenario, IHME, 2021 on

(8) Canada National Daily percent change in mobility, IHME, 2020 on

(9) Canada National Daily mask use, IHME, 2020 on

(10) Canada National Percent cumulative vaccinated, IHME, 2020 on

Selected graphs - Canada, Provinces together

(a) Official reports to WHO, via JOHN || (b) Models: IHME

book

(2) Alberta Daily deaths, Reference scenario, 2021 on

(2b) Alberta Daily deaths, Reference scenario, 2022 on, with JOHN raw

(2c) Alberta Daily deaths, Reference scenario, 2022 on, without JOHN raw

book

(3) Alberta Daily cases or infections, Reference scenario, 2020 on

(4) Alberta Daily cases or infections, Reference scenario, 2021 on

(4b) Alberta Daily cases or infections, Reference scenario, 2022 on

book

(4c) Alberta Daily cases, Reference scenario, 2022 on, with JOHN raw

(4d) Alberta Daily cases, Reference scenario, 2022 on, without JOHN raw

book

Selected graphs - British Columbia

(1) British Columbia Daily deaths, Reference scenario, 2020 on

book

(2) British Columbia Daily deaths, Reference scenario, 2021 on

(2b) British Columbia Daily deaths, Reference scenario, 2022 on, with JOHN raw

(2c) British Columbia Daily deaths, Reference scenario, 2022 on, without JOHN raw

book

(3) British Columbia Daily cases or infections, Reference scenario, 2020 on

(4) British Columbia Daily cases or infections, Reference scenario, 2021 on

(4b) British Columbia Daily cases or infections, Reference scenario, 2022 on

book

(4c) British Columbia Daily cases, Reference scenario, 2022 on with JOHN raw

(4d) British Columbia Daily cases, Reference scenario, 2022 on, without JOHN raw

book

Selected graphs - Manitoba

(1) Manitoba Daily deaths, Reference scenario, 2020 on

book

(2) Manitoba Daily deaths, Reference scenario, 2021 on

(2b) Manitoba Daily deaths, Reference scenario, 2022 on, with JOHN raw

(2c) Manitoba Daily deaths, Reference scenario, 2022 on, without JOHN raw

book

(3) Manitoba Daily cases or infections, Reference scenario, 2020 on

(4) Manitoba Daily cases or infections, Reference scenario, 2021 on

(4b) Manitoba Daily cases or infections, Reference scenario, 2022 on

book

(4c) Manitoba Daily cases, Reference scenario, 2022 on, with JOHN raw

(4d) Manitoba Daily cases, Reference scenario, 2022 on, without JOHN raw

book

Selected graphs - Nova Scotia

(1) Nova Scotia Daily deaths, Reference scenario, 2020 on

book

(2) Nova Scotia Daily deaths, Reference scenario, 2021 on

(2b) Nova Scotia Daily deaths, Reference scenario, 2022 on, with JOHN raw

(2c) Nova Scotia Daily deaths, Reference scenario, 2022 on, without JOHN raw

book

(3) Nova Scotia Daily cases or infections, Reference scenario, 2020 on

(4) Nova Scotia Daily cases or infections, Reference scenario, 2021 on

(4b) Nova Scotia Daily cases or infections, Reference scenario, 2022 on

book

(4c) Nova Scotia Daily cases, Reference scenario, 2022 on, with JOHN raw

(4d) Nova Scotia Daily cases, Reference scenario, 2022 on, without JOHN raw

book

Selected graphs - Ontario

(1) Ontario Daily deaths, Reference scenario, 2020 on

book

(2) Ontario Daily deaths, Reference scenario, 2021 on

(2b) Ontario Daily deaths, Reference scenario, 2022 on, with JOHN raw

(2c) Ontario Daily deaths, Reference scenario, 2022 on, without JOHN raw

book

(3) Ontario Daily cases or infections, Reference scenario, 2020 on

(4) Ontario Daily cases or infections, Reference scenario, 2021 on

(4b) Ontario Daily cases or infections, Reference scenario, 2022 on

book

(4c) Ontario Daily cases, Reference scenario, 2022 on, with JOHN raw

(4d) Ontario Daily cases, Reference scenario, 2022 on, without JOHN raw

book

Selected graphs - Quebec

(1) Quebec Daily deaths, Reference scenario, 2020 on

book

(2) Quebec Daily deaths, Reference scenario, 2021 on

(2b) Quebec Daily deaths, Reference scenario, 2022 on, with JOHN raw

(2c) Quebec Daily deaths, Reference scenario, 2022 on, without JOHN raw

book

(3) Quebec Daily cases or infections, Reference scenario, 2020 on

(4) Quebec Daily cases or infections, Reference scenario, 2021 on

(4b) Quebec Daily cases or infections, Reference scenario, 2022 on

book

(4c) Quebec Daily cases, Reference scenario, 2022 on, with JOHN raw

(4d) Quebec Daily cases, Reference scenario, 2022 on, without JOHN raw

book

Selected graphs - Saskatchewan

(1) Saskatchewan Daily deaths, Reference scenario, 2020 on

book

(2) Saskatchewan Daily deaths, Reference scenario, 2021 on

(2b) Saskatchewan Daily deaths, Reference scenario, 2022 on, with JOHN raw

(2c) Saskatchewan Daily deaths, Reference scenario, 2022 on, without JOHN raw

book

(3) Saskatchewan Daily cases or infections, Reference scenario, 2020 on

(4) Saskatchewan Daily cases or infections, Reference scenario, 2021 on

(4b) Saskatchewan Daily cases or infections, Reference scenario, 2022 on

book

(4c) Saskatchewan Daily cases, Reference scenario, 2022 on, with JOHN raw

(4d) Saskatchewan Daily cases, Reference scenario, 2022 on, without JOHN raw

book

II. METHODS AND RESULTS OF THIS WORK

CovidVisualized: Visualized compilation of international updated models’ estimates of COVID-19 pandemic at global and country levels

Farshad Pourmalek, MD PhD

SUMMARY

Objectives: To identify international and periodically updated models of the COVID-19 pandemic, compile and visualize their estimation results at the global and country levels, and periodically update the compilations. When one or more models predict an increase in daily cases or infections and deaths in the next one to three months, technical advisors to the national and subnational decision-makers can consider this early alarm for assessment and suggestion of augmentation of preventive measures and interventions.

Methods and Results: Five international and periodically updated models of the COVID-19 pandemic were identified, created by: (1) Massachusetts Institute of Technology, Cambridge, (2) Institute for Health Metrics and Evaluation, Seattle, (3) Imperial College, London, (4) Los Alamos National Laboratories, Los Alamos, and (5) University of Southern California, Los Angeles. Estimates of these five identified models were gathered, combined, and graphed at global and two country levels. Canada and Iran were chosen as countries with and without subnational estimates, respectively. Compilations of results are periodically updated. Three Github repositories were created that contain the codes and results, i.e., “CovidVisualizedGlobal” for the global and regional levels, “CovidVisualizedCountry” for a country with subnational estimates – Canada, and “covir2” for a country without subnational estimates – Iran.

Keywords: COVID-19, pandemic, epidemic, models, visualization, global, Canada, Iran

BACKGROUND

Objectives and rationale:

The objectives are to identify international and periodically updated models of the COVID-19 epidemic, compile and visualize their estimations’ results at the global and country levels, and periodically update the compilations. The ultimate objective is to provide an early warning system for technical advisors to the decision-makers. When the predictions of one or more models show an increase in daily cases or infections, hospitalizations, or deaths in the next one to three months, technical advisors to the national and subnational decision-makers may consider assessing the situation and suggesting augmentation of non-pharmacologic preventive interventions and vaccinations. No similar work provides visualization of the models’ results in one place and keeps records of the previous updates. This paper describes why and how the CovidVisualized tools were created and how countries can use them. It is possible to create and use such an early warning tool for future surges in the pandemic in a way that is usable by researchers and the technical advisers to policymakers.

METHODS

Eligibility criteria: The criteria for inclusion of target COVID-19 models were (1) an international model scope and (2) periodic updates. “International model” denotes a model that estimates COVID-19 cases or infections and deaths for all countries of the world, with global-level estimates that equate the sum of the national-level estimates. “Periodically updated” denotes a model with a record of periodically updated estimates since its first release, with continued updates in 2021.

Finding the eligible models: The eligible models were found within the literature search of a previous publication, “Rapid review of COVID-19 epidemic estimation studies for Iran” [1]. The results were verified by comparison with models found in a study on “Predictive performance of international COVID-19 mortality forecasting models” [2].

RESULTS

Results are described under the following items: (1) Identified eligible models, (2) The CovidVisualized repositories created in this work, (3) Data management, and (4) Periodical uptakes.

(1) Identified eligible models

Five international and periodically updated models of the COVID-19 pandemic were identified: (1) DELPHI , Massachusetts Institute of Technology, Cambridge (abbreviation used in this work: DELP) [3], (2) Institute for Health Metrics and Evaluation, Seattle (IHME) [4], (3) Imperial College, London (IMPE) [5], (4) Los Alamos National Laboratories, Los Alamos (LANL) [6], (5) University of Southern California, Los Angeles, by Srivastava, Ajitesh (SRIV) [7].

(1) DELP

. DELP = DELPHI: Differential Equations Lead to Predictions of Hospitalizations and Infections
. Citation: COVID Analytics. DELPHI epidemiological case predictions. Cambridge: Operations Research Center, Massachusetts Institute of Technology. https://www.covidanalytics.io/projections
. Study website: https://www.covidanalytics.io/projections
. Estimates web site: https://www.covidanalytics.io/projections, down the page, link that reads, "Download Most Recent Predictions"
. License: https://github.com/COVIDAnalytics/DELPHI/blob/master/LICENSE
. Institution: Operations Research Center, Massachusetts Institute of Technology, Cambridge
. Among articles: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7883965/ , https://www.medrxiv.org/content/10.1101/2020.06.23.20138693v1, https://www.covidanalytics.io/DELPHI_documentation_pdf
. Periodically updated: Yes
. Periodical updates accessible: Yes

(2) IHME

. IHME = Institute for Health Metrics and Evaluation
. Citation: Institute for Health Metrics and Evaluation (IHME). COVID-19 mortality, infection, testing, hospital resource use, and social distancing projections. Seattle: Institute for Health Metrics and Evaluation (IHME), University of Washington. http://www.healthdata.org/covid
. Study web site: http://www.healthdata.org/covid
. Estimates web site: http://www.healthdata.org/covid/data-downloads
. License: http://www.healthdata.org/about/terms-and-conditions
. Institution: IHME, University of Washington, Seattle
. Among articles: https://www.nature.com/articles/s41591-020-1132-9
. Periodically updated: Yes
. Periodical updates accessible: Yes

(3) IMPE

. IMPE = Imperial College
. Citation: MRC Centre for Global Infectious Disease Analysis (MRC GIDA). Future scenarios of the healthcare burden of COVID-19 in low- or middle-income countries. London: MRC Centre for Global Infectious Disease Analysis, Imperial College London. https://mrc-ide.github.io/global-lmic-reports/
. Study web site: https://mrc-ide.github.io/global-lmic-reports/
. Estimates web site: https://mrcdata.dide.ic.ac.uk/global-lmic-reports/ (new), https://github.com/mrc-ide/global-lmic-reports/tree/master/data (old) . License: https://github.com/mrc-ide/global-lmic-reports
. Institution: Imperial College, London
. Among articles: https://science.sciencemag.org/content/369/6502/413
. Periodically updated: Yes
. Periodical updates accessible: Yes

(4) LANL

. LANL = Los Alamos National Laboratories
. Citation: Los Alamos National Laboratory (LANL). COVID-19 cases and deaths forecasts. Los Alamos: Los Alamos National Laboratory (LANL). https://covid-19.bsvgateway.org
. Study web site: https://covid-19.bsvgateway.org
. Estimates web site: https://covid-19.bsvgateway.org, Model Outputs, Global
. License: https://covid-19.bsvgateway.org
. Institution: Los Alamos National Laboratories, Los Alamos
. Among documents: https://covid-19.bsvgateway.org/static/COFFEE-methodology.pdf
. Periodically updated: Yes
. Periodical updates accessible: Yes

(5) SRIV

. SRIV = Srivastava, Ajitesh
. Citation: University of Southern California (USC). COVID-19 forecast. Los Angeles: University of Southern California. https://scc-usc.github.io/ReCOVER-COVID-19
. Study web site: https://scc-usc.github.io/ReCOVER-COVID-19/
. Estimates web site: https://github.com/scc-usc/ReCOVER-COVID-19/tree/master/results/historical_forecasts
. License: https://github.com/scc-usc/ReCOVER-COVID-19/blob/master/LICENSE
. Institution: University of Southern California, Los Angeles
. Among articles: https://arxiv.org/abs/2007.05180
. Periodically updated: Yes
. Periodical updates accessible: Yes

(0) JOHN

. JOHN = Johns Hopkins University. Coronavirus resource center. https://coronavirus.jhu.edu
. Not a model, but a benchmark for comparison.
. Citation: "COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University"
. Study web site: https://coronavirus.jhu.edu
. Estimates web site: https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series , "COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University"
. License: https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series
. Institution: Johns Hopkins University, Baltimore
. Among articles: Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020 May;20(5):533-534. doi: 10.1016/S1473-3099(20)30120-1. Epub 2020 Feb 19. . Periodically updated: Yes
. Periodical updates accessible: Yes

The COVID-19 pandemic model by Youyang Gu [https://covid19-projections.com and https://github.com/youyanggu/covid19_projections] and the model by University of California, Los Angeles model [https://covid19.uclaml.org/info.html and https://github.com/uclaml/ucla-covid19-forecasts/tree/master/current_projection] could not be categorized as international and periodically updated models. The COVID-19 International Modelling Consortium (CoMo Consortium) model, created by researchers at the University of Oxford and Cornell University [https://www.medsci.ox.ac.uk/news/como-consortium-the-covid-19-pandemic-modelling-in-context and https://github.com/ocelhay/como], and CovidSim (COVID Simulation) model, developed by researchers at Imperial College, London [https://covidsim.org/v5.20210727/?place=ca and https://covidsim.org/v5.20210727/?place=ir], provide templates for researchers to model the future of epidemic trajectory at national and subnational levels of their choice, through adjusting the model inputs and setting the time horizon into future for the estimations. Unlike the five international and periodically updated models mentioned above, the latter two models are not intended for periodic updates by their original creators. The CoMo Consortium has engaged some countries, including Iran, but not Canada. There is no evidence of either model being used on a periodically updated basis in Iran or Canada.

(2) The CovidVisualized repositories created in this work

Repositories for codes and data sharing: Three Github repositories were created for this project: “CovidVisualizedGlobal” [8] for the global and regional levels, “CovidVisualizedCountry” [9] for countries with subnational estimates, and “covir2” [10] for countries without subnational estimates. Canada and Iran were chosen for case representation of each of the two types of countries, respectively. These are referred to as CovidVisualized GitHub repositories hereon . Six World Health Organization regions were used for the regional level: African Region (AFR), Americas Region (AMR), Eastern Mediterranean Region (EMR), European Region (EUR), South-East Asian Region (SEAR), and Western Pacific Region (WPR).

Four of the five identified models share codes and estimates updates via GitHub repositories, and the IHME estimates are released on IHME’s website [4].

GitHub repositories allow others to view and/or download, scrutinize, and verify the integrity of the codes and data. It is also possible to minimally modify the codes to recreate similar repositories for any other country that reports COVID-19 cases and deaths to World Health Organization. Such use of the codes and data in GitHub is free of charge and bound to the pertinent licenses.

The three GitHub repositories created in this project are:

. CovidVisualizedGlobal, COVID-19 pandemic estimates at the global level [8] https://github.com/pourmalek/CovidVisualizedGlobal
. CovidVisualizedCountry, COVID-19 pandemic estimates at the country level: Canada [9] https://github.com/pourmalek/CovidVisualizedCountry
. covir2, COVID-19 pandemic estimates at the country level: Iran [10] https://github.com/pourmalek/covir2

(3) Data management

Data management: A template was created to assign comparable variable names to various outcomes from different models. The CovidVisualized methodology document explains the conceptual and computational details of the development of CovidVisualized tools and provides example [11]. Stata SE 14.2 (Stata Statistical Software. StataCorp. College Station, Texas) was used to write and run the codes. Graphs for all types of predicted outcomes, their mean estimates and uncertainty limits, and different scenarios within each model where available are created. IHME and IMPE models have alternative (e.g., “better” and “worse) scenarios besides their reference (aka status quo) scenario. Predictions’ graphs are shown on the pages of the three CovidVisualized GitHub repositories [8-10] and in periodical Situation Reports created with each uptake. The DELP and IHME models provide subnational-level estimates for countries reporting national and subnational level COVID-19 outcomes. Graphs were created for national and subnational-level locations (i.e., provinces in Canada) available in DELP and IHME model outputs.

(4) Periodical uptakes

Periodical uptakes: The two models with the least frequency of periodic updates of estimates are IHME and IMPE, updated almost weekly and bi-weekly, respectively – until November 2021. After the spread of the Omicron variants, these models reduced the frequency of their update releases. Therefore, two sets of arrangements ruled the frequency of performing uptakes in the CovidVisualized tools. The first set covered the year 2021: With the release of each update of either of these two models, the whole set of the five included models are updated in all the three CovidVisualized GitHub repositories. The most recent update of each model is used. The conventions for periodical uptake are described in detail in CovidVisualized methodology document [11]. R software via RStudio 1.4 (Integrated Development for R. RStudio. PBC, Boston, Massachusetts) was used for semi-automatization of the uptakes’ execution. Estimates of the LANL model get updated about every 3-4 days, and DELP and SRIV models get updated daily. The second set of arrangements for the frequency of performing uptakes in the CovidVisualized tools started in 2022. Uptakes are conducted each week on Friday. Each uptake uses the latest available update of each model.

Similar work: The “covidcompare” tool [12] provides graph visualization of the latest estimates of daily and total deaths from international and periodically updated COVID-19 models for countries of the world and US states, along with historical forecasts and model performance, based on IHME’s “Predictive performance of international COVID-19 mortality forecasting models” [2].

LIMITATIONS, STRENGTHS, AND FURTHER DIRECTIONS

Limitations: Limitations of this work include the programming languages, automatization of uptakes, and choice of the website for presentation of the results.

Stata programming language constitutes about 99% of the codes. Whereas Stata is a commercial software package, using non-commercial packages such as R and/or Python can increase the accessibility and adaptability of the codes for other researchers. Further use of R and/or Python can also make the uptakes almost fully automatized. Some health researchers may not be familiar with GitHub and GIT programming. Therefore, additional use of a dedicated website that is more visible to and accessible for the target audience can increase the reach and effect of this work.

Strengths and weaknesses of individual international and periodically updated COVID-19 pandemic models are not mentioned here, but they have been discussed elsewhere [1-2,11].

Strengths: Strengths of this work include usability for informing technical advisors to the decision-makers, adaptability for use in other countries, and automatized data acquisition.

Tested usability for informing technical advisors to the decision-makers at the country level: Results of the GitHub repository “covir2” [15] were used to present the predictions of the five international and periodically updated models of COVID-19 pandemic about the possibility, timing, slope, height, and drivers of a potential fifth wave of the epidemic in Iran. This presentation was done using the results of the covir2 repository along with the results of an e-mail survey of more than 40 epidemiologists and public health specialists. The predictions and results were presented and described in a live online session for four Deputy Ministers of Health and six epidemiologists selected by Iran’s Ministry of Health and Medical Education (MOHME). Periodical situation reports based on each uptake are also shared with MOHME.

Adaptability of the codes for use in other countries or regions in the world: The codes available in GitHub repositories “CovidVisualizedCountry” [9] and “covir2” [10] can be slightly modified by any researcher to be used for countries with and without subnational estimates respectively. See examples for Afghanistan, Pakistan, Japan 20210506, Japan 20210928. “CovidVisualizedCountry” can be adjusted for use for any type of regionalization of the countries / locations of the world, e.g., World Health Organization regions.

Automatized data acquisition: The Stata codes in these repositories automatically download the estimates’ data files from the five included models once executed. There is no additional need for users to locate, download, and edit the estimates’ data of individual models before running the codes. This automatic data acquisition further enhances computational reproducibility – “obtaining consistent results using the same input data; computational steps, methods, and code; and conditions of analysis” [https://doi.org/10.17226/25303].

Further research: Further directions include using the “ensemble” method to statistically combine models’ estimates, and retrospective assessment of models’ predictive performance. In ensemble methods, individual models are evaluated for minimum requirements of quality and reporting. They are statistically combined using specific relative weights for each model, where the weights reflect the comparative accuracy of each model. Such ensemble methods are used by European Centre for Disease Prevention and Control [https://covid19forecasthub.eu/background.html and https://github.com/epiforecasts/covid19-forecast-hub-europe] and US COVID-19 Forecast Hub [https://covid19forecasthub.org/doc/ensemble and https://github.com/reichlab/covid19-forecast-hub]. The ensemble models have been empirically shown to be more accurate than any of the individual models used in the ensemble method [https://www.medrxiv.org/content/10.1101/2021.02.03.21250974v3]. Retrospective assessment of models’ predictive performance includes using statistical and graphical methods to estimate and visualize the accuracy of models’ estimations [2].

DELERATIONS

Ethics approval and consent to participate

All the used and produced data are at the non-individual and aggregate level, publicly available on the Internet, and under pertinent licenses and copyrights for non-commercial use, reproduction, and distribution for scientific research, provided that the conditions mentioned in their respective licenses and copyrights are met. Therefore, no ethics approval or consent to participate was applicable.

Consent for publication

Not applicable.

Availability of data and materials

The data described in this Data Note can be freely and openly accessed on (1) GitHub repository “CovidVisualizedGlobal” under (http://doi.org/10.5281/zenodo.5019030) [8], (2) GitHub repository “CovidVisualizedCountry” under (http://doi.org/10.5281/zenodo.5019482) [9], and (3) GitHub repository “covir2” under (http://doi.org/10.5281/zenodo.5020797) [10]. Please see table 1 and references [8-11] for details and links to the data. No individual patient data was mentioned to be used for modeling in the five models used in this work [13-18]. Third-party data has been used in this study and their relevant attributions are available [19-24] and observed.

Competing interests

The author worked as a post-graduate research fellow in Institute for Health Metrics and Evaluation from 2009 to 2011 and continues voluntary collaboration as a Global Burden of Disease study collaborator without employment or financial relation. The author declares that he has no competing interests.

Funding

There were no sources of funding for this research.

References

Pourmalek F, Rezaei Hemami M, Janani L, Moradi-Lakeh M. Rapid review of COVID-19 epidemic estimation studies for Iran. BMC Public Health. 2021 Feb 1;21(1):257. doi: 10.1186/s12889-021-10183-3. link
Friedman J, Liu P, Troeger CE, Carter A, Reiner RC Jr, et al. Predictive performance of international COVID-19 mortality forecasting models. Nat Commun. 2021 May 10;12(1):2609. doi: 10.1038/s41467-021-22457-w. link
COVID Analytics. DELPHI epidemiological case predictions. Cambridge: Operations Research Center, Massachusetts Institute of Technology. https://www.covidanalytics.io/projections Accessed 23 June 2021.
Institute for Health Metrics and Evaluation (IHME). COVID-19 mortality, infection, testing, hospital resource use, and social distancing projections. Seattle: Institute for Health Metrics and Evaluation (IHME), University of Washington. http://www.healthdata.org/covid/ Accessed 23 June 2021.
MRC Centre for Global Infectious Disease Analysis (MRC GIDA). Future scenarios of the healthcare burden of COVID-19 in low- or middle-income countries. London: MRC Centre for Global Infectious Disease Analysis, Imperial College London. https://mrc-ide.github.io/global-lmic-reports/ Accessed 23 June 2021.
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Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020 May;20(5):533-534. doi: 10.1016/S1473-3099(20)30120-1. Epub 2020 Feb 19. link
Bertsimas D, Boussioux L, Cory-Wright R, Delarue A, Digalakis V, Jacquillat A, et al. From predictions to prescriptions: A data-driven response to COVID-19. Health Care Manag Sci. 2021 Jun;24(2):253-272. doi: 10.1007/s10729-020-09542-0. Epub 2021 Feb 15. link
IHME COVID-19 Forecasting Team. Modeling COVID-19 scenarios for the United States. Nat Med. 2021 Jan;27(1):94-105. doi: 10.1038/s41591-020-1132-9. Epub 2020 Oct 23. link
Walker PGT, Whittaker C, Watson OJ, Baguelin M, Winskill P, Hamlet A, et al. The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries. Science. 2020 Jul 24;369(6502):413-422. doi: 10.1126/science.abc0035. Epub 2020 Jun 12. link
Castro L, Fairchild G, Michaud I, Osthus D. COFFEE: COVID-19 Forecasts using Fast Evaluations and Estimation. https://covid-19.bsvgateway.org/static/COFFEE-methodology.pdf Accessed 23 June 2021.
Srivastava A, Xu T. Fast and accurate forecasting of COVID-19 deaths using the SIkJα model. arXiv:200705180. Submitted on 10 Jul 2020 (v1), last revised 13 Jul 2020 (this version, v2). https://arxiv.org/abs/2007.05180
Johns Hopkins University Center for Systems Science and Engineering. COVID-19 Data Repository. https://github.com/CSSEGISandData/COVID-19 Accessed 23 June 2021.
Li ML, Bouardi HT, Omar, Lami OS, Ghane-Ezabadi M, Soni S. DELPHI: The Epidemiological model underlying COVIDAnalytics. https://github.com/COVIDAnalytics/DELPHI Accessed 23 June 2021.
Institute for Health Metrics and Evaluation. University of Washington. COVID-19 resources. Terms and Conditions. https://www.healthdata.org/about/terms-and-conditions Accessed 23 June 2021.
MRC Centre for Global Infectious Disease Analysis. Department of Infectious Disease Epidemiology at Imperial College London. Global LMIC COVID-19 reports. https://github.com/mrc-ide/global-lmic-reports Accessed 23 June 2021.
Los Alamos National Laboratory. LANL COVID-19 Cases and Deaths Forecasts. https://covid-19.bsvgateway.org Accessed 23 June 2021.
Srivastava A, Xu F, Xiaochen Yang B, Chen J. Data-driven COVID-19 forecasts and detection of unreported cases. https://github.com/scc-usc/ReCOVER-COVID-19 Accessed 23 June 2021.

III. INNER WORKS OF THIS REPOSITORY

The Stata codes can be executed on local machines:

Run in Stata the master do file on your local machine after the downloaded repository directory is stored in the root of /Downloads/ folder of your local machine.

For CovidVisualizedCountry, the master do file is:

CovidVisualizedCountry-main/20220318/code/master/do CovidVisualizedCountry master.do

and the downloaded repository directory is:

/CovidVisualizedCountry-main/

and /20220318/ denotes the date of uptake.

Data management describes the template for models’ output data management used in this repository.

Periodical updates and uptakes describes the rule for periodical uptakes used in this repository.

Bugs and issues describes how to report bugs and issues.

Troubleshooting describes possible difficulties in running the Stata codes on your computer after the repository has been downloaded to your local machine.

uptakes in this repository, since June 2021

bold italic fonts show the uptake was triggered by either IHME or IMPE (before 20211008), or the model updates that are new in this uptake (20211008 and afterwards).

.

(uptake number) uptake date: study update date, study update date | situation report

.

(73) uptake 20220826: DELP 20220721, IHME 20220719, IMPE 20220808, SRIV 20220824 || Days old: DELP 37, IHME 39, IMPE 19, SRIV 3

(72) uptake 20220812: DELP 20220721, IHME 20220719, IMPE 20220808, SRIV 20220728 ||Days old: DELP 23, IHME 25, IMPE 5, SRIV 16

(72) uptake 20220729: DELP 20220719, IHME 20220719, NO IMPE, SRIV 20220728 || Days old: DELP 10, IHME 10, IMPE > 2 weeks, SRIV 1

(71) uptake 20220722: DELP 20220719, IHME 20220719, NO IMPE, SRIV 20220722 || Days old: DELP 3, IHME 3, IMPE > 2 weeks, SRIV 0

(70) uptake 20220715: DELP 20220618, IHME 20220610, IMPE 20220703, SRIV 20220715 || Days old: DELP 28, IHME 36, IMPE 13, SRIV 0

(69) uptake 20220708: DELP 20220618, IHME 20220610, IMPE 20220620, SRIV 20220708 || Days old: DELP 21, IHME 29, IMPE 19, SRIV 0

(68) uptake 20220701: DELP 20220618, IHME 20220610, No IMPE 20220530, SRIV 20220701 || Days old: DELP 14, IHME 22, IMPE 33, SRIV 0

(67) uptake 20220624: DELP 20220618, IHME 20220610, No 20220530, SRIV 20220623 || Days old: DELP 7, IHME 15, IMPE 26, SRIV 1

(66) uptake 20220617: DELP 20220529, IHME 20220610, IMPE 20220530, SRIV 20220617 || Days old: DELP 20, IHME 7, IMPE 19, SRIV 0

(65) uptake 20220610: DELP 20220529, IHME 20220610, No IMPE, SRIV 20220610 || Days old: DELP 13, IHME 0, no IMPE, SRIV 0

(64) uptake 20220603: DELP 20220529, IHME 20220506, No IMPE, SRIV 20220603 || Days old: DELP 5, IHME 29, no IMPE, SRIV 0

(63) uptake 20220527: DELP 20220527, IHME 20220506, No IMPE, SRIV 20220522 || Days old: DELP 0, IHME 14, IMPE 67, SRIV 5

(62) uptake 20220520: DELP 20220520, IHME 20220506, No IMPE, SRIV 20220520 || Days old: DELP 0, IHME 14, IMPE 67, SRIV 0

(61) uptake 20220513: DELP 20220512, IHME 20220506, No IMPE, SRIV 20220508 || Days old: DELP 1, IHME 7, IMPE 60, SRIV 5

(60) uptake 20220506: DELP 20220502, IHME 20220506, IMPE 20220315, SRIV 20220502, WHO 20220505 || Days old: DELP 4, IHME 0, IMPE 53, SRIV 4, WHO 1, No PHAC

(59) uptake 20220429: DELP 20220429, IHME 20220408, no IMPE, SRIV 20220429, No PHAC || Days old: Days old: DELP 0, IHME 21, no IMPE, SRIV 0

(58) uptake 20220422: DELP 20220422, IHME 20220408, no IMPE, SRIV 20220422, No PHAC || Days old: DELP 0, IHME 14, no IMPE, SRIV 0

(57) uptake 20220415: DELP 20220415, IHME 20220408, no IMPE, SRIV 20220413, No PHAC || Days old: DELP 0, IHME 7, no IMPE, SRIV 2

(56) uptake 20220408: DELP 20220408, IHME 20220408, IMPE 20220131, SRIV 20220408 || Days old: DELP 0, IHME 0, IMPE 68, SRIV 0, No PHAC

(55) uptake 20220401: DELP 20220328, IHME 20220322, IMPE 20220131, SRIV 20220401

(54) uptake 20220325: DELP 20220325, IHME 20220322, IMPE 20220120, SRIV 20220325

(53) uptake 20220318: DELP 20220318, IHME 20220318, IMPE 20220120, SRIV 20220318

(52) uptake 20220311: DELP 20220311, IHME 20220218, IMPE 20220120, SRIV 202203111

(51) uptake 20220304: DELP 20220304, IHME 20220218, IMPE 20220120, SRIV 20220301

(50) uptake 20220218: DELP 20220218, IHME 20220218, NO IMPE , NO IMPE

(49) uptake 20220204: DELP 20220204, IHME 20220204, IMPE NO, IMPE NO, PHAC 20220114

(48) uptake 20220128: DELP 20220128, IHME 20220121, IMPE 20220102, SRIV 20220126, PHAC 20220114

(47) uptake 20220121: DELP 20220121, IHME 20220121, IMPE 20220102, SRIV 20220119, PHAC 20220114 | Canada COVID-19 epidemic models situation report No 30 - 2022-01-21

(46) uptake 20220114: DELP 2022014, IHME 20220114, IMPE 20220102, SRIV 20220113, PHAC 20220114 | Canada COVID-19 epidemic models situation report No 29 - 2022-01-14

(45) uptake 20220110: DELP 20220110, IHME 20220110, IMPE 20211213, SRIV 20220110 | Canada COVID-19 epidemic models situation report No 28 - 2022-01-10

(44) uptake 20220104: DELP 20220104, IHME 20211221, IMPE 20211213, SRIV 20220104 | Canada COVID-19 epidemic models situation report No 27 - 2022-01-04

.

(43) uptake 20211221: DELP 20211222, IHME 20211221, IMPE 20211205, SRIV 20211219 | Canada COVID-19 epidemic models situation report No 26 - 2021-12-21

(42) uptake 20211217: DELP 20211216, IHME 20211119, IMPE 20211205, SRIV 20211217 | Canada COVID-19 epidemic models situation report No 25 - 2021-12-17

(41) uptake 20211210: DELP 20211210, IHME 20211119, IMPE 20211129, SRIV 20211210 | Canada COVID-19 epidemic models situation report No 24 - 2021-12-10

(40) uptake 20211203: DELP 20211203, IHME 20211119, IMPE 20211129, SRIV 20211203 | Canada COVID-19 epidemic models situation report No 23 - 2021-12-03

(39) uptake 20211126: *DELP 20211123, IHME 20211119, IMPE 20211115, SRIV 20211126 | Canada COVID-19 epidemic models situation report No 22 - 2021-11-26

(38) uptake 20211119: DELP 20211119, IHME 20211119, IMPE 20211115, SRIV 20211119 | Canada COVID-19 epidemic models situation report No 21 - 2021-11-19

(37) uptake 20211112: DELP 20211112, IHME 20211104, IMPE 20211103, SRIV 20211112 | Canada COVID-19 epidemic models situation report No 20 - 2021-11-12

(36) uptake 20211105: DELP 20211105, IHME 20211104, IMPE 20211027, SRIV 20211105 | Canada COVID-19 epidemic models situation report No 19 - 2021-11-05

(35) uptake 20211029: DELP 20211029, IHME 20211021, IMPE 20211021, SRIV 20211029, PHAC 20211008 | Canada COVID-19 epidemic models situation report No 18 - 2021-10-29

(34) uptake 20211022: DELP 20211019, IHME 20211021, IMPE 20211006, SRIV 20211017, PHAC 20211008 | Canada COVID-19 epidemic models situation report No 17 - 2021-10-19

(33) uptake 20211015: DELP 20211015, IHME 20211015, IMPE 20211006, SRIV 20211015, PHAC 20211008 | Canada COVID-19 epidemic models situation report No 16 - 2021-10-15

(32) uptake 20211008: DELP 20211008, IHME 20211001, IMPE 20210924, LANL 20210926, SRIV 20211008, PHAC 20211008 | Canada COVID-19 epidemic models situation report No 15 - 2021-10-08

(31) uptake 20211001: DELP 20210930, IHME 20211001, IMPE 20210924, LANL 20210926, SRIV 20210930, PHAC 20210903 | Canada COVID-19 epidemic models situation report No 14 - 2021-10-01

(30) uptake 20210928: DELP 20210927, IHME 20210923, IMPE 20210924, LANL 20210926, SRIV 20210928, PHAC 20210903 | Canada COVID-19 epidemic models situation report No 13 - 2021-09-28

(29) uptake 20210923: DELP 20210923, IHME 20210923, IMPE 20210909, LANL 20210919, SRIV 20210923, PHAC 20210903 | Canada COVID-19 epidemic models situation report No 12 - 2021-09-23

(28) uptake 20210920: DELP 20210920, IHME 20210916, IMPE 20210909, LANL 20210919, SRIV 20210920, PHAC 20210903 | Canada COVID-19 epidemic models situation report No 11 - 2021-09-20

(27) uptake 20210916: DELP 20210916, IHME 20210916, IMPE 20210825, LANL 20210912, SRIV 20210916, PHAC 20210903 | Canada COVID-19 epidemic models situation report No 10 - 2021-09-16

(26) uptake 20210910: DELP 20210910, IHME 20210910, IMPE 20210825, LANL 20210905, SRIV 20210910, PHAC 20210903 | Canada COVID-19 epidemic models situation report No 09 - 2021-09-10

(25) uptake 20210903: DELP 20210903, IHME 20210902, IMPE 20210825, LANL 20210829, SRIV 20210903, PHAC 20210903 | Canada COVID-19 epidemic models situation report No 08 - 2021-09-03

(24) uptake 20210902: DELP 20210902, IHME 20210902, IMPE 20210825, LANL 20210829, SRIV 20210902 | Canada COVID-19 epidemic models situation report No 07 - 2021-09-02

(23) uptake 20210901: DELP 20210901, IHME 20210826, IMPE 20210825, LANL 20210829, SRIV 20210901 | Canada COVID-19 epidemic models situation report No 06 - 2021-09-01

(22) uptake 20210826: DELP 20210826, IHME 20210826, IMPE 20210819, LANL 20210822, SRIV 20210826 | Canada COVID-19 epidemic models situation report No 05 - 2021-08-28

(21) uptake 20210824: DELP 20210824, IHME 20210819, IMPE 20210819, LANL 20210822, SRIV 20210824 | Canada COVID-19 epidemic models situation report No 04 - 2021-08-24

(20) uptake 20210819: DELP 20210819, IHME 20210819, IMPE 20210806, LANL 20210815, SRIV 20210819 | Canada COVID-19 epidemic models situation report No 03 - 2021-08-23

(19) uptake 20210813: DELP 20210813, IHME 20210806, IMPE 20210806, LANL 20210808, SRIV 20210813

(18) uptake 20210806: DELP 20210806, IHME 20210806, IMPE 20210719, LANL 20210801, SRIV 20210801

(17) uptake 20210730: DELP 20210730, IHME 20210730, IMPE 20210719, LANL 20210725, SRIV 20210730 | Canada COVID-19 epidemic models situation report No 02 - 2021-08-01

(16) uptake 20210727: DELP 20210727, IHME 20210723 version 2, IMPE 20210719, LANL 20210725, SRIV 20210727

(15) uptake 20210726: DELP 20210726, IHME 20210723 version 2, IMPE 20210709, LANL 20210718, SRIV 20210726 | COVID-19 epidemic models situation report No 01 - 2021-07-31

(14) uptake 20210723: DELP 20210723, IHME 20210723, IMPE 20210709, LANL 20210718, SRIV 20210723

(13) uptake 20210715: DELP 20210715, IHME 20210715, IMPE 20210709, LANL 20210711, SRIV 20210715

(12) uptake 20210714: DELP 20210714, IHME 20210702, IMPE 20210709, LANL 20210711, SRIV 20210714

(11) uptake 20210709: DELP 20210708, IHME 20210702, IMPE 20210702, LANL 20210704, SRIV 20210709

(10) uptake 20210704: DELP 20210704, IHME 20210702, IMPE 20210626, LANL 20210704, SRIV 20210704

(09) uptake 20210703: DELP 20210703, IHME 20210702, IMPE 20210618, LANL 20210627, SRIV 20210703

(08) uptake 20210625: DELP 20210625, IHME 20210625, IMPE 20210618, LANL 20210613, SRIV 20210624

(07) uptake 20210624: DELP 20210624, IHME 20210618, IMPE 20210618, LANL 20210613, SRIV 20210624

(06) uptake 20210618: DELP 20210618, IHME 20210618, IMPE 20210611, LANL 20210613, SRIV 20210618

(05) uptake 20210611: DELP 20210611, IHME 20210610, IMPE 20210611, LANL 20210606, SRIV 20210611

(04) uptake 20210610: DELP 20210610, IHME 20210610, IMPE 20210604, LANL 20210606, SRIV 20210610

(03) uptake 20210605: DELP 20210603, IHME 20210604, IMPE 20210604, LANL 20210602, SRIV 20210604

(02) uptake 20210604: DELP 20210604, IHME 20210604, IMPE 20210527, LANL 20210602, SRIV 20210604

(01) uptake 20210603: DELP 20210604, IHME 20210604, IMPE 20210527, LANL 20210602, SRIV 20210604

IV. SELECTED GRAPHS FROM PREVIOUS UPTAKES

Selected graphs from previous uptakes are stored in the followingweb pages:

RESULTS Alberta 2021

RESULTS Alberta 2022

RESULTS British Columbia 2021

RESULTS British Columbia 2022

RESULTS CANADA, national 2021

RESULTS CANADA, national 2022

RESULTS CANADA, provinces 2021

RESULTS CANADA, provinces 2022

RESULTS Manitoba 2021

RESULTS Manitoba 2022

RESULTS New Brunswick 2021

RESULTS New Brunswick 2022

RESULTS Newfoundland and Labrador 2021

RESULTS Newfoundland and Labrador 2022

RESULTS Northwest Territories 2021

RESULTS Northwest Territories 2022

RESULTS Nova Scotia 2021

RESULTS Nova Scotia 2022

RESULTS Prince Edward Island 2021

RESULTS Prince Edward Island 2022

RESULTS Quebec 2021

RESULTS Quebec 2022

RESULTS Saskatchewan 2021

RESULTS Saskatchewan 2022

RESULTS Yukon 2021

RESULTS Yukon 2022

Licenses / Copyrights of data and / or graphs used in this repository:

All the data and / or graphs used in this repository are at non-individual and aggregate level, publicly available on the Internet, and under pertinent licenses and copyrights for non-commercial use, reproduction, and distribution for scientific research, provided that the conditions mentioned in the respective licenses and copyrights are met, as referred to below.

.

(1) ABBREVIATED NAME IN THIS REPOSITORY: DELP

CITATION: COVID Analytics. DELPHI epidemiological case predictions. Cambridge: Operations Research Center, Massachusetts Institute of Technology. https://www.covidanalytics.io/projections and https://github.com/COVIDAnalytics/website/tree/master/data/predicted

SOURCE REPOSITORY: https://github.com/COVIDAnalytics/DELPHI

SOURCE REPOSITORY LICENCE: https://github.com/COVIDAnalytics/website/blob/master/LICENSE

.

(2) ABBREVIATED NAME IN THIS REPOSITORY: IHME

CITATION: Institute for Health Metrics and Evaluation (IHME). COVID-19 mortality, infection, testing, hospital resource use, and social distancing projections. Seattle: Institute for Health Metrics and Evaluation (IHME), University of Washington. http://www.healthdata.org/covid/ and http://www.healthdata.org/covid/data-downloads

SOURCE REPOSITORY: http://www.healthdata.org/covid/data-downloads

SOURCE REPOSITORY LICENCE: http://www.healthdata.org/about/terms-and-conditions

.

(3) ABBREVIATED NAME IN THIS REPOSITORY: IMPE

CITATION: MRC Centre for Global Infectious Disease Analysis (MRC GIDA). Future scenarios of the healthcare burden of COVID-19 in low- or middle-income countries. London: MRC Centre for Global Infectious Disease Analysis, Imperial College London. https://mrc-ide.github.io/global-lmic-reports/ and https://github.com/mrc-ide/global-lmic-reports/tree/master/data

SOURCE REPOSITORY: https://github.com/mrc-ide/global-lmic-reports/tree/master/data

SOURCE REPOSITORY LICENCE: https://mrc-ide.github.io/global-lmic-reports/

.

(4) ABBREVIATED NAME IN THIS REPOSITORY: LANL

CITATION: Los Alamos National Laboratory (LANL). COVID-19 cases and deaths forecasts. Los Alamos: Los Alamos National Laboratory (LANL). https://covid-19.bsvgateway.org

SOURCE REPOSITORY: https://covid-19.bsvgateway.org

SOURCE REPOSITORY LICENCE: https://covid-19.bsvgateway.org

.

(5) ABBREVIATED NAME IN THIS REPOSITORY: SRIV

CITATION: Srivastava, Ajitesh. University of Southern California (USC). COVID-19 forecast. Los Angeles: University of Southern California. https://scc-usc.github.io/ReCOVER-COVID-19 and https://github.com/scc-usc/ReCOVER-COVID-19/tree/master/results/historical_forecasts

SOURCE REPOSITORY: https://github.com/scc-usc/ReCOVER-COVID-19/tree/master/results/historical_forecasts

SOURCE REPOSITORY LICENCE: https://github.com/scc-usc/ReCOVER-COVID-19/blob/master/LICENSE

.

(6) ABBREVIATED NAME IN THIS REPOSITORY: JOHN

CITATION: "COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University" https://coronavirus. jhu.edu/map.html and https://github.com/CSSEGISandData/COVID-19

SOURCE REPOSITORY: https://github.com/CSSEGISandData/COVID-19

SOURCE REPOSITORY LICENCE: https://github.com/CSSEGISandData/COVID-19

.

(7) ABBREVIATED NAME IN THIS REPOSITORY: covidcompare

CITATION: Friedman J, Liu P, Akre S. The covidcompare tool. https://covidcompare.io/about

SOURCE REPOSITORY: https://covidcompare.io/

SOURCE REPOSITORY LICENCE: https://covidcompare.io/about

.

License, DOI, and suggested Citation of this reposirory

All codes are copyrighted by the author under Apache License 2.0.

See LICENSE

DOI

Pourmalek, F. GitHub repository “CovidVisualizedCountry”: Combine and visualize international periodically updating estimates of COVID-19 pandemic at the global level. Version 1.1, Released June 23, 2021. https://doi.org/10.5281/zenodo.5019482 , https://github.com/pourmalek/CovidVisualizedCountry

Name		Name	Last commit message	Last commit date
Latest commit History 8,250 Commits
.github/workflows		.github/workflows
20210603		20210603
20210604		20210604
20210605		20210605
20210610		20210610
20210611		20210611
20210618		20210618
20210624		20210624
20210625		20210625
20210703		20210703
20210704		20210704
20210709		20210709
20210714		20210714
20210715		20210715
20210723		20210723
20210726		20210726
20210727		20210727
20210730		20210730
20210806		20210806
20210813		20210813
20210819		20210819
20210824		20210824
20210826		20210826
20210901		20210901
20210902		20210902
20210903		20210903
20210910		20210910
20210916		20210916
20210920		20210920
20210923		20210923
20210928		20210928
20211001		20211001
20211008		20211008
20211015		20211015
20211022		20211022
20211029		20211029
20211105		20211105
20211112		20211112
20211119		20211119
20211126		20211126
20211203		20211203
20211210		20211210
20211217		20211217
20211221 JOHN		20211221 JOHN
20211221		20211221
20211231		20211231
20220104		20220104
20220110		20220110
20220114		20220114
20220121		20220121
20220128		20220128
20220204		20220204
20220210 JOHN		20220210 JOHN
20220218		20220218
20220225 JOHN		20220225 JOHN
20220304 JOHN		20220304 JOHN
20220304		20220304
20220311		20220311
20220318		20220318
20220325		20220325
20220401		20220401
20220408		20220408
20220415		20220415
20220422		20220422
20220429		20220429
20220506		20220506
20220513		20220513
20220520		20220520
20220527		20220527
20220603		20220603
20220610		20220610
20220617		20220617
20220624		20220624
20220701		20220701
20220708		20220708
20220715		20220715
20220722		20220722
20220729		20220729
20220812		20220812
20220826		20220826
ADAPTATIONS_EXAMPLES		ADAPTATIONS_EXAMPLES
RESULTS Alberta 2021		RESULTS Alberta 2021
RESULTS Alberta 2022		RESULTS Alberta 2022
RESULTS British Columbia 2021		RESULTS British Columbia 2021
RESULTS British Columbia 2022		RESULTS British Columbia 2022
RESULTS CANADA, national 2021		RESULTS CANADA, national 2021
RESULTS CANADA, national 2022		RESULTS CANADA, national 2022
RESULTS CANADA, provinces 2021		RESULTS CANADA, provinces 2021
RESULTS CANADA, provinces 2022		RESULTS CANADA, provinces 2022
RESULTS Manitoba 2021		RESULTS Manitoba 2021
RESULTS Manitoba 2022		RESULTS Manitoba 2022
RESULTS New Brunswick 2021		RESULTS New Brunswick 2021
RESULTS New Brunswick 2022		RESULTS New Brunswick 2022
RESULTS Newfoundland and Labrador 2021		RESULTS Newfoundland and Labrador 2021
RESULTS Newfoundland and Labrador 2022		RESULTS Newfoundland and Labrador 2022
RESULTS Northwest Territories 2021		RESULTS Northwest Territories 2021
RESULTS Northwest Territories 2022		RESULTS Northwest Territories 2022
RESULTS Nova Scotia 2021		RESULTS Nova Scotia 2021
RESULTS Nova Scotia 2022		RESULTS Nova Scotia 2022
RESULTS Nunavut 2021		RESULTS Nunavut 2021

License

pourmalek/CovidVisualizedCountry

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