The project is based on collecting data from various sources from the World Health Organization, John Hopkins University, Weather and WorldOMeter, related to weather, temperature, humidity, wind speed, population density, ages, immigrants, and people culture, in addition to the number of cases, recovery, and death. Corona virus will reduce its spread and predict its end.
we choose this challenge because we believe that all factors mentioned above (weather, temperature, humidity, wind speed, population density, ages, immigrants, and people culture) are related to coronavirus spread depending on our analysis that we did and the results we got.
the approach to develop this project is using Agile Methodology by studying the needs , ask questions classify the requirements , drawing the dashboard.
the tools and programming languages used on this project are Python and Jupyter Notebook , PowerBI Desktop, Excel , PowerBI web server, Apowersoft screen recorder.
Hardware Tools used is my laptop with 16GB Ram , Win10 , Intel Processor Corei7 10th GEN.
the problems starting from finding the way to relate all the mentioned factors all together, the source of data , planning and data engineering.
we cleaned the data , transform and Model them , Using ETL then Visualize it using PowerBI.
some of achievements or findings
1- The covid19 spread to all countries around the world between October and March 2020
2- Most cases are in the Northern Hemisphere, and are relatively cooler at this time of year. The virus may be temperature sensitive, and as summer progresses, we may see a decrease in case growth in the northern hemisphere. Meaning that it is not a good winter for the Southern Hemisphere.
3- With expectations of an increase in cases in the southern hemisphere and Latin America when the onset of winter begins at the beginning of May
4- it seems that the temperature affects the spread but there is no clear relation with the humidity because the humidity is low in china on other hand its high on Europe.
5- the spread of infection increases exponentially relative to population density.
6- More than 50% of all deaths were people aged 80 years or older
WHO - World Health Organization
John Hopkins University
WorldOmeter
Earth Data Earthdata search
Weather Data