We used data about confirmed cases of Coronavirus and compared these data with temperature, air quality and air humidity data. Thereby we were able to estimate where and when we could have a large increase in numbers of patients.
For the purpose of develop a solution to the challenge presented, we analyzed the current context of the coronavirus and its relationship with environmental factors. In this approach, we are immersed in information from dubious sources or difficult to understand for people who did not have knowledge in the area. For this, we have developed a platform with the objective of providing secure information, which would serve all those interested in the subject, whether to acquire information or to have access to data used in research. In order to guarantee the security of the information exposed, we use databases offered by NASA, to integrate in our maps and graphs information about the conexion between the coronavirus and the environment. In this solution, we use programming tools such as the Python language, API’s, and a future application, Machine Learn. The latter will be used to allow the provision of future forecasts regarding the spread of the virus related to environmental factors. In this way, it is possible to make precise estimates on the planning of vaccination campaigns, according to the priority of the locations that have a more accentuated contamination forecast. During the development of the prototype, we had problems accessing a variety of databases so that we could offer several factors. We had to learn a lot of things from the ground up to have more knowledge over development.
https://data.humdata.org/dataset/novel-coronavirus-2019-ncov-cases
Google’s Geocoding API.