The Event Horizon| An Integrated Assessment
Awards & Nominations
The Event Horizon has received the following awards and nominations. Way to go!
An Integrated Assessment
Quarantine: Deal or No Deal?
Summary
Our goal is to develop a COVID-19 Preparedness Index Calculator. We looked at Earth observation data as well as socio-economic data to study the impacts of covid-19 on the economy, the environment and global health. Then we looked at 21 states and examined which actions and preparedness measures helped minimize negative impacts of the virus. Our final goal was to come up with a “golden standard reaction” that would not only promote health but also minimize any negative socio-economic or environmental impacts. Our calculator will take in a country's data (from air pollution, to stock market, to governmental trust and approval ratings) in order to determine this standard.
How We Addressed This Challenge
Currently we are using Earth Observation data (pollution and CO data) integrated with economic data (stock market), population density, unemployment rates, method of control and political aspects of the country compared to their coronavirus cases to enhance our insight in understanding the "golden standard" of how to handle a pandemic, and if that country exists.
Our application can be used to help countries currently learn from others in order to help save their economy, control the spread of the coronavirus in their region, and to better prepare themselves for future pandemics, with the concept of a golden standard that we will create, that can be adapted to each country based of off population density.
We considered the vegetation index as there have been many reports of how animals are exploring more city grounds since there are less people outside of their homes. We found with the majority of the countries that we had selected generally transition between winter to spring/summer, that would reveal a great increase in vegetation, but it is unclear whether the increase in vegetation is due to weather or the coronavirus. Because of this, we decided it was best not to include it in our analysis to avoid bias.
We did however consider air quality and carbon monoxide levels, as that would help indicate human activity such as the use of motor vehicles, factories, and so on. With this, we can help compare the country's statement on how they handle the pandemic (herd immunity, or full quarantine, and everywhere in between) and the activity that was actually going on. This can also relate to the political views and trust in the government within the country - if there is a low trust in the government there is likely to be a higher number of people who disobey any lock down orders, for example. This will be exposed in the CO and air quality data. We can then compare this to the number of coronavirus cases per million. This way, we can find out the best way to lead a country during a pandemic, based on our findings.
How We Developed This Project
A global pandemic is a multifaceted challenge that is being dealt with by all nations around the globe. Nations who were more prepared and made smart decisions are better equipped in their fight to mitigate the damages of COVID-19. In order to determine a golden standard on how to be best prepared and make smartest decisions during a pandemic we first must understand the virus and its impacts on the society, the economy, the environment and the global health. The integrated assessment provides a unique opportunity to look at these impacts by merging earth observation data with socioeconomic data to provide a better understanding of COVID-19.
The most technical component of our project was the extraction of raw data from the CSA Open Data server, specifically from the shared satellite, MOPITT in order to determine the change in CO emission levels across the globe from March/April of last year and March/April of this year. The downloaded files were .csv format with 200,000 rows of pertinent data of daily pass throughs. When we ran a python pipeline to plot the data, we got visualization only in the path that the satellite followed and so, we did not have a clear mapping on a global scale. To fix that, we had to combine with unix utilities all csv files from each day to create one large appended monthly file that can then be plotted with matplotlib library. Beyond the plots, the real challenge now was to now open the massive 500mb csv files and interpret the data on a country by country basis by pulling CO values from the appropriate latitude and longitude values corresponding to the 21 diverse countries we chose.
The intention with our final solution for a ‘COVID-19 Preparedness Index Calculator’ was to be developed in JavaScript and provide an output of how prepared a country is based on multitude of analysis factors from our research as an input. We combined some seemingly overlooked factors so that we may make a clear distinction of every component that contributes to preparedness. We were however able to get data on the majority of the factors we were initially looking to consider, and have made some conclusions as to which countries seem to closest fit the idea of the “golden standard” - where a country goes through a pandemic with little to no negative economic, social, or environmental impact while containing the spread of the virus. We do not have a working calculator, but we did find all of the tangible data we could in order to rate the countries that we have chosen to analyze, and determine the qualities of a golden standard country when it comes to handling a pandemic. Our results are shown on our webpage, under the "Observations + Conclusions" tab.
We also wanted to include a deeper look into the politics surrounding each country during the pandemic. This way, each country can find it's own gold standard with our calculator, based off of a number of factors, including the current political government in power and population density. We thought of this because from some of our research, we had noted that countries with more far right/left wing governments often (but not always) have less support from their residents, and therefore for example will disobey a quarantine law for example in order to counteract the government, leading to a greater spread of the virus. We also wanted to see if the more relaxed governments that had fewer laws surrounding quarantine and deemed quarantine more or less a “recommendation”, if the residents actually followed the recommendations even if there was no governmental penalty for disobedience, simply based on a trust and honour system.
There is also very important data for our calculator that we simply could not access. This was the poverty data. As mentioned on our website, the unemployment data only records those who are unemployed and who are looking for a job. This does not include those who are unemployed and who are afraid of working due to the coronavirus. Without poverty data, we cannot accurately see the true depth of the unemployment data, as people who fear for their lives and would rather go into poverty from being unemployed for a long period than risk getting the virus are not represented in the data.
Project Demo
Our app: https://theeventhorizon.co/index.html
Video Demo:https://drive.google.com/drive/folders/1JyMwC59Md1kDPBvpLbO0K6iCnh-kdMWz?usp=sharing
Data & Resources
- Data for my covid tracking graph:
https://ourworldindata.org/grapher/covid-contact-tracing
- Originally sourced from:
Thomas Hale, Sam Webster, Anna Petherick, Toby Phillips, and Beatriz Kira (2020). Oxford COVID-19 Government Response Tracker, Blavatnik School of Government.
- For the travel restriction world plot:
https://docs.google.com/document/d/1Re54ag3v0bJoPbfUGzRZ1tXy9MYxgfumJ0XnbgTzxSU/edit?usp=sharing
- Data for average tests conducted per new confirmed cases for each country:
- Original data was taken from: https://ourworldindata.org/coronavirus-testing
- The number of tests each day divided by the number of confirmed cases each day.
- In post processing we took the average of all the tests conducted per new confirmed cases during the pandemic.
CSA Open Data Server - MOPITT : Grabbed the csv files that were converted from .he5 format and compiled them into monthly csv file, then plotted and pulled data for individual countries.
Source for Canadian stock market data :https://tradingeconomics.com/canada/stock-market
Source for South Korean stock market data: :https://tradingeconomics.com/canada/stock-market
Source for USA stock market data :https://tradingeconomics.com/united-states/stock-market
Source for Mexico stock market data :https://tradingeconomics.com/mexico/stock-market
Source for UK stock market data :https://tradingeconomics.com/united-kingdom/stock-market
Source for Spain stock market data :https://tradingeconomics.com/spain/stock-market
Source for Italy stock market data :https://tradingeconomics.com/italy/stock-market
Source for Russia stock market data :https://tradingeconomics.com/russia/stock-market
Source for Germany stock market data :https://tradingeconomics.com/germany/stock-market
Source for Israel stock market data :https://tradingeconomics.com/israel/stock-market
Source for Norway stock market data :https://tradingeconomics.com/norway/stock-market
Source for India stock market data :https://tradingeconomics.com/india/stock-market
Source for Nigeria stock market data :https://tradingeconomics.com/nigeria/stock-market
Source for Brazil stock market data :https://tradingeconomics.com/brazil/stock-market
Source for China stock market data :https://tradingeconomics.com/china/stock-market
Source for Egypt stock market data :https://tradingeconomics.com/egypt/stock-market
Source for South Africa stock market data :https://tradingeconomics.com/south-africa/stock-market
Source for Iran stock market data :https://tradingeconomics.com/iran/stock-market
Source for Sweden stock market data :https://tradingeconomics.com/sweden/stock-market
Source for UAE stock market data :https://tradingeconomics.com/united-arab-emirates/stock-market
Source for Canada unemployment rates: https://tradingeconomics.com/canada/unemployment-rate
Source for South Korea unemployment rates: https://tradingeconomics.com/south-korea/unemployment-rate
Source for USA unemployment rates: https://tradingeconomics.com/united-states/unemployment-rate
Source for Mexico unemployment rates: https://tradingeconomics.com/mexico/unemployment-rate
Source for UK unemployment rates: https://tradingeconomics.com/united-kingdom/unemployment-rate
Source for Italy unemployment rates: https://tradingeconomics.com/italy/unemployment-rate
Source for Russia unemployment rates: https://tradingeconomics.com/russia/unemployment-rate
Source for Germany unemployment rates: https://tradingeconomics.com/germany/unemployment-rate
Source for Israel unemployment rates: https://tradingeconomics.com/israel/unemployment-rate
Source for Norway unemployment rates: https://tradingeconomics.com/norway/unemployment-rate
Source for India unemployment rates: https://tradingeconomics.com/india/unemployment-rate
Source for Brazil unemployment rates: https://tradingeconomics.com/brazil/unemployment-rate
Source for China unemployment rates: https://tradingeconomics.com/china/unemployment-rate
Source for Australia unemployment rates: https://tradingeconomics.com/australia/unemployment-rate
Source for Egypt unemployment rates: https://tradingeconomics.com/egypt/unemployment-rate
Source for Sweden unemployment rates: https://tradingeconomics.com/sweden/unemployment-rate
Accurate Population density by country as of 2020:
https://www.worldometers.info/world-population/population-by-country/
Used in order to have a better understanding of the COVID-19 situation in each country using a proportion of how many cases are in the country and how large the population is. Used as a better metric than total COVID-19 cases since it scales the various population sizes of countries and can help better understand the reason a country’s COVID-19 situation is the way it is.
Number of COVID-19 Cases per million people per country
Used in order to create the graph for each country of “Total Confirmed Cases of COVID-19 per million People vs Time”.