! THE MOST RECENT WEEKLY SITUATION REPORT, EVERY MONDAY MORNING, HERE.
by models: Delphi, IHME, Imperial college, Los Alamos, and Srivastava,
for daily (and total) deaths, cases or infections, and hospitalizations,
or for countries via code adjustment, e.g., Afghanistan, Pakistan
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Project: Combine and visualize international periodically updated estimates of COVID-19 pandemic at the global level (CovidVisualizedGlobal)
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Person: Farshad Pourmalek (pourmalek_farshad at yahoo dot com)
|| PubMed||
Global Reach Top 10%|| UBC SPPH || UW IHME || YouCheck -
Time (initial): 2021-04-14
Under review pre-print for this project:
https://europepmc.org/article/PPR/PPR377517
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THREE “CovidVisualized” repositories, use a common template and logic for visualization of the results of estimates of FIVE international and periodically updated COVID-19 pandemic models for the future of the epidemic:
CovidVisualizedGlobal for the GLOBAL level and six world REGIONS
CovidVisualizedCountry for countries with subnational estimates: CANADA and its provinces
covir2 for countries without subnational estimates: IRAN
The results in these three repositories get periodically updated.
The codes in these repositories can be adapted for use for any country or region in the world.
- For a sample application of “covir2” to a country without subnational estimates see
Afghanistan,Pakistan
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LATEST UPTAKE: uptake 20220114
DELP 2022014, IHME 20220110, IMPE 20211226, NO SRIV
Note on why the SRIV model is not used in the graphs of this uptake: The file format and the included variables in the SRIV 20220114 update have changed, compared to their 20220113 and previous updates, in a way that I cannot locate the estimates of deaths and cases. Therefore, I resorted to using the SRIV 20220113 update for this uptake. The magnitudes of the estimates in the SRIV 20220113 update are several times – and generally highly implausibly – more than the estimates from all other models. That suppresses all the curves of the other models in the graphs. Therefore, in this uptake, the estimates from the SRIV model are not included.
Even without the outlier SRIV model, the results of the remaining three models (i.e., DELP, IHME, and IMPE) are very heterogeneous. Such degree of heterogenous was not seen before, even with the Delta variant. This heterogeneity is both qualitative and quantitative. Qualitative heterogeneity: The directions of the rise and fall of the same variables (i.e., infections, cases, or deaths) during similar calendar time periods in future, for a given location (i.e., WHO region), across the models, are contradictory. One model says it will rise, the other model says it will fall, and the third model says it will stagnate horizontally. Quantitative heterogeneity: Levels of the same estimates in the same times and places have several-fold differences in magnitude across models. Peak times also several weeks to few months difference across models.
Notably, both the IHME and the IMPE models have had two estimates update releases each with inclusion of the Omicron variant.
One possible underlying cause of this extreme heterogeneity in the models’ results can consist of the following factors: (1) Volatile and rapidly changing dynamics of the spread, symptomacity, and fatality of the Omicron Variant across the globe. (2) The resultant variability of the set of published and unpublished data and evidence that are used in models for parametrization of the Omicron dynamics. (3) The degree of the meticulousness of different models in the inclusion of the Omicron dynamics in their models. (4) Asynchronicity of the different models’ update release time.
Omicron not only is disrupting health care services, but it also poses an unprecedented turmoil and challenge for its inclusion in the epidemic models. What will happen with a possible next variant of concern?
👀 SEE: See the predictions of the GLOBAL and REGIONAL COVID-19 pandemic trajectory, in: Global COVID-19 epidemic models situation report No 24 – 2022-01-14
(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
(1) Outcomes: Daily deaths, Daily cases or infections, Hospital-related outcomes, Daily deaths estimated to reported ratio, Daily cases or infections estimated to reported cases ratio. Followed by extra outcomes estimated by IHME and added starting from uptake 20210916, i.e., Daily Infection-outcome ratios, Daily mobility, Daily mask use, and (Percent) cumulative vaccinated.
(2) Calendar time of estimates coverage: All-time, followed by 2021. To view the whole epidemic trajectory and further focus on the near future.
(3) Scenarios: Reference scenarios, followed by alternative scenarios. To examine the main or reference (aka. status quo) scenario and alternative (better and worse) scenarios.
(4) Five models: Different models within each graph (for which model estimates update release dates are maximally synchronized), plus official reports of the country to WHO (curated by Johns Hopkins University) as the under-reported benchmark for trends. To examine how heterogeneity in methods used by different models results in heterogeneous results for the same outcome (same time-place-person aggregated units)
(1) Global Daily deaths, reference scenarios, all time
(2) Global Daily deaths, reference scenarios, 2021 on
(2b) Global Daily deaths, reference scenarios, 2021 on, with IHME excess deaths
(3) Global Daily deaths, 3 scenarios, 2021 on
(3b) Global Daily deaths, 3 scenarios, 2021 on, IHME
(3c) Global Daily deaths, 3 scenarios, 2021 on, IMPE
(4) Global Daily cases or infections, reference scenarios, all time
(5) Global Daily cases or infections, reference scenarios, 2021 on
(6) Global Daily cases or infections, 3 scenarios, 2021 on
(6b) Global Daily cases, 2021 on
(6c) Global Daily estimated infections IHME IMPE to reported cases JOHN, main scenarios, 2021 on
(7) Global Hospital-related outcomes, all time
(8) Global Hospital-related outcomes, 2021 on, without IHME Bed need and IMPE Hospital demand
(9) Global Daily deaths estimated to reported, reference scenarios, 2021 on
(11) Global Daily Infection outcomes ratios, 3 scenarios, all time, IHME
(11b) Global Daily Infection -detection and -hospitalizations ratios, 3 scenarios, all time, IHME
(12) Global Daily mobility, 3 scenarios, all time, IHME
(13) Global Daily mask use, 3 scenarios, IHME
(14) Global Percent cumulative vaccinated, 2021 on, IHME
(0a) Regions together Daily deaths, with GLOBAL, JOHN
(0b) Regions together Daily deaths, without GLOBAL, JOHN
(1) Regions together Daily deaths, with GLOBAL, IHME, IMPE
(2) Regions together Daily deaths, without GLOBAL, IHME, IMPE
(3) Regions together Daily deaths, with GLOBAL, DELP, SRIV
(4) Regions together Daily deaths, without GLOBAL, DELP, SRIV
(5) Regions together Daily excess deaths, with GLOBAL, IHME
(6) Regions together Daily excess deaths, without GLOBAL, IHME
(00a) Regions together Daily cases, with GLOBAL, JOHN
(00b) Regions together Daily cases, without GLOBAL, JOHN
(00c) Regions together Daily cases, without GLOBAL, JOHN, Recent
(7) Regions together Daily infections, with GLOBAL, IHME, IMPE
(8) Regions together Daily infections, without GLOBAL, IHME, IMPE
(9) Regions together Daily cases, with GLOBAL, DELP, SRIV
(10) Regions together Daily cases, without GLOBAL, DELP, SRIV
(11) Regions together Total deaths, with GLOBAL, IHME, IMPE
(12) Regions together Total deaths, without GLOBAL, IHME, IMPE
(13) Regions together Total deaths, with GLOBAL, DELP, SRIV
(14) Regions together Total deaths, without GLOBAL, DELP, SRIV
(15) Regions together Total excess deaths, with GLOBAL, IHME
(16) Regions together Total excess deaths, without GLOBAL, IHME
(17) Regions together Total infections, with GLOBAL, IHME, IMPE
(18) Regions together Total infections, without GLOBAL, IHME, IMPE
(19) Regions together Total cases, with GLOBAL, DELP, SRIV
(20) Regions together Total cases, without GLOBAL, DELP, SRIV
(1) African region Daily deaths, reference scenarios, all time
(3) African region Daily deaths, 3 scenarios, 2021 on
(4) African region Daily cases or infections, reference scenarios, all time
(6) African region Daily cases or infections, 3 scenarios, 2021 on
(1) Americas region Daily deaths, reference scenarios, all time
(3) Americas region Daily deaths, 3 scenarios, 2021 on
(4) Americas region Daily cases or infections, reference scenarios, all time
(6) Americas region Daily cases or infections, 3 scenarios, 2021 on
(1) Americas region, Canada and United States Daily deaths, reference scenarios, all time
(3) Americas region, Canada and United States Daily deaths, 3 scenarios, 2021 on
(4) Americas region, Canada and United States Daily cases or infections, reference scenarios, all time
(6) Americas region, Canada and United States Daily cases or infections, 3 scenarios, 2021 on
(1) Americas region, without Canada and United States Daily deaths, reference scenarios, all time
(3) Americas region, without Canada and United States Daily deaths, 3 scenarios, 2021 on
(4) Americas region, without Canada and United States Daily cases or infections, reference scenarios, all time
(6) Americas region, without Canada and United States Daily cases or infections, 3 scenarios, 2021 on
(1) Eastern Mediterranean region Daily deaths, reference scenarios, all time
(3) Eastern Mediterranean region Daily deaths, 3 scenarios, 2021 on
(4) Eastern Mediterranean region Daily cases or infections, reference scenarios, all time
(6) Eastern Mediterranean region Daily cases or infections, 3 scenarios, 2021 on
(1) European region Daily deaths, reference scenarios, all time
(3) European region Daily deaths, 3 scenarios, 2021 on
(4) European region Daily cases or infections, reference scenarios, all time
(6) European region Daily cases or infections, 3 scenarios, 2021 on
(1) South-East Asian region Daily deaths, reference scenarios, all time
(3) South-East Asian region Daily deaths, 3 scenarios, 2021 on
(4) South-East Asian region Daily cases or infections, reference scenarios, all time
(6) South-East Asian region Daily cases or infections, 3 scenarios, 2021 on
(1) Western Pacific region Daily deaths, reference scenarios, all time
(3) Western Pacific region Daily deaths, 3 scenarios, 2021 on
(4) Western Pacific region Daily cases or infections, reference scenarios, all time
(6) Western Pacific region Daily cases or infections, 3 scenarios, 2021 on
Considering the recent deviations of IMPE and IHME from their regularly scheduled updates, omission of IMPE models’ previous updates except for the latest couple of updates, and the retirement of LANL model on 20210926, the following changes are made, effective as of 20211008.
(1) Every Friday, a new uptake will be performed.
(2) Any model updates older than two weeks on the uptake date will not be included in the new uptake.
As such, the last LANL update of 20210926 will no longer be used after the uptake 20211008.
Pre-print for this project:
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 level, “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 pandemic, 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. In doing so, the strengths and weaknesses of individual models need to be considered and those of this work. Models’ estimates demonstrate the trajectory of COVID-19 deaths, cases or infections, and hospital-related outcomes in one to three months into the future.
METHODS
Eligibility criteria: The criteria for inclusion of target COVID-19 pandemic 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 recently published study on “Predictive performance of international COVID-19 mortality forecasting models” [2]. While non-updated or one-time models can be contemporaneously usable, their results would not sustain up-to-dateness in the long run, especially with the emergence of new variants of concern and various degrees of uncertainties in the progression of vaccination coverage.
RESULTS
Results are described under the following items: (1) The five identified models / studies, (2) The CovidVisualized repositories created in this work, (3) Data management, and (4) Periodical uptakes.
(1) The five identified models / studies
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]. Official reports of countries to World Health Organization, curated by Johns Hopkins University Coronavirus resource center (JOHN) [8], were also used for comparison.
(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
(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 target study, 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 [9] and the model by University of California, Los Angeles model [10] 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 [11], and CovidSim (COVID Simulation) model, developed by researchers at Imperial College, London [12], 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
GitHub (https://github.com) is used for sharing the codes and data. Global and country levels were chosen for demonstration of results. For the national level, one country with and one country without subnational estimates – Canada and Iran, respectively – were chosen based on personal bonds.
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]. Three Github repositories were created for this project: “CovidVisualizedGlobal” [13] for the global level, “CovidVisualizedCountry” [14] for Canada, and “covir2” [15] for Iran. These are referred to as CovidVisualized GitHub repositories hereon. The “covir2” repository was created as “COVID Iran Review number 2” in March 2021, as an update after a first-iteration review was published [1]. The idea and work were further developed toward applicability to any location in the world, with case application for the global level, Canada, and Iran.
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 [13]
https://github.com/pourmalek/CovidVisualizedGlobal
.
CovidVisualizedCountry, COVID-19 pandemic estimates at the country level: Canada [14]
https://github.com/pourmalek/CovidVisualizedCountry
.
covir2, COVID-19 pandemic estimates at the country level: Iran [15]
https://github.com/pourmalek/covir2
(3) Data management
Data management template: A data management template was created for assigning comparable variable names to various outcomes from different models. Comparable and common variable names consist of generic parts (positions in the variable name) denoting the following items: (1) daily or total, (2) deaths, cases or infections, or other outcomes, (3) mean estimate, or lower, or upper uncertainty limit, (4) raw or smoothed estimate, (5) each of the five individual models, and (6) scenarios within each model. This template is described in detail in “variable name structure” in the CovidVisualized GitHub repositories [16]. Stata SE 14.2 (Stata Statistical Software. StataCorp. College Station, Texas) was used to write and run the codes on macOS Big Sur, and test run on Microsoft Windows 10.
Data management: Data acquisition, management, and graphing were performed via Stata codes. Stata codes download the models’ output files from their respective websites, edit them according to the data management template, store each model’s estimates in a single file, and create graphs for all outcomes produced by each model. Then, the models’ single data files are compiled into a single final file, and graphs for all common outcomes are created for this compilation of all models. These graphs are shown on the pages of the three CovidVisualized GitHub repositories [13-15] and in periodical Situation Reports created with each uptake.
Outcome types: Besides (1) daily deaths, (2) daily incident cases or infections, (3) total deaths, and (4) total cases or infections, other outcomes estimated by one or more individual models include prevalence, active or prevalent cases, recovered cases, hospital admissions, regular beds needed, ICU (Intensive Care Unit) beds needed, ventilated cases, seroprevalence, and effective reproduction number. All outcome types were graphed.
Secondary variables: Secondary variables are created in this work using the primary variables released by the individual models. They include case fatality rate (CFR), infection fatality rate (IFR), cases per deaths, ratio of estimated to reported deaths, and ratio of estimated to reported cases. CFR and IFR have only a daily version, and the other secondary variables have both daily and total (cumulative) versions.
Uncertainty, scenarios, variants, and vaccines: For each model and for each outcome, both the point (mean) estimates and the interval estimates (95% uncertainty limits) were graphed where available. Similarly, both the “reference” scenario (aka status quo) and alternative scenarios (i.e., “better” and “worse” scenarios) were graphed for models with more than one scenario (i.e., IHME and IMPE). Assumptions about and empirical inputs from distributions of variants and vaccination coverages across space and time have been progressively included in models and scenarios of IHME and IMPE.
Subnational estimates: 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
A set of conventions were created for the periodic uptake of the models’ estimates updates. The two models with the least frequency of periodic updates of estimates are IHME and IMPE, updated almost weekly and bi-weekly, respectively. 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. These updates of CovidVisualized repositories are labelled as “uptakes” to differentiate them from models’ estimates updates. These conventions for periodical uptakes are described in detail in the CovidVisualized GitHub repositories [17]. 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. Uptaking the models’ estimates updates with every update of these latter three models is not expected to depict a much more informative profile of the epidemic’s trajectory in future, when compared against the current convention for uptakes. The IHME, IMPE, and SRIV models provide estimates for about three months into the future with each update release, the DELP model for about two months, and the LANL model for about one month.
With each uptake, a directory is created in the root of the main branch of each of the three CovidVisualized repositories and named with the uptake date (e.g., 20210730). Uptakes are also created retrospectively, compiling the results of the previous updates of the models’ results. Available uptakes for Iran and the global level have been created going back to April 2021 and for Canada to June 2021. Under each dated uptake directory, there are two directories for “code” and for “output”. Under each, there are located directories with the abbreviated name of the models. For example, “DELP” directories under “code” store the Stata code files (.do), and those under “output” contain the outputs from executing the codes: the single data file for model estimates (in. dta and .csv formats), Stata log file (.smcl), and the graphs in PDF format. The directory “master” contains the master Stata do-file which executes all the other do-files, and the directory “merge” (under “code”) contains the code for creating the single final merged file of all models. The directory “merge” (under “output”) stores the created single final merged file of all compiled models, as well as the graphs that contain all the models. Selected graphs that contain all the models are visible on the root page of each uptake directory and are also stacked in reverse chronological order on the main page of each repository. With each uptake, selected graphs of estimated outcomes are added at the start of the main page for each repository. Situation Reports are created and shared with national and subnational health authorities - - WHO for the global level (CovidVisualizedGlobal).
Similar work: The “covidcompare” tool [18] 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].
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 three 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” [14] and “covir2” [15] can be slightly modified by any researcher to be used for countries with and without subnational estimates respectively (See examples for United States, Afghanistan, and Pakistan). “CovidVisualizedCountry” can be adjusted for use for any type of regionalization of the countries 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” [19].
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 [20] and US COVID-19 Forecast Hub [21]. The ensemble models have been empirically shown to be more accurate than any of the individual models used in the ensemble method [22]. 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 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, as provided in [23]. Therefore, no ethics approval or consent to participate were 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) [13], (2) GitHub repository “CovidVisualizedCountry” under (http://doi.org/10.5281/zenodo.5019482) [14], and (3) GitHub repository “covir2” under (http://doi.org/10.5281/zenodo.5020797) [15]. See references [13-15] for details and links to the data.
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.
-
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.
-
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
-
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
-
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
-
Los Alamos National Laboratory (LANL). COVID-19 cases and deaths forecasts. Los Alamos: Los Alamos National Laboratory (LANL). https://covid-19.bsvgateway.org
-
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
-
Johns Hopkins University. Coronavirus resource center. https://coronavirus.jhu.edu/map.html and https://github.com/CSSEGISandData/COVID-19
-
Gu, Youyang. COVID-19 Projections Using Machine Learning. https://covid19-projections.com and https://github.com/youyanggu/covid19_projections
-
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
-
The COVID-19 International Modelling Consortium (CoMo Consortium), 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
-
MRC Centre for Global Infectious Disease Analysis, Imperial College London. CovidSim. COVID-19 Scenario Analysis Tool. https://covidsim.org/v5.20210727/?place=ca and https://covidsim.org/v5.20210727/?place=ir
-
Pourmalek, F. GitHub repository “CovidVisualizedGlobal”: Combine and visualize international periodically updated estimates of COVID-19 pandemic at the global level. Version 1.1, Released June 23, 2021. http://doi.org/10.5281/zenodo.5019030 https://github.com/pourmalek/CovidVisualizedGlobal
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Pourmalek, F. GitHub repository “CovidVisualizedCountry”: Combine and visualize international periodically updated estimates of COVID-19 pandemic at the country level, countries with subnational level estimates: Canada, national level, provinces, and territories. Version 1.1, Released June 23, 2021. http://doi.org/10.5281/zenodo.5019482 https://github.com/pourmalek/CovidVisualizedCountry
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Pourmalek, F. GitHub repository “covir2”: Combine and visualize international periodically updated estimates of COVID-19 pandemic at the country level, countries without subnational level estimates: Iran. Version 2.2, Released June 23, 2021. http://doi.org/10.5281/zenodo.5020797 https://github.com/pourmalek/covir2
-
Pourmalek, F. “covir2”: Combine and visualize international periodically updated estimates of COVID-19 at the country level: Iran. Version 2.2, Released June 23, 2021. Variable name structure. http://doi.org/10.5281/zenodo.5020797 https://github.com/pourmalek/covir2/blob/main/Variable%20name%20structure.md
-
Pourmalek, F. “covir2”: Combine and visualize international periodically updated estimates of COVID-19 at the country level: Iran. Version 2.2, Released June 23, 2021. Setup. http://doi.org/10.5281/zenodo.5020797 https://github.com/pourmalek/covir2/tree/main/setup
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Friedman J, Liu P, Akre S. The covidcompare tool. https://covidcompare.io/about
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National Academies of Sciences, Engineering, and Medicine. Reproducibility and Replicability in Science. Washington, DC: The National Academies Press. 2019. https://doi.org/10.17226/25303
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European Centre for Disease Prevention and Control. European Covid-19 Forecast Hub. https://covid19forecasthub.eu/background.html and https://github.com/epiforecasts/covid19-forecast-hub-europe
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COVID-19 Forecast Hub. https://covid19forecasthub.org/doc/ensemble and https://github.com/reichlab/covid19-forecast-hub Accessed 23 June 2021.
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Cramer EY, Ray EL, Lopez VK, Bracher J, Brennen A, et al. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US. medRxiv preprint. Posted February 05, 2021. https://www.medrxiv.org/content/10.1101/2021.02.03.21250974v1
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Pourmalek, F. GitHub repository “covir2”: Combine and visualize international periodically updated estimates of COVID-19 at the country level, countries without subnational level estimates: Iran. Version 2.2, Released June 23, 2021. Licenses / Copyrights of data and/or graphs used in this repository. http://doi.org/10.5281/zenodo.5020797 https://github.com/pourmalek/covir2#licenses--copyrights-of-data-and--or-graphs-used-in-this-repository Accessed 23 June 2021.
The Stata codes can be executed on local machines:
Run in Stata "Users/local-user-name/Downloads/CovidVisualizedGlobal/20210709/code/master/do country master.do" on your local machine after the directory /CovidVisualizedGlobal/ is downloaded into and is located in the root of /Downloads/ folder of your local machine, for uptake 20210709; and other instances of 202YMMDD for other uptake dates.
Preproduced outputs are stored for each uptake, e.g., 20210709, in folder output, for each component study, i.e., DELP, IHME, IMPE, LANL, and SRIV, plus JOHN as benchmark. Combined results are stored in merge. All merged graphs for each uptake are accessible via main page of each uptake date, e.g., https://github.com/pourmalek/CovidVisualizedGlobal/tree/main/20210709/ and the link is placed in front of Merged graphs of uptake 20210709 here
Variable name structure describes the template for models’ output data management used in this repository.
Rule for 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.
On 20210824, the Imperial College COVID-19 model/study removed their "old fits" from https://github.com/mrc-ide/global-lmic-reports/tree/master/data (old), but they are accessible on https://mrcdata.dide.ic.ac.uk/global-lmic-reports/ (new). Stata codes are updated accordingly to use estimates files from the latter site.
Component studies (the international periodically updated models) and their scenarios are mentioned below.
[Names within brackets assigned by this project.]
- S00 = [Not Applicable]
- S00 = [Single scenario]
Scenarios of IHME model as of update 20211221:
- S01 = Current projection [Status Quo, Reference scenario]
- S02 = 80% mask use [Best scenario]
- S03 = High severity of Omicron [Worse scenario]
- S04 = Third dose of vaccine [Second best scenario]
- S05 = Reduced vaccine hesitancy [Third best scenario]
Scenarios of IHME model before update 20211221:
- S01 = Reference scenario [Status Quo]
- S02 = Best scenario (Universal masks) [Best]
- S03 = Worse scenario (Mandates easing) [Worst]
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S01 = Additional 50% Reduction [Best], renamed "Optimistic" with IMPE update 20211103 (2021-11-03_v9.csv.zip) released on 20211110
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S02 = Current level of interventions [Reference]
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S03 = Relax Interventions 50% [Worst], renamed "Pessimistic" with IMPE update 20211103 (2021-11-03_v9.csv.zip) released on 20211110
. additional alternate scenarios:
-
S04 = Surged Additional 50% Reduction [Best, Surged], renamed "Surged Optimistic"
-
S05 = Surged Maintain Status Quo [Reference, Surged]
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S06 = Surged Relax Interventions 50% [Worst, Surged], renamed "Surged Pessimistic"
- S00 = [Single scenario]
- S00 = [current]
- Note: SRIV has 3 sets of scenrios; see
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
.
DELP 2022014, IHME 20220110, IMPE 20211226, NO SRIV
(59) uptake 2022014:
DELP 2022014, IHME 20220110, IMPE 20211226, NO SRIV
(58) uptake 20220112:
DELP 20220111, IHME 20220110, IMPE 20211226, SRIV 20220112
(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 2021119, IMPE 20211205, SRIV 20211217
(54) uptake 20211210:
DELP 20211210, IHME 2021119, 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 global level 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
(08) uptake 20210506: DELP 20210506, IHME 20210506, IMPE 20210424, LANL 20210505, SRIV 20210506
(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 20210408, IHME 20210409, IMPE 20210406, LANL 20210407, SRIV 20210409
(02) uptake 20210406: DELP 20210401, IHME 20210401, IMPE 20210406, LANL 20210404, SRIV 20210406
(01) uptake 20210401: DELP 20210401, IHME 20210401, IMPE 20210329, LANL 20210331, SRIV 20210401
Selected graphs from previous uptakes are stored in another web page: RESULTS, PREVIOUS UPTAKES, links to which are also provided below:
(1) Daily deaths, reference scenarios, all time
(2) Daily deaths, reference scenarios, 2021
(3) Daily deaths, 3 scenarios, 2011
(3b) Daily deaths, 3 scenarios, 2021, IHME
(3c) Daily deaths, 3 scenarios, 2021, IMPE
(4) Daily cases, reference scenarios, all time
(5) Daily cases, reference scenarios, 2021
(6) Daily cases, 3 scenarios, 2011
(7) Hospital-related outcomes, all time
(8) Hospital-related outcomes, 2021, without IHME Bed need and IMPE Hospital demand
(9) Daily deaths estimated to reported, reference scenarios, 2021
(10) Daily cases estimated to reported, reference scenarios, 2021
.
(11) Daily Infection-outcome ratios, 3 scenarios, IHME
(12) Daily mobility, scenarios IHME
(13) Daily mask use, 3 scenarios, IHME
(14) Percent cumulative vaccinated, IHME
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
.
- All codes are copyrighted by the author under Apache License 2.0.
- See
LICENSE
Pourmalek, F. GitHub repository “CovidVisualizedGlobal”: Combine and visualize international periodically updating estimates of COVID-19 pandemic at the global level. Version 1.1, Released June 23, 2021. http://doi.org/10.5281/zenodo.5019030 , https://github.com/pourmalek/CovidVisualizedGlobal