Author: Rahaman Khan, H.; Howlader, T.
Title: Visualizing the COVID-19 pandemic in Bangladesh using coxcombs: A tribute to Florence Nightingale Cord-id: s2b050g5 Document date: 2020_5_24
ID: s2b050g5
Snippet: Following detection of the first confirmed case of COVID-19 in early December 2019 in Wuhan, China, nearly six months have passed and almost every country in the world is battling against the COVID-19 war. The frontline warriors, namely the doctors, nurses and healthcare staff, have in many countries struggled to care for the sick under conditions of limited resources and protection and the threat of an overwhelmed healthcare system. It is during times such as this, that we draw strength and ins
Document: Following detection of the first confirmed case of COVID-19 in early December 2019 in Wuhan, China, nearly six months have passed and almost every country in the world is battling against the COVID-19 war. The frontline warriors, namely the doctors, nurses and healthcare staff, have in many countries struggled to care for the sick under conditions of limited resources and protection and the threat of an overwhelmed healthcare system. It is during times such as this, that we draw strength and inspiration from Florence Nightingale - a passionate statistician, social reformer, feminist champion and a pioneer of modern nursing and data visualization. Nightingale's famed Florence Nightingle Diagram also known as "coxcomb", which was created 150 years ago and used to display the causes of death in the British Army hospital barracks, demonstrated how data visualization techniques could be a powerful medium of communication and a force for change. This paper pays tribute to Nightingale's work by using data from Bangladesh to show that the coxcomb graph is still relevant in the era of COVID-19. The coxcomb graphs that have been produced to display COVID-19 data have provided deeper insights into the trends and relative changes of variables over the course of the pandemic. The paper also describes codes that allow one to easily reproduce the graphs using the statistical programming language R.
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