Summary of Project

Our group project addressed the COVID-19 pandemic through data analysis focusing primarily on individual country response efforts and their related outcomes (tests, cases, and deaths).

My specific contribution to this project's dashboard is displayed below. In addition, I also contributed by sourcing and cleaning the raw data using Python (Pandas library), creating the SQLite database and related connectivity through SQLalchemy, performed exploratory analysis using Python’s various libraries, and visually presented all results using the Python library Matplotlib.

Visit our GitHub repo for full project details. To view the full dashboard, please click here.
To compare government responses between countries, Oxford University created an index ("government response index" (GR)) that rated government responses on a scale of 0 to 100. An index of zero meant no measures, whereas 100 meant that the most aggressive measures, relative to all countries, were employed.

The GR index was an aggregate of the indices stringency, health/containment, and economic support. These indices rated government responses by their respective categories (e.g. stringency), also on a scale of 0 to 100. Each index grouped similar government measures. For example, stringency (i.e. behavioural restrictions) includes: school closures, workplace closures, cancellations of public events, restrictions on gathering, public transport closures, stay at home requirements, restrictions on internal movement, and international travel controls.

Stringency Index "Ramp Up" vs Total Cases / Deaths

Each country’s stringency “ramp up” period (the time it takes for each government to implement their most stringent policies - based on Stringency Index) was isolated.

Then each country’s 30-, 60-, 90-, and 120-day reported totals for both total cases and total deaths from the beginning of the ramp up period, as a percentage of population, was captured.

Lastly, the global average for both stringency ramp up as well as for total cases and total deaths was determined in order to compare a country’s efforts against the global average, as well as against other countries.
Analysis for Rate of Increase of Total Cases/Deaths
Investigated whether the (A) slope of the ramp up period, (B) slope of the total cases (or total deaths), together with the (C) length of the ramp up period, could be used to rank each country’s efforts at flattening the curb, ultimately providing an overall country ranking system and thus determining which country’s efforts were successful and which were not.
Ramp Up

Which countries were more/less successful at flattening the curve based on their initial stringency efforts?
Stringency Ramp Up Ranking
(A)+(C)		 Overall Cases Ranking
(A)+(B=Cases)+(C)		 Overall Deaths Ranking
(A)+(B=Deaths)+(C)		 Overall Ranking
(A)+(B=Cases+Deaths)+(C)
Top 10 Countries 1. Jordan
2. Angola
3. Laos
4. Ecuador
5. Kyrgyzstan
6. Rwanda
7. Austria
8. Mexico
9. Ukraine
10. United Kingdom 		1. Laos
2. Angola
3. Jordan
4. Rwanda
5. Uganda
6. Zimbabwe
7. Gambia
8. Eritrea
9. Chad
10. Mauritius 		1. Laos
2. Angola
3. Jordan
4. Rwanda
5. Uganda
6. Zimbabwe
7. Eritrea
8. Cote d'Ivoire
9. Gambia
10. Guinea 		1. Laos
2. Angola
3. Uganda
4. Rwanda
5. Jordan
6. Eritrea
7. Gambia
8. Zimbabwe
9. Sri Lanka
10. Chad
Bottom 10 Countries 153. Suriname
154. Japan
155. Mozambique
156. Malaysia
157. Oman
158. Iran
159. Venezuela
160. Australia
161. Guam
162. Malawi 		153. Italy
154. Suriname
155. Iceland
156. Brazil
157. Oman
158. Singapore
159. Guam
160. Kuwait
161. Iran
162. Chile 		153. Azerbaijan
154. Peru
155. El Salvador
156. Kuwait
157. Suriname
158. Brazil
159. Guam
160. Italy
161. Chile
162. Iran 		153. El Salvador
154. Suriname
155. Iceland
156. Guam
157. Peru
158. Kuwait
159. Italy
160. Brazil
161. Iran
162. Chile
Findings
Is there any relationship between the timing and severity of each countries initial stringency efforts and their outcome of total cases and deaths as a percentage of population?
There is an extremely low correlation between the slope of the ramp up period and resulting total cases and deaths, however when reviewing the results of the top and bottom ranked countries, those with a quicker/higher ramp up period showed more promising results regarding total cases and deaths versus those with longer/lower ramp up periods. There appears to be something behind the numbers that warrants further exploration.

Further avenues for analysis: group similar countries (ex. government structure, GDP, population sentiment towards government, social unrest, etc.) and compare the results of each country within each grouping with the hopes of providing a more equitable comparison.
Ramp Up Correlation