Our Project addresses this challenge by analyzing the data of human activities, after the announcement of national wide lock-down, how one community by modifying the old social structures and beliefs tackled the COVID-19 virus easily. The old social structures which promote social exclusion, inequalities, unequal wealth distribution among the people makes the community more vulnerable towards the virus. We analysed the data of human movements and activities to find the risk zones. We analysed the data of social reforms like, elimination of Untouchability and elimination of exiling of menstrual women and girls from their home often to a cowshed, helped to decrease the susceptibility of that community towards COVID-19 than the one who didn't implement. Besides this we analysed the data of literacy rates between the communities and found-out the one with higher literacy and awareness dealt with COVID-19 more productively.
The ongoing social differences among people had made the COVID-19 crisis more lethal. Lack of unity among people because of traditional beliefs in social hierarchy and constant social bullying. The rising of inequality among people, socio-economic crisis inspired our team to choose this challenge how human factors can help in spread of COVID-19 virus more rapidly. Our approach towards developing this projects was of data analysis. We analysed the data related to various aspects like literacy and awareness rates, social reforms and human activities among different communities and come up with proper conclusion. We used space agency data in our project for drawing conclusion and coming up with proper analysis. We used Python programming language in our project to simulate the outspread of COVID-19 virus throughout a population. Our team had problem in representing data in an efficient manner.
https://drive.google.com/folderview?id=1Q-kFa6X7cvVdJ4HjoVcmuAPWFJiOg3Gg
IASI CO and NO2 observations in Italy.
Italy number of coronavirus deaths per day from Johns Hopkins University.