COVAPP

COVAPP| Human Factors

Human Factors

The emergence and spread of infectious diseases, like COVID-19, are on the rise. Can you identify patterns between population density and COVID-19 cases and identify factors that could help predict hotspots of disease spread?

Summary

Our team is focused on providing useful information about the global pandemic in a mobile and webpage application. Starting by creating the Covmap showing a global map with the vital information of confirmed cases, deaths, and recovered of each country with daily updates of the information. The other part of Covapp is to use the valuable information that a high concentration of people increases the possibility of spreading the virus so our app intends to track people and when they are 10 or more people in a certain place such as a park, supermarket, etc it warns you about a possible hotspot so you can avoid it. in the future but that is not the entirety of the project. AI will be used.

How We Addressed This Challenge

The mobile application will prevent people to go to places when there is a big concentration of people, preventing the spread of the virus. It will also show the actual number of cases of the pandemic and try to identify future hotspots with machine learning.

How We Developed This Project

With all the actual information we noticed that human activity is the main factor of the pandemic and the speed in which it spreads all around the world. Our app, named Covapp, is focused on being a tool for the people, for them to avoid getting in contact with others that could be infected. For our application, we use the NASA Socioeconomic Data and Applications Center (SEDAC), in which it shows the actual cases all around the world. We will also use a local GPS that shows the amount of people in the same public spot. The localization of the user will be off once they are in their homes for privacy, but once they get into a public place location will be on. A survey will ask daily to each user about any symptoms and if they are possible virus carriers they will be marked in yellow in the map, and red if they are confirmed infected. The Covmap was our own version of the pandemic data in a map with each country with the perspective cases, deaths, and recovered patients, we use a database for having daily updates of the cases. We also plan to create a Machine Learning model to identify future hotspots in specific areas by using specific parameters such as precipitation and temperatures that numerous articles indicate that there is a correlation with covid-19. At last, we developed a webpage in which we explain our services and objectives; also, in the covapp webpage, you can enter to covmap, covspot, and covapp.

Our webpage is the following:

https://covapp.co/index.html

Project Demo

https://docs.google.com/presentation/d/1FTUZ_FuIMlLnKoVx8D8KFmgAlQqcP1ksO31i_mYRz3Y/edit?usp=sharing

Project Code

https://github.com/BernardoPeralta/Covid-19-Tracker

Data & Resources

https://www.euromoney.com/article/b1lj77ns18lmg3/covid-19-quotes-of-the-month-may-2020

https://ds.nccs.nasa.gov/thredds/catalog/bypass/NEX-GDDP/bcsd/rcp85/r1i1p1/pr/catalog.html

https://ds.nccs.nasa.gov/thredds/catalog.html

https://svs.gsfc.nasa.gov/31142