Proposal Review

[LaurieLBaker]

Proposal Review

Reviewer Name: Professor Baker Date: March 7, 2022

Section 1 - Introduction:

The introduction should introduce your general research question and your data (where it came from, how it was collected, what are the cases, what are the variables, etc.).

What is their general research question: Is the general research question clear? If it is not clear, what questions do you have?

• I think your question is clear but also very broad. Covid-19 epidemiological modeling is a huge topic area and making a covid model is really complex, see this blog.
• I'm wondering if there are a few subquestions you might look at instead of covid deaths that might be interesting/look at covid from a different angle compared to what we are seeing every day, e.g. are there commonalities that countries share that have low or high vaccination rates? How do country's responses to the pandemic vary depending on their location or other characteristics about their economies/governments?

• Section 2 - Data:

  • [X] Is the data in the /data folder?
  • [X] Does the README include the dimensions and codebook for the data set?
  • [X] Does the proposal include the output of glimpse() or skim() of the data frame.

Data suitability:

• [X] Does the dataset have at least 50 observations and between 10 to 20 variables (exceptions can be made).
• [X] Does the data set include a mix of categorical variables, discrete numerical variables, and continuous numerical variables.
• [X] What variables does the data include (list below):

Section 3 - Data analysis plan:

• [X] Does the proposal include outcome (response, Y) and predictor (explanatory, X) variables they will use to answer your question? And/or the comparison groups they will use, if applicable.

Do the outcome and predictor variables and/or comparison groups make sense in the context of the question? Why or Why not?

• Yes, but see above for suggestion to focus on other subquestions.

• [X] Does the proposal include some very preliminary exploratory data analysis, including some summary statistics and visualizations, along with some explanation on how they help you learn more about your data. (They can add to these later as they work on their project.)

• [X] Does the proposal include the statistical method(s) that they believe will be useful in answering your question(s). (They can update these later as they work on their project.)

• [X] Do they include what results from these specific statistical methods that are needed to support their hypothesized answer?

Reflections

1. What was something you found interesting about the project?

   • I think the dataset looks really cool and there are a lot of possibilities for things to look at.

2. What ideas/feedback do you have for other things they may explore?

   • You'll want to think about ways you will deal with/capture uncertainty in the data. I think you'll want to think about how you can make your analysis stand out from other covid projects that have been in the news. Is there a particular aspect of the covid response that you've always wondered about? Which countries are you most curious about?

3. What kinds of plots should they consider to complete the project goal to create some kind of compelling visualization(s) of this data in R?

   • I think there will be a lot of potential to look at time series plots. Maps will be useful too. I think animations would also be interesting to show how vaccination rates have changed through time across the world. I think there will be a lot of interesting ways to explore the data.

4. Any additional feedback you'd like to give the other group:

   • My PhD was in epidemiology and I have a lot of colleagues who work in this area. I also spent a part of the pandemic on a team reviewing covid literature and looking at how it would affect LMICs. Ask me questions about disease in general and covid models. I can point you to some good resources/authors.

Great work @chrissyaman and @Rundstedtzz!

[jdonahu]

Proposal Review Reviewers Team Name: Gamesonyourphone Date: 3/1/2022

Section 3 - Data analysis plan: Does the proposal include outcome (response, Y) and predictor (explanatory, X) variables they will use to answer your question? And/or the comparison groups they will use, if applicable.

-Yes, the outcome variables were described as deaths and/or other indicators of the magnitude of the pandemic. The independent variables would be varied amongst different factors that contribute to the indicator.

Do the outcome and predictor variables and/or comparison groups make sense in the context of the question? Why or Why not?

-Yes, because they are helping us to visualize and decipher the relationship between covid guidelines, etc. and measurements of the pandemic such as death numbers. ...

Does the proposal include some very preliminary exploratory data analysis, including some summary statistics and visualizations, along with some explanation on how they help you learn more about your data. (They can add to these later as they work on their project.)

-Yes, they even made a histogram!

Does the proposal include the statistical method(s) that they believe will be useful in answering your question(s). (They can update these later as they work on their project.)

-Yes! They included correlations, linear regressions, machine learning algorithms, difference regressions

Do they include what results from these specific statistical methods that are needed to support their hypothesized answer?

-Yes, they even offered a prediction of what they think will be successful.

Reflections What was something you found interesting about the project?

-I like how relevant it is, and how it might teach us more about an issue that affects/affected all of us ... What ideas/feedback do you have for other things they may explore?

-Their project was very far-reaching in terms of what covid-related data they could provide, so I don't have any other ideas there. But perhaps they might consider, if they've exhausted their resources as for covid-related data, to look beyond and consider other affects of the pandemic that one might not think of initially

... What kinds of plots should they consider to complete the project goal to create some kind of compelling visualization(s) of this data in R?

-I think any plot is good given a certain context, but it totally depends on the specific dataset they are looking at

... ... Any additional feedback you'd like to give the other group: Nice

[gkacton]

Section 2 - Data: Is the data in the /data folder? Yes Does the README include the dimensions and codebook for the data set? Yes Does the proposal include the output of glimpse() or skim() of the data frame. Yes Data suitability: Does the dataset have at least 50 observations and between 10 to 20 variables (exceptions can be made). Yes Does the data set include a mix of categorical variables, discrete numerical variables, and continuous numerical variables. Yes What variables does the data include (list below): It has 67 so I'm not listing them all!

• Continent
• Location
• Date
• Total cases
• New cases
• Total deaths
• New deaths
• Reproduction rate
• ICU and hospital patient numbers
• Vaccination data
• Population and population density
• GDP per capita

Reflections What was something you found interesting about the project? I think it's cool to combine the COVID data with more demographic information about the countries. I feel like it's sometimes easy for Americans to forget that other countries have far less resources than we do, and that's important to remember when talking about COVID.

What ideas/feedback do you have for other things they may explore? It might be interesting to look at public health data points that aren't necessarily explicit COVID safety measures to see how they impact COVID numbers. For example, handwashing facilities and prevelance of diabetes and cardiovascular deaths might have interesting relationships to COVID, since they can indicate the overall strength of a country's public health system.

What kinds of plots should they consider to complete the project goal to create some kind of compelling visualization(s) of this data in R? I'd love to see some maps, since this set is so tied to geographical data!

[finnaball4eva]

Section 1 - Introduction:

The introduction should introduce your general research question and your data (where it came from, how it was collected, what are the cases, what are the variables, etc.).

What is their general research question: How did different countries experience the pandemic, and what caused countries to have such different experiences?

Is the general research question clear? If it is not clear, what questions do you have?

Yes I do understand the research question and think they have a compelling idea. The have set themselves up to make interesting connections about COVID response between different countries.

Reflections

What was something you found interesting about the project?

Their project is super relevant right now. They are working with relatively new data where they can make some new insights.

What ideas/feedback do you have for other things they may explore?

I am unsure if they have enough explanatory variables at this point. How are they planning on explaining why vaccination rates or death rates are higher in certain places?

What kinds of plots should they consider to complete the project goal to create some kind of compelling visualization(s) of this data in R?

Bar charts will be useful in comparing different countries response to COVID. I can also envision summary statistics being useful to compare vaccination or health rates among different countries.

...