Project 2: Pandemics

By Melissa Wright and Molly Cox

[With additional technical support from tutors Mark Steadman, Samael Reyna, and Earnest Long Jr.]

The Task

1. Use a Python Flask–powered RESTful API, HTML/CSS, JavaScript, and one database: SQLite.
2. Make a dashboard page with multiple charts that update from the same data : Globe, Bar, Line
3. Use a JS library that we did not cover in class: PlanetaryJS
4. Ensure Visualizations are powered by data sets with at least 100 records. : Multiple Pandemics & Centroids for plotting 195 Countries
5. User-driven interaction: dropdowns & Menu Bar to view about page / pandemic info / charts
6. Final visualization should ideally include at least three views. : Globe, Bar Chart, and Line Chart

Dataset

1. Covid19: https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset#covid_19_data.csv
2. Ebola 2014-16: https://www.kaggle.com/imdevskp/ebola-outbreak-20142016-complete-dataset (we added possible, suspected to the confirmed deaths)
3. Swineflu 2009: https://en.wikipedia.org/wiki/2009_flu_pandemic_by_country
4. Spanish Flu: https://ourworldindata.org/spanish-flu-largest-influenza-pandemic-in-history (for reference, not included in visualizations)

Dataset for Population & Plotting on Map

1. United Nations: https://population.un.org/wpp/Download/Standard/Population/
2. Centroids Data - https://worldmap.harvard.edu/data/geonode:country_centroids_az8

ETL PROCESS

We used Jupyter Notebook to accomplish the following:

• Take the raw data and group by Country/Year.
• Merge the dataset with United Nations populations data so we could compare the magnitude of the epidemic relative to country population.
• Merge this dataset with a dataset that contains a latitude and longitude for the center of each country so that we could plot the data on the globe.
• To merge the files by country, we made a lookup table that had the country names for each data set.
• Create a Jupyter Notebook for each Pandemic and then had it generate a clean CSV file.
• Import the now clean csv files into a new notebook, combine them, and store the data in a sqlite database.

Jupyter Notebook Files:

• lastdate_covid_19_scrubbing
• ebola_scrub
• swineflu_scrub_wikipedia
• pop_scrub
• combine_csvs

Scrubbed .CSV Files:

• merged_covid_19_data

• swineflu_data

• ebola_data

• pop_data

• clean_country_centroids_az8 (centroid data)

• Country_lookup_table

Database File:

• pandemic_final.db (sqlite database)

PANDEMIC WEBSITE - VISUALIZATIONS

MENU BAR TO ACCESS BAR CHARTS

These Change as you select the drop down

These Change as you select the drop down

MENU BAR TO ACCESS GLOBE

** There is a delay when running these so we have taken a screenshot of the corresponding globe image

![about](Images/about.png)

Challenges

• Accuracy and availability of data: there is a lack of comprehensive data for pandemics prior to 1960
• Making the Flask/Database/Javascript connection: Until we were able to do it ourselves, we didn't understand how to put all these pieces together.

If we had more time...

• Try to fix delay in epidemic data appearing on globe
• Add more pandemics to list of visualizations for comparison
• Clean up jupyter notebooks to be more efficient