“Lies, damned lies, and statistics” — Unknown
Time-series COVID data of confirmed infection and deaths from every single US county can be analyzed using
pandas only. In this article, I will slice and dice the time-series data, plot them, compare them against each other, and present them for your interpretation. COVID data from Corona Virus Resource Center, John Hopkins University is used here for the purpose of demonstration.
At the end of this article you will be able to get time-series statistics like nominal daily new COVID infections for all US counties, and compare COVID statistics of your county with…
In Google Colab environment you can
pip install packages required for your code. You need
shapely for this project.
!pip install plotly-geo!pip install geopandas!pip install pyshp!pip install shapely
After installing these packages, git clone data repository to import COVID data. I have build a data repository with submodule that takes the latest data from John Hopkins University’s COVID database and import necessary packages.
# Clone repository!git clone https://github.com/sidbannet/US-political-analysis.git%cd ./US-political-analysis!git submodule…
If you are a public official and looking for a playbook on things not to do in a pandemic, look no further than America’s failed response to the coronavirus crisis. Half baked, arbitrary, un-coordinated, incoherent public policy compounded with lack of leadership at the top caused America to surrender to this virus. When the President of the United States started a culture war over mask coverings, it surely resonated with his supporters. People in Trump leaning counties stopped adhering to their local and federal public safety guidelines of wearing masks or maintaining social distancing. Who can forget images of supporters…
Why region-based, data-driven decision is warranted to open the world’s biggest economy— and why Decatur, IN is a current COVID hotspot while San Francisco, CA is not
“In God we trust, all others must bring data.” — W. Edwards Deming
Questions of the day are: when and how are we going to open the country after weeks of lockdown to mitigate COVID-19 spread? Are the mitigation measures working? What can we learn from the data so far? To answer that, visualizing data can be very important in giving us a greater picture of the state of affairs during this pandemic…
I developed a python codebase and published it in my GitHub repository to present spread of novel corona virus using database maintained by John Hopkins University. In this article, I do not present any projection models, but instead focus on presenting the data in a meaningful way intended to draw evidence-based judgement of how the novel corona virus infection is spreading in different geographical areas. Although there is clear sign that official numbers are grossly under-reported, this codebase intends to democratize official data and analysis toolkit on COVID-19 trends.
The data is presented in geo-scatter animation of reported cases of…
If remains unconstrained, epidemics like COVID-19 grows exponentially in infecting population. Using data-science and mathematics, one can derive simple projections of spread of highly infectious disease in a population.
Here is a graph showing total number of people affected by COVID-19 outside of China between mid-February and early-March of 2020, using World Health Organization’s database. It shows how rapidly COVID-19 had spread so far in population. This is a typical example of exponential growth of viral infection spreading across population. …
Computational physicist | Data science communicator | Author | Inventor | Life Enthusiast | Indian Classical Musician