Transforming Spreadsheet Data to Python Code for Simplified Covid Monitoring
In the ongoing battle against Covid-19, data analysis plays a crucial role in understanding the disease's impact on the UK. Python, a versatile programming language, has proven to be an effective tool for analyzing and visualizing Covid-19 data, including hospitalization rates and the ratio of cases to hospital admissions.
The author began analyzing the ratio of cases to hospital admissions from September 1st, 2020, using the Pandas and Matplotlib libraries for data storage and plotting, respectively. The data was sourced from the UK government, primarily through the NHS or Public Health England, and was often available as downloadable CSV files or accessible via APIs.
By cleaning and structuring the data using pandas, the author calculated key metrics such as the hospitalization rate and case-to-hospital admission ratio. The hospitalization rate was calculated as the number of hospital admissions divided by the number of confirmed cases over a specified time period, while the case-to-hospital admission ratio was computed by dividing total cases by hospital admissions to understand severity or healthcare burden trends.
The author produced a plot showing the number of Covid cases per day, indicating a significant increase recently. A second plot showed the hospitalization rate over time, revealing that in late 2020, one in 15 people who caught Covid-19 in the UK went to hospital.
Interestingly, the peak in hospitalizations was observed to be 8 days after the peak in cases. This delay is an essential factor to consider when analyzing hospitalization trends.
The author also noted that the ratio of cases to admissions improved to about one in 40 in mid-2021, presumably due to vaccination. However, the author cautioned that more data are needed before firm conclusions can be drawn.
As the Omicron variant becomes more prevalent, the author is currently interested in how it is affecting case numbers in the UK. The author finds it easy to re-run the code and update the graphs whenever desired, which would be more difficult with manual spreadsheet downloads.
By leveraging Python's data processing and visualization libraries along with UK government and NHS trusted datasets, the author can conduct detailed and insightful analysis of Covid-19 hospitalization metrics and case severity indicators efficiently and effectively. For more advanced insights, the author suggests using linked datasets available through platforms like OpenSAFELY, which integrate hospital records, testing, and vaccination data in a pseudonymised and secure way, enabling stratification by demographic factors or vaccination status for deeper understanding of hospitalization risk.
Science plays a pivotal role in the medical analysis of various conditions, such as Covid-19 in the UK. With the help of data-and-cloud-computing technology and Python, a versatile programming language, the author is able to utilize data from the UK government and NHS, such as hospital admission ratios and case numbers, to conduct detailed analysis and visualise trends in medical conditions like Covid-19, leveraging libraries like Pandas and Matplotlib for data handling and plotting.
