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Team members

• Justin Liang 56007123
• Felix Ma 22972558
• Yifan Wang 45753621
• Jiale Wang 45464484

Description of the data

comment: if the report directly refers to the content from other sources, remember to specify it. For example, 'courtesy to https://github.com/owid/covid-19-data/tree/master/public/data for supplying the descriptions for the data'

The data set is sourced from https://github.com/owid/covid-19-data/tree/master/public/data

As of today September 28, the data set contains 119875 observations.

The data set has a total of 65 columns.

Some interesting predictors are 'new_cases' - the new Covid-19 cases on a given day, 'new_vaccinations' - the number of newly vaccinated people on the given day, 'people_vaccinated' - the total number of people that have been vaccinated and 'hosp_patients' and 'icu_patients', the number of patients in hospital and specifically in intensive care unit respectively.

The complete list of variables are listed below:

Confirmed cases

Variable	Description
total_cases	Total confirmed cases of COVID-19
new_cases	New confirmed cases of COVID-19
new_cases_smoothed	New confirmed cases of COVID-19 (7-day smoothed)
total_cases_per_million	Total confirmed cases of COVID-19 per 1,000,000 people
new_cases_per_million	New confirmed cases of COVID-19 per 1,000,000 people
new_cases_smoothed_per_million	New confirmed cases of COVID-19 (7-day smoothed) per 1,000,000 people

Confirmed deaths

Variable	Description
total_deaths	Total deaths attributed to COVID-19
new_deaths	New deaths attributed to COVID-19
new_deaths_smoothed	New deaths attributed to COVID-19 (7-day smoothed)
total_deaths_per_million	Total deaths attributed to COVID-19 per 1,000,000 people
new_deaths_per_million	New deaths attributed to COVID-19 per 1,000,000 people
new_deaths_smoothed_per_million	New deaths attributed to COVID-19 (7-day smoothed) per 1,000,000 people

Excess mortality

Variable	Description
excess_mortality	Percentage difference between the reported number of weekly or monthly deaths in 2020–2021 and the projected number of deaths for the same period based on previous years. For more information, see https://github.com/owid/covid-19-data/tree/master/public/data/excess_mortality
excess_mortality_cumulative	Percentage difference between the cumulative number of deaths since 1 January 2020 and the cumulative projected deaths for the same period based on previous years. For more information, see https://github.com/owid/covid-19-data/tree/master/public/data/excess_mortality
excess_mortality_cumulative_absolute	Cumulative difference between the reported number of deaths since 1 January 2020 and the projected number of deaths for the same period based on previous years. For more information, see https://github.com/owid/covid-19-data/tree/master/public/data/excess_mortality
excess_mortality_cumulative_per_million	Cumulative difference between the reported number of deaths since 1 January 2020 and the projected number of deaths for the same period based on previous years, per million people. For more information, see https://github.com/owid/covid-19-data/tree/master/public/data/excess_mortality

Hospital & ICU

Variable	Description
icu_patients	Number of COVID-19 patients in intensive care units (ICUs) on a given day
icu_patients_per_million	Number of COVID-19 patients in intensive care units (ICUs) on a given day per 1,000,000 people
hosp_patients	Number of COVID-19 patients in hospital on a given day
hosp_patients_per_million	Number of COVID-19 patients in hospital on a given day per 1,000,000 people
weekly_icu_admissions	Number of COVID-19 patients newly admitted to intensive care units (ICUs) in a given week
weekly_icu_admissions_per_million	Number of COVID-19 patients newly admitted to intensive care units (ICUs) in a given week per 1,000,000 people
weekly_hosp_admissions	Number of COVID-19 patients newly admitted to hospitals in a given week
weekly_hosp_admissions_per_million	Number of COVID-19 patients newly admitted to hospitals in a given week per 1,000,000 people

Policy responses

Variable	Description
stringency_index	Government Response Stringency Index: composite measure based on 9 response indicators including school closures, workplace closures, and travel bans, rescaled to a value from 0 to 100 (100 = strictest response)

Reproduction rate

Variable	Description
reproduction_rate	Real-time estimate of the effective reproduction rate (R) of COVID-19. See https://github.com/crondonm/TrackingR/tree/main/Estimates-Database

Tests & positivity

Variable	Description
total_tests	Total tests for COVID-19
new_tests	New tests for COVID-19 (only calculated for consecutive days)
total_tests_per_thousand	Total tests for COVID-19 per 1,000 people
new_tests_per_thousand	New tests for COVID-19 per 1,000 people
new_tests_smoothed	New tests for COVID-19 (7-day smoothed). For countries that don't report testing data on a daily basis, we assume that testing changed equally on a daily basis over any periods in which no data was reported. This produces a complete series of daily figures, which is then averaged over a rolling 7-day window
new_tests_smoothed_per_thousand	New tests for COVID-19 (7-day smoothed) per 1,000 people
positive_rate	The share of COVID-19 tests that are positive, given as a rolling 7-day average (this is the inverse of tests_per_case)
tests_per_case	Tests conducted per new confirmed case of COVID-19, given as a rolling 7-day average (this is the inverse of positive_rate)
tests_units	Units used by the location to report its testing data

Vaccinations

Variable	Description
total_vaccinations	Total number of COVID-19 vaccination doses administered
people_vaccinated	Total number of people who received at least one vaccine dose
people_fully_vaccinated	Total number of people who received all doses prescribed by the vaccination protocol
total_boosters	Total number of COVID-19 vaccination booster doses administered (doses administered beyond the number prescribed by the vaccination protocol)
new_vaccinations	New COVID-19 vaccination doses administered (only calculated for consecutive days)
new_vaccinations_smoothed	New COVID-19 vaccination doses administered (7-day smoothed). For countries that don't report vaccination data on a daily basis, we assume that vaccination changed equally on a daily basis over any periods in which no data was reported. This produces a complete series of daily figures, which is then averaged over a rolling 7-day window
total_vaccinations_per_hundred	Total number of COVID-19 vaccination doses administered per 100 people in the total population
people_vaccinated_per_hundred	Total number of people who received at least one vaccine dose per 100 people in the total population
people_fully_vaccinated_per_hundred	Total number of people who received all doses prescribed by the vaccination protocol per 100 people in the total population
total_boosters_per_hundred	Total number of COVID-19 vaccination booster doses administered per 100 people in the total population
new_vaccinations_smoothed_per_million	New COVID-19 vaccination doses administered (7-day smoothed) per 1,000,000 people in the total population

Others

Variable	Description
iso_code	ISO 3166-1 alpha-3 – three-letter country codes
continent	Continent of the geographical location
location	Geographical location
date	Date of observation
population	Population in 2020
population_density	Number of people divided by land area, measured in square kilometers, most recent year available
median_age	Median age of the population, UN projection for 2020
aged_65_older	Share of the population that is 65 years and older, most recent year available
aged_70_older	Share of the population that is 70 years and older in 2015
gdp_per_capita	Gross domestic product at purchasing power parity (constant 2011 international dollars), most recent year available
extreme_poverty	Share of the population living in extreme poverty, most recent year available since 2010
cardiovasc_death_rate	Death rate from cardiovascular disease in 2017 (annual number of deaths per 100,000 people)
diabetes_prevalence	Diabetes prevalence (% of population aged 20 to 79) in 2017
female_smokers	Share of women who smoke, most recent year available
male_smokers	Share of men who smoke, most recent year available
handwashing_facilities	Share of the population with basic handwashing facilities on premises, most recent year available
hospital_beds_per_thousand	Hospital beds per 1,000 people, most recent year available since 2010
life_expectancy	Life expectancy at birth in 2019
human_development_index	A composite index measuring average achievement in three basic dimensions of human development—a long and healthy life, knowledge and a decent standard of living. Values for 2019, imported from http://hdr.undp.org/en/indicators/137506

Precise description of the question(s)

We would like to be able to model future Covid-19 cases on a given day and determine the variables in the above dataset that can best explain the number of cases that will occur.

Why this question/dataset

Different countries have different vaccination rates and economical and sociopolitical environments, Covid-19 is a serious issue currently and it can be of great insight to predict future Covid-19 cases. This question seems interesting as it would potentially be very useful to understand the factors that are causing a sudden rise or fall in cases and perhaps be able to determine if factors like amount of people vaccinated, gdp, population density can affect the number of cases that are occuring. The dataset itself is very complete with several useful variables that may influence the susceptibility of a population to Covid-19 spread.

Reading list

1.COVID-19 in Canada: a six-month update on social and economic impacts. By Canadian Government EBook Collection; Statistics Canada.

2. COVID-19 in Canada: Experience and Response. By Detsky, Allan S; Bogoch, Isaac I.

3. Epidemiology of COVID-19 in Canada in 2020: The Pre-Vaccine Era. By Sligl, Wendy.

4. A "No More Waves" strategy for COVID-19 in Canada. By Morris, Andrew M; Mintz, Jack M.

5. Priority strategies to optimize testing and screening for COVID-19 in Canada: report. By Canada. Health Canada; Canadian Government EBook Collection.

6. COVID-19 in Canada. By Arora, Anil.

7. Face Masks, Public Policies and Slowing the Spread of COVID-19: Evidence from Canada. By Karaivanov, Alexander.

======= Some Additional Links
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0241165
https://www.mdpi.com/2227-7390/9/13/1558
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8118153/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7308996.3/
https://www.sciencedirect.com/science/article/pii/S2049080120301485
https://link.springer.com/article/10.1007/s11831-020-09472-8
https://www.scirp.org/html/3-8402135_101729.htm

Team contract.

Participation
We agree to at least make 1 commit per week, and actively participate in group discussion.

Communication
We agree to communicate actively through WeChat, use the platforms like Google Drive, Google Docs to store our ideas and meeting records.

Meetings
We agree to have a group face to face meeting every Monday 5p.m. to exchange ideas and dealing with issues. We agree to use anytime Zoom Cloud Meeting to deal with accident issues. We agree to inform other group members in advance if we will be absent in the meeting time.

Conduct
We agree to start everything ahead and finish everything on time. We agree to make some progress every week, and finish tasks responsibly.

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Do not make any changes from here on. Only the TAs will edit the following.

Checkpoint 1 grade

(5 / 5)

Checkpoint 2 grade

Total (28 / 30)

Words (6 / 6) The text is laid out cleanly, with clear divisions and transitions between sections and sub-sections. The writing itself is well-organized, free of grammatical and other mechanical errors, divided into complete sentences logically grouped into paragraphs and sections, and easy to follow from the presumed level of knowledge.

Numbers (1 / 1) All numerical results or summaries are reported to suitable precision, and with appropriate measures of uncertainty attached when applicable.

Pictures (6 / 7) Figures and tables are easy to read, with informative captions, axis labels and legends, and are placed near the relevant pieces of text or referred to with convenient labels.

Code (3 / 4) The code is formatted and organized so that it is easy for others to read and understand. It is indented, commented, and uses meaningful names. It only includes computations which are actually needed to answer the analytical questions, and avoids redundancy. Code borrowed from the notes, from books, or from resources found online is explicitly acknowledged and sourced in the comments. Functions or procedures not directly taken from the notes have accompanying tests which check whether the code does what it is supposed to. The text of the report is free of intrusive blocks of code. With regards to R Markdown, all calculations are actually done in the file as it knits, and only relevant results are shown.

Exploratory data analysis (12 / 12) Variables are examined individually and bivariately. Features/observations are discussed with appropriate figure or tables. The relevance of the EDA to the questions and potential models is clearly explained.

Comment

1. https://learning.github.ubc.ca/STAT-406-101-2021W/project-husky/blob/3a7cdc4097b158d23a8d6dd28b18cbfed7e9e8a8/report.Rmd#L337 Good job on using dplyr and ggplot to write clean code. However, it lacks comments of what you are doing to restructure/manipulate the dataset.
2. https://learning.github.ubc.ca/STAT-406-101-2021W/project-husky/blob/3a7cdc4097b158d23a8d6dd28b18cbfed7e9e8a8/report.Rmd#L358 It remains unclear under the bivariate analysis plots what the x-axis value stands for?

Checkpoint 3 grade

Total (65 / 65)

Words (8 / 8) The text is laid out cleanly, with clear divisions and transitions between sections and sub-sections. The writing itself is well-organized, free of grammatical and other mechanical errors, divided into complete sentences logically grouped into paragraphs and sections, and easy to follow from the presumed level of knowledge.

Numbers (1 / 1) All numerical results or summaries are reported to suitable precision, and with appropriate measures of uncertainty attached when applicable.

Pictures (7 / 7) Figures and tables are easy to read, with informative captions, axis labels and legends, and are placed near the relevant pieces of text.

Code (4 / 4) The code is formatted and organized so that it is easy for others to read and understand. It is indented, commented, and uses meaningful names. It only includes computations which are actually needed to answer the analytical questions, and avoids redundancy. Code borrowed from the notes, from books, or from resources found online is explicitly acknowledged and sourced in the comments. Functions or procedures not directly taken from the notes have accompanying tests which check whether the code does what it is supposed to. The text of the report is free of intrusive blocks of code. If you use R Markdown, all calculations are actually done in the file as it knits, and only relevant results are shown.

Exploratory Data Analysis (12 / 12) Variables are examined individually and bivariately. Features/observations are discussed with appropriate figure or tables. The relevance of the EDA to the questions and potential models is clearly explained.

Results and analysis (25 / 25) The statistical summaries are clearly related to, or possibly derive from, the substantive questions of interest. Any assumptions are checked by means of appropriate diagnostic plots or formal tests. Limitations from un-fixable problems are clearly noted. The actual estimation of parameters, predictions, or other calculations are technically correct. All calculations based on estimates are clearly explained, and also technically correct. All estimates or derived quantities are accompanied with appropriate measures of uncertainty.

Conclusions (8 / 8) The substantive questions are answered as precisely as the data and the model allow. The chain of reasoning from estimation results about models, or derived quantities, to substantive conclusions is both clear and convincing. Contingent answers ("if $X$, then $Y$, but if $Z$, then $W$") are likewise described as warranted by the and data. If uncertainties in the data mean the answers to some questions must be imprecise, this too is reflected in the conclusions.