We intended to answer the question "Is there any prooved way to assess quarantine quickly and efficiency in different areas according to the satellite data of cities brightness and gas contents?”. We have also concluded how can the change in brightness correspond to the change in human activity. We discovered that quarantine restrictions make a major difference in CO concentration only while having almost no impact on O3 and UFPs.
Overall, we have included multiple factors building the chain to predict the COVID-19 spreading.
What inspired your team to choose this challenge? What was your approach to developing this project? How did you use space agency data in your project? What tools, coding languages, hardware, the software did you use to develop your project? What problems and achievements did your team have?
During the project advancements, we have used Intelij Idea primarily to develop a Java app for building multigraphs and analyzing data. We created SIR mathematical model, that predicts the spread and epidemiological situation. Firstly, we derived the empirical dependence of the brightness of the city on its population. For this, we used ImageJ software. Total brightness was estimated as Area(in pixels)*mean brightness, thus we found almost linear dependence of the brightness of the region on its population (we implemented MS Excel and Google sheet functions to assess linear trends). Also, it’s known that there is a dependence between the size of the city and the average number of social contacts between people (“The scaling of human interactions with city size” - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4233681/). The number of social contacts, in turn, influences the speed of the virus spread.
By analyzing data of different emissions we detected that significant changes could be observed in CO concentration only. Thus, we compared CO concentration changes and the number of social contacts between people. Hence, we detected that the average number of social contacts between people is proportionally dependent on the changes in CO concentration - the data gathered by NASA satellites.
Overall, we got the coefficient of transmission between people, which we inserted in our model. As you can see on the last slide, our model does a good job of predicting the future prognosis of the pandemical situation and can be used for further investigation.
https://docs.google.com/presentation/d/1QTc3fi_vE2X2gBVc-V8UGVXwS53yagKzxepbxrC0Nx0/edit?usp=sharing
Datasets:
https://data.europa.eu/euodp/en/data/dataset/covid-19-coronavirus-data - Covid19 cases
https://aqicn.org/data-platform/covid19/ - air quality during the covid 19
The code:
https://github.com/Antebe/hackathon
Used resources:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4233681/
https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology
https://scipython.com/book/chapter-8-scipy/additional-examples/the-sir-epidemic-model/
https://earthobservatory.nasa.gov/features/NightLights
https://www1.nyc.gov/site/doh/covid/covid-19-data.page