Our team, Grandpas’s Space Ship, is developing the COVID Prediction Dashboard, a platform that allows a better understanding of the impact of certain actions in relation to COVID-19. Our goal is to help business owners with large numbers of employees, and city and nation governments, so that a change in isolation is taken in the best possible way. Using data from the past to predict future contagion situations, we can more simply describe the different scenarios to be achieved, depending on the way that social isolation is treated.
The inspiration of our team comes from the way that data is shown to us, we want to integrate it to have a different view from the traditional, in order to understand it better helping people to make decisions about strategies to contain the spread of the COVID-19.
Our team focused mainly on measures to contain the spread of the virus, so we started to pre-process the databases to look for correlations between socioeconomic data and COVID-19 spread data, after experiments we concluded that there was a negative correlation how positive among such attributes. For these reasons we started looking to look to the future and thus we arrived at prediction models by machine learning.
In our application we used data from the space agency EU Open Data Portal,Base U.S. Bureau Of Labor Statistics, Microsoft/Bing and Google COVID19 mobility, E. These data allow the graphics to be assembled and provide us with a basis for forecasting future data.
For the development of the application the languages TypeScript and JavaScript were used, NodeJS for the backend and AngularJS for the frontend, the development environment was Visual Studio Code, the hardware used was a computer with intel i7 7500u, 12GB, GTX 940M 2GB under Ubuntu 18.04 OS.
Our team had problems in the data pre-processing phase, where most of the bases had large differences in attributes, the implementation of MVP had complications during the integration of technologies, our main achievements were the deliveries carried out efficiently and completely, as well as our MVP, which was completed satisfactorily, showing that we are able to fully carry out the project.
EU Open Data Portal - Import prices in industry
EU Open Data Portal - Living conditions
Effects of COVID-19 Pandemic on Employment and Unemployment StatisticsGoogle COVID-19 Mobility
1.Henrique, Bruno Miranda, Vinicius Amorim Sobreiro, and Herbert Kimura. "Stock price prediction using support vector regression on daily and up to the minute prices." The Journal of finance and data science 4.3 (2018): 183-201.
2. European Open Data Portal – Living Conditions, functional urban areas. <https://data.europa.eu/euodp/en/data/dataset/ojAmzVahjBnws2njEN0qhQ>
3.European Open Data Portal – Import prices, total industry.
<https://data.europa.eu/euodp/en/data/dataset/TiMiIjYp3OfBS2vYNjQg>
4.European Open Data Portal – Import prices in industry, quarterly data.
<https://data.europa.eu/euodp/en/data/dataset/TW8FHXr0xLTT9tkZcNhTA/resource/b99d7480-e85d-44fb-b724-b169ae53fc61>
5.Base U.S. Bureau Of Labor Statistics – The Employment Situation, April 2020. <https://www.bls.gov/news.release/pdf/empsit.pdf>
6.U.S. Bureau of Labor Statistics – Average weekly hours and overtime of all employees.
<https://www.bls.gov/news.release/empsit.t18.htm>
7.Wikipedia - National respondes to the COVID-19 pandemic.
<https://en.wikipedia.org/wiki/National_responses_to_the_COVID-19_pandemic>
8.Google – COVID-19: Relatórios de mobilidade da comunidade.
<https://www.google.com/covid19/mobility/>
9.GitHub - Bing COVID-19 data.
<https://github.com/microsoft/Bing-COVID-19-Data>
10. BBC – Coronavirus: The world in lockdown in maps and charts.
<https://www.bbc.com/news/world-52103747>
11.Trading Economics – Countries Unemployment Rate.
<https://tradingeconomics.com/countries>
12. Federal Reserve Bank of St.Louis – Population of Countries by Year.
<https://fred.stlouisfed.org/>
13. Worldometer - COVID-19 Coronavirus Pandemic.