**! Read Iran COVID-19 epidemic models situation report No 70 - 2022-08-26 here

Journal articles published for this work:

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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

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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

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Dear Dr. Pourmalak,

Thank you very much indeed! The situation reports that you prepared is a very well prepared summary document on epidemic of covid-19 in Iran, for all policymakers and researchers.

Regards

Farid Najafi MD PhD

Epidemiologist

Deputy Minister of Research and Technology

• Project: Combine and visualize predictions of international periodically updated COVID-19 pandemic models for countries without subnational level estimates: Iran

• Person: Farshad Pourmalek (pourmalek_farshad at yahoo dot com)

• Time (initial): 2021-02-10

• Research Article

covir2 Combine and visualize predictions of international periodically updated COVID-19 pandemic models for countries without subnational level estimates: Iran

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CovidVisualizedCountry Combine and visualize predictions of international periodically updated COVID-19 pandemic models for countries with subnational level estimates: Canada

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CovidVisualizedGlobal Combine and visualize predictions of international periodically updated COVID-19 pandemic models for the global level and six WHO regions

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CovidVisualizedMethodology CovidVisualized Methodology Documents for 3 repositories above

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CovidLongitudinal Longitudinal assessment of international periodically updating COVID-19 pandemic studies // work in progress

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CovidVisualizedEnsemble Ensemble modeling of international periodically updating COVID-19 pandemic studies // work in progress

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

IHME: Institute for Health Metrics and Evaluation. COVID-19 pandemic model by developed Institute for Health Metrics and Evaluation, Seattle. https://covid19.healthdata.org/global?view=cumulative-deaths&tab=trend

IMPE: Imperial. COVID-19 pandemic model developed by Imperial College, London. https://mrc-ide.github.io/global-lmic-reports/

LANL: Los Alamos National Laboratories. COVID-19 pandemic model developed by Los Alamos National Laboratories, Los Alamos. https://covid-19.bsvgateway.org

SRIV: Srivastava, Ajitesh. COVID-19 pandemic model developed by Ajitesh Srivastava, University of Southern California, Los Angeles. https://scc-usc.github.io/ReCOVER-COVID-19/#/

JOHN: Johns Hopkins. The coronavirus resource center, Johns Hopkins University, Baltimore. https://www.arcgis.com/apps/dashboards/bda7594740fd40299423467b48e9ecf6 (Official reports of the countries to the World Health Organization; benchmark)

(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

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.

These are for documentation in history and future generations, those who and if have remained. Someday some intelligent people will read.

I informed the Ministry of Health of Iran about these reports of COVID-19 epidemic models for Iran. They listened and went on their deficient way.

Not to forget:

(1) China reacted with the lowest speed, releasing the virus to the world. WHO reacted reluctantly. The world nagged and then let it go.

(2) The course of events showed that the function of the Ministers of Health and Public Health Officers (and their equivalent positions, e.g., Health Deputy Ministers) indicated that their decisions are primarily driven by POLITICS rather than PUBLIC HEALTH. This is particularly true about Iran and Canada. Iran deliberately chose free propagation of virus for a delusional objective of herd immunity.

(3) Epidemic models for COVD-19 are generally weak (i.e., of low predictive validity), and the emergence of newer variants practically makes them flail until the new wave has started to rise. I have looked at this, and the manuscript will be ready in future, with codes and details to be placed here: https://github.com/pourmalek/CovidLongitudinal

(4) The available vaccines do not substantially reduce the spread of the virus. This puts evolutionary pressure on the virus for the emergence of newer immune-evasive variants that are more spreadable and / or fatal. The problem will remain without vaccines that stop transmission and without effective suppression of airborne propagation. These are necessary components of the solution but not a sufficient collection of components.

(6) Governments have chosen to set the current and ongoing (low or high) levels of morbidity and mortality of the people to preserve the economy and stability. People have regressed into personal and family survival mode. Numerable individuals speak, and no one listens. Environmental degradation (consequences of which includes the infamous global warming) is an existential threat for Homo sapiens, and the causes and solutions have been explained since the 1970s.

Among the Eastern Mediterranean countries, only Tunisia had more COVID-19 deaths than Iran in July 2002.

Graph (4) EMR Daily reported deaths, EMR countries, Johns Hopkins, June 2022 on, without extremes

(0) Iran Daily reported deaths, JOHN, all times

(0b) Iran Daily reported deaths, JOHN, 2022

(0c) Iran Daily reported deaths, JOHN, 2022 June on

(1) Iran Daily deaths, all time

(2) Iran Daily deaths, 2021 on

(3) Iran Daily deaths, 2021 on, reference scenario with uncertainty, IHME

(4) Iran Daily deaths, 2021 on, all scenarios, IHME

(5) Iran Deaths, Iran, IMPE, 2021 on, 3 scenarios

(6b) Iran Daily deaths, 2022 on, reference scenarios

(00) Iran Daily reported cases, JOHN, all times

(00b3) Iran Daily reported cases, JOHN, 2022

(00c) Iran Daily reported cases, JOHN, 2022 June on

(7) Iran Daily cases or infections, all time

(8) Iran Daily cases or infections, 2021 on

(8b) Iran Daily cases, 2021 on

(8b1) Iran Daily cases JOHN, and infections IMPE, Iran, 2021 on

(8b2) Iran Daily cases, 2022 on

(8c) Iran Daily estimated infections IHME to reported cases JOHN, main scenarios, 2021 on

(9) Iran Hospital-related outcomes, all time

(10) Iran Hospital-related outcomes, 2021 on

(11) Iran Daily deaths estimated to reported, all time

(12) Iran Daily cases or infections estimated to reported cases, 2021 on

IHME graphs

(13) Iran R effective, 2 scenarios, 2021 on, IHME

(14) Iran Daily Infection-outcome ratios, 2 scenarios, 2021 on, IHME

(15) Iran Daily mobility, 2 scenarios, all time, IHME

(16) Iran Daily mask use, 2 scenarios, all time, IHME

(17) Iran Percent cumulative vaccinated, 2021 on, IHME

AFG: Afghanistan; ARE: United Arab Emirates; BHR: Bahrain; DJI: Djibouti; EGY: Egypt; EMR: EMRO; IRN: Iran; IRQ: Iraq; JOR: Jordan; KWT: Kuwait; LBN: Lebanon; LBY: Libya; MAR: Morocco; OMN: Oman; PAK: Pakistan; PSE: Palestine; QAT: Qatar; SAU: Saudi Arabia; SDN: Sudan; SOM: Somalia; SYR: Syria; TUN: Tunisia; YEM: Yemen

(b1) Eastern Mediterranean Region (EMR), official country reports (JOHN)

(1) EMR Daily reported deaths, EMR countries, Johns Hopkins, 2022

(2) EMR Daily reported deaths, EMR countries, Johns Hopkins, 2022, without extremes

(3) EMR Daily reported deaths, EMR countries, Johns Hopkins, June 2022 on

(4) EMR Daily reported deaths, EMR countries, Johns Hopkins, June 2022 on, without extremes

(5) EMR Daily reported cases, EMR countries, Johns Hopkins, 2022

(6) EMR Daily reported cases, EMR countries, Johns Hopkins, 2022, without extremes

(7) EMR Daily reported cases, EMR countries, Johns Hopkins, June 2022 on

(b2) Eastern Mediterranean Region (EMR), IHME model

(8) EMR Daily deaths, EMR countries, IHME, 2022

(9) EMR Daily deaths, EMR countries, IHME, 2022, Forecast only

(10) EMR Daily infections, EMR countries, IHME, 2022

(11) EMR Daily infections, EMR countries, IHME, 2022, Forecast only

(12) EMR Daily infections, EMR countries, IHME, 2022, Forecast only, without extremes

(13) EMR Daily infections, EMR countries, IHME, 2022, Forecast only, without more extremes

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. https://www.healthdata.org/covid
. Study web site: https://www.healthdata.org/covid
. Estimates web site: https://www.healthdata.org/covid/data-downloads
. License: https://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.

. 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 (https://doi.org/10.5281/zenodo.5019030) [8], (2) GitHub repository “CovidVisualizedCountry” under (https://doi.org/10.5281/zenodo.5019482) [9], and (3) GitHub repository “covir2” under (https://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.

1. 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

2. 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

3. COVID Analytics. DELPHI epidemiological case predictions. Cambridge: Operations Research Center, Massachusetts Institute of Technology. https://www.covidanalytics.io/projections Accessed 23 June 2021.

4. 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. https://www.healthdata.org/covid/ Accessed 23 June 2021.

5. 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.

6. Los Alamos National Laboratory (LANL). COVID-19 cases and deaths forecasts. Los Alamos: Los Alamos National Laboratory (LANL). https://covid-19.bsvgateway.org Accessed 23 June 2021.

7. 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 Accessed 23 June 2021.

8. Pourmalek, F. pourmalek/CovidVisualizedGlobal: 1.1 public release. 2021. Zenodo. https://doi.org/10.5281/zenodo.5019030 Accessed 23 June 2021. link

9. Pourmalek, F. pourmalek/CovidVisualizedCountry: 1.1 public release. 2021. Zenodo. https://doi.org/10.5281/zenodo.5019482 Accessed 23 June 2021. link

10. Pourmalek, F. pourmalek/covir2: 2.2 public release. 2021. Zenodo. https://doi.org/10.5281/zenodo.5020797 Accessed 23 June 2021. link

11. Pourmalek, F. CovidVisualized Methodology Document. Methodology document for CovidVisualized tools: CovidVisualizedGlobal, CovidVisualizedCountry, and covir2. Zenodo. https://doi.org/10.5281/zenodo.6371475 Accessed 10 March 2021. [link]https://github.com/pourmalek/CovidVisualizedMethodology()

12. Friedman J, Liu P, Akre S. The covidcompare tool. https://covidcompare.io/about Accessed 23 June 2021.

13. 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

14. 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

15. 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

16. 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

17. 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.

18. 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

19. Johns Hopkins University Center for Systems Science and Engineering. COVID-19 Data Repository. https://github.com/CSSEGISandData/COVID-19 Accessed 23 June 2021.

20. 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.

21. 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.

22. 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.

23. Los Alamos National Laboratory. LANL COVID-19 Cases and Deaths Forecasts. https://covid-19.bsvgateway.org Accessed 23 June 2021.

24. 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.

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 covir2, the master do file is:

covir2-main/20220318/code/master/do country master.do

and the downloaded repository directory is:

/covir2-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.

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

.

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

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

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

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

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

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

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

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

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

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

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

(75) uptake 20220527: DELP 20220527, IHME 20220506, No IMPE, SRIV 20220522 || Days old: DELP 0, IHME 21, no IMPE, SRIV 5

(74) uptake 20220520: DELP 20220520, IHME 20220506, No IMPE, SRIV 20220520 || Days old: DELP 1, IHME 7, IMPE 60, SRIV 5

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

(72) uptake 20220506: DELP 20220502, IHME 20220506, IMPE 20220315, SRIV 20220502, WHO 20220505 || Days old: DELP 4, IHME 0, IMPE 53, SRIV 4, WHO 1

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

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

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

(68) uptake 20220408: DELP 20220408, IHME 20220322, IMPE 20220131, SRIV 20220408 || Days old: DELP 0, IHME 18, IMPE 68, SRIV 0

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

(66) uptake 20220325: DELP 20220325, IHME 20220222, IMPE 20220120, SRIV 20220325

(65) uptake 20220318: DELP 20220318, IHME 20220218, IMPE 20220120, SRIV 20220318

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

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

(63) uptake 20220218: DELP 20220218, IHME 20220218, IMPE NO, SRIV NO

(62) uptake 20220204: DELP 20220204, IHME 20220204, IMPE NO, SRIV NO

(62) uptake 20220128: DELP 20220128, IHME 20220121, IMPE 20220102, SRIV 20220126

(61) uptake 20220121: DELP 20220121, IHME 20220121, IMPE 20220102, SRIV 20220119

(60) uptake 20220116: DELP 20220115, IHME 20220114, IMPE 20220102, SRIV 20220116

(59) uptake 20220114: DELP 20220114, IHME 20220110, IMPE 20211226, SRIV 2022013

(58) uptake 20220110: DELP 20220110, IHME 20220110, IMPE 20211213, SRIV 20220110

(57) uptake 20220104: DELP 20220104, IHME 20211221, IMPE 20211213, SRIV 20220104

(56) uptake 20211221: DELP 20211222, IHME 20211221, IMPE 20211205, SRIV 20211219

(55) uptake 20211217: DELP 20211216, IHME 20211119, IMPE 20211205, SRIV 20211217

(54) uptake 20211210: DELP 20211210, IHME 20211119, IMPE 20211129, SRIV 20211210

(53) uptake 20211203: DELP 20211203, IHME 20211119, IMPE 20211129, SRIV 20211203

(52) uptake 20211126: DELP 20211123, IHME 20211119, IMPE 20211115, SRIV 20211126

(51) uptake 20211119: DELP 20211119, IHME 20211119, IMPE 20211115, SRIV 20211119

(50) uptake 20211112: DELP 20211112, IHME 20211104, IMPE 20211103, SRIV 20211112

(49) uptake 20211105: DELP 20211105, IHME 20211104, IMPE 20211027, SRIV 20211105

(48) uptake 20211029: DELP 20211029, IHME 20211021, IMPE 20211021, SRIV 20211029

(47) uptake 20211022: DELP 20211019, IHME 20211021, IMPE 20211006, SRIV 20211017

(46) uptake 20211015: DELP 20211015, IHME 20211015, IMPE 20211006, SRIV 20211015

(45) uptake 20211008: DELP 20211008, IHME 20211001, IMPE 20210924, LANL 20210926, SRIV 20211008

(44) uptake 20211001: DELP 20210930, IHME 20211001, IMPE 20210924, LANL 20210926, SRIV 20210930

(43) uptake 20210928: DELP 20210927, IHME 20210923, IMPE 20210924, LANL 20210926, SRIV 20210928

(42) uptake 20210923: DELP 20210923, IHME 20210923, IMPE 20210909, LANL 20210919, SRIV 20210923

(41) uptake 20210920: DELP 20210920, IHME 20210916, IMPE 20210909, LANL 20210919, SRIV 20210920

(40) uptake 20210916: DELP 20210916, IHME 20210916, IMPE 20210825, LANL 20210912, SRIV 20210916

(39) uptake 20210910: DELP 20210910, IHME 20210910, IMPE 20210825, LANL 20210905, SRIV 20210910

(38) uptake 20210902: DELP 20210902, IHME 20210902, IMPE 20210825, LANL 20210829, SRIV 20210902

(37) uptake 20210901: DELP 20210901, IHME 20210826, IMPE 20210825, LANL 20210829, SRIV 20210901

(36) uptake 20210826: DELP 20210826, IHME 20210826, IMPE 20210819, LANL 20210822, SRIV 20210826

(35) uptake 20210824: DELP 20210824, IHME 20210819, IMPE 20210819, LANL 20210822, SRIV 20210824

(34) uptake 20210819: DELP 20210819, IHME 20210819, IMPE 20210806, LANL 20210815, SRIV 20210819

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

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

(31) uptake 20210730: DELP 20210730, IHME 20210730, IMPE 20210719, LANL 20210725, SRIV 20210730

(30) uptake 20210727: DELP 20210726, IHME 20210723 version 2, IMPE 20210719, LANL 20210725, SRIV 20210727

(29) uptake 20210726: DELP 20210726, IHME 20210723 version 2, IMPE 20210709, LANL 20210718, SRIV 20210726

. 20210726: IHME estimates for Iran in update 20210723 and in update 20210715 WERE identical UPON FIRST RELEASE OF update 20210723, with numerical value difference of zero. As of 20210726, update 20210723 has been replaced by IHME and is not identical with update 20210715.

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

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

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

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

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

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

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

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

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

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

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

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

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

(15) uptake 20210603: DELP 20210603, IHME 20210528, IMPE 20210527, LANL 20210526, SRIV 20210603

(14) uptake 20210528: DELP 20210528, IHME 20210528, IMPE 20210522, LANL 20210526, SRIV 20210528

(13) uptake 20210522: DELP 20210522, IHME 20210521, IMPE 20210522, LANL 20210519, SRIV 20210522

(12) uptake 20210521: DELP 20210521, IHME 20210521, IMPE 20210516, LANL 20210519, SRIV 20210521

(11) uptake 20210516: DELP 20210516, IHME 20210514, IMPE 20210516, LANL 20210516, SRIV 20210516

(10) uptake 20210515: DELP 20210515, IHME 20210514, IMPE 20210510, LANL 20210512, SRIV 20210515

(09) uptake 20210514: DELP 20210514, IHME 20210514, IMPE 20210424, LANL 20210512, SRIV 20210514

(00) uptake 20210507: DELP 20210507, IHME 20210507, IMPE 20210424, LANL 20210505, SRIV 20210507. IHME update 20210507 vanished after release.

(08) uptake 20210506: DELP 20210506, IHME 20210506, IMPE 20210424, LANL 20210505, SRIV 20210506

(00) uptake 20210430: DELP 20210430, IHME 20210430, IMPE 20210424, LANL 20210428, SRIV 20210430. IHME update 20210430 vanished after release.

(07) uptake 20210424: DELP 20210424, IHME 20210423, IMPE 20210424, LANL 20210421, SRIV 20210424

(06) uptake 20210423: DELP 20210423, IHME 20210423, IMPE 2010417, LANL 20210421, SRIV 20210423

(05) uptake 20210417: DELP 20210417, IHME 20210416, IMPE 20210417, LANL 20210414, SRIV 20210417

(04) uptake 20210416: DELP 20210416, IHME 20210416, IMPE 20210406, LANL 20210414, SRIV 20210416

(03) uptake 20210409: DELP 20210409, IHME 20210409, IMPE 20210406, LANL 20210407, SRIV 20210409

(02) uptake 20210406: DELP 20210406, IHME 20210401, IMPE 20210406, LANL 20210404, SRIV 20210406

(01) uptake 20210401: DELP 20210401, IHME 20210401, IMPE 20210329, LANL 20210331, SRIV 20210401

RESULTS 2021

RESULTS 2022

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. https://www.healthdata.org/covid/ and https://www.healthdata.org/covid/data-downloads

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

SOURCE REPOSITORY LICENCE: https://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: YYGU

CITATION: Gu, Youyang. COVID-19 Projections Using Machine Learning. https://covid19-projections.com and https://github.com/youyanggu/covid19_projections

SOURCE REPOSITORY: https://github.com/youyanggu/covid19_projections

SOURCE REPOSITORY LICENCE: https://github.com/youyanggu/covid19_projections/blob/master/LICENSE

.

(8) ABBREVIATED NAME IN THIS REPOSITORY: UCLA

CITATION: Statistical Machine Learning Lab, Computer Science Department, University of California, Los Angeles. Combating COVID-19. https://covid19.uclaml.org/info.html and https://github.com/uclaml/ucla-covid19-forecasts/tree/master/current_projection

SOURCE REPOSITORY: https://github.com/uclaml/ucla-covid19-forecasts/tree/master/current_projection

SOURCE REPOSITORY LICENCE: https://www.uclaml.org

.

(9) 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

.

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

• See LICENSE