Our project integrates the NASA climate maps: NO2 Total, Surface Temperature, and Relative Humidity at Surface with John Hopkins Coronavirus Resource Center dataset about the transmission of and deaths caused by SARS-CoV-2 in order to comprehend the impact of climate and the spread dynamic providing support for the prevention and improve public decisions and actions
- Challenge
Our challenge is to integrate various EO-derived features with available and/or derived socio-economic data in various ways in order to discover or enhance our understanding of COVID-19 impacts.
- Background
COVID-19 is characterized in Brazil as an infectious disease with strong infectivity and difficult detection, which has led to the rapid development of an epidemic environment with inefficiency and delay in public policies. One of the reasons for this late response of the Brazilian public institutions was the premise that the spread dynamic of the SAR-CoV-2 was slower in tropical environments. Besides the constant and categorical affirmation of this idea by Brazilian congressmen, the relation between climate data and virus transmission is not totally supported by the medical literature. This situation requires better data analysis to allow an efficient supply of information about the spread dynamics and its relation with temperature and humidity to provide support for the prevention and improve public decisions and actions.
- Our solution
We idealized a platform that correlates transmission of and deaths caused by SARS-CoV-2 provided by the John Hopkins Coronavirus Resource Center with NASA climate maps generating data that could predict the impact of weather variables in the disease spread. This project could provide indications of the success of public policies through time, generate reliable data to identify clusters of contagious based on climate data, therefore, improving the distribution of financial resources and support public decisions. This platform has as stakeholders the governments and private companies that deal with decisions based on the spread of COVID-19.
- NASA Resources
- Overview of our build
The platform uses 2 data sets:
Presentation
https://drive.google.com/file/d/19gFkaU7uWTYcX6U4sVhkCN_UT2k0XytS/view?usp=sharing
Results
https://drive.google.com/file/d/1cGGgPrisSapI84MNOQ_x1WCtXIsCRAR9/view?usp=sharing
https://github.com/rahyanazin/losbuenos
Deaths Vs. NO2 Emission
https://images.spaceappschallenge.org/stream-images/TIEh3PnXnYRyYOmkmiZAVgCV_3I=/7563/width-800/
Deaths Vs. Weather
https://images.spaceappschallenge.org/stream-images/9pclBzqG1QgMavgrC0FadyB38jg=/7562/width-800/
In order to understand the spread of Covid-19, we clustered the most impacted 20 countries by weather (temperature and humidity) and production of NO2 (as a proxy of the effectivity of lockdown) and plotted to reach out to the impact of these variables in the spread of Covid-19 for the first 70 days of the disease. We conclude that the lockdown is related to the decrease of deaths by COVIS-19, besides wet countries are the most impacted ones.
- Impact
We generated the correlation model
Through the data generated, we could:
- Future Plans
In the present moment, our project deals with the generation and analysis of past data. Our propose is to develop a platform that generates real-time data that could increase the mechanisms of disease spread predictions. Further, delivery to the Brazilian government a more reliable source of data to the prevention of new cases. Lastly, we hope to help the prediction of the infection through time and space in Brazil and be useful in the elaboration of better decisions.
- NASA Resources
- John Hopkins Coronavirus Resource CenterTransmission of and deaths caused by SARS-CoV-2: