High-Level Project Summary
CoWIN is an app developed by ThreeSmart, that allows the user to be aware of the well-known Covid-19 virus, that has conquered the whole world. The app provides you with individual information about the risk of catching the Covid-19 virus, what steps to follow to be protected, what kind of places to avoid during the pandemic, and so on. The information is generated out of your personal and publicly available environmental data. Our app will help you avoid crowded places thus decreasing overall virus spread.
Link to Project "Demo"
Link to Final Project
Detailed Project Description
After successful registration, the user is prompted to login and enter their personal data such as age, biological sex, ethnicity/race, and diseases. By using ML/AI models the data is processed and the risk factor is calculated for each user. The risk factor gets combined with social activity information of the user's current location and gets displayed on the map. Usually catching Covid is due to not knowing the environment and being in places where you shouldn't be. The biggest benefit of our app is that it uses social activity data rather than only Covid rate data because the main reason for the virus spread is being in crowded areas and not keeping social distance.
Here is shown the social activity data for Washington D.C., by knowing the crowded areas, our users can avoid these places and prevent the virus from spreading.
Our mission is to raise social responsibility awareness so that people can truly understand the risk involved in catching the virus and finally take steps to stop and prevent the virus from spreading furthermore.
The base application is written in Java (Android Studio). The UI is built using Adobe XD and then integrated to Android Studio. The main map is from Google. For data pre-processing and analysis we have used Python(Tensorflow, Sklearn, OpenCV, Numpy, Pandas, etc. ). We have trained models on mobile workstations for individual and environmental data analysis. For analyzing geospatial data we have used Quantum GIS software.
Space Agency Data
For individual risk factor calculation model development, we have used information from the Centers of Disease Control and Prevention.
The social activity data was acquired from the Strava Global Heatmap. This data has been initially analyzed and rastered using Quantum GIS afterwards processed using Python-OpenCV then converted into a Geo-JSON file containing social activity data points.
We have thoroughly analyzed the weather data from NOAA GSOD, and have found no correlation between Covid cases and the weather.
Hackathon Journey
During the SpacApps challenge, we learned how to develop a project in an online environment which is totally different from working with a team offline. Similar hackathons allow teams to work hard in stressful and extreme conditions to solve real-life problems.
We have learned time management which is crucial when developing something within a specified deadline, in this case in just 48 hours. We have learned about satellites and space technologies which enable us to solve issues in today's social world. We have tried working together as much as possible and being notified about what each and everyone is doing so that splitting the tasks could be effective.
References
https://covid.cdc.gov/covid-data-tracker/#vaccinations_vacc-total-admin-rate-total
https://www.strava.com/heatmap
https://data.noaa.gov/datasetsearch/
https://developer.android.com/
https://www.jetbrains.com/pycharm/
https://opencv.org/
https://www.qgis.org/en/site/
https://www.tensorflow.org/
https://scikit-learn.org/stable/
https://www.adobe.com/products/xd.html
https://openweathermap.org/api
https://developers.google.com/maps
Tags
#cowin, #AI, #ML, #prediction, #covid19, #virus, #mask, #covid
Global Judging
This project has been submitted for consideration during the Judging process.