Addressing criticisms of COVID-19 reporting through data

Addressing criticisms of COVID-19 reporting through data

Addressing criticisms of COVID-19 reporting through data. A light-touch stab at adjusting doomsday numbers.

The 6 month anniversary of the introduction of the first lockdown measures in the UK is fast approaching, and with it the promise of tightening of restrictions once more, following a period of cautious “return to normal” over the past few months. Most of us have anticipated a second lockdown in some form after the relative frivolities of the summer, so few will be surprised by Boris Johnson’s announcement to re-introduce certain limitations in an effort to curb the spread of the virus. Looking at the alarming rate at which reported positive cases are increasing, this seems like an obvious decision, too:

Image for post

Fig 1 UK Total Cases — source

With the ensuing public concern came a rising sense of skepticism about the reported numbers. Several outlets, including the BBC, have called into question the reliability of these figures, driven by the observation that neither hospital admissions nor COVID-related mortality have increased at anywhere near the rate of reported positive cases:

Image for post

Fig 2 UK Total Hospital Admissions — source

Image for post

Fig 3 UK Total Deaths — source

While all of the challenges laid out by these outlets may not be valid, they do highlight a number of glaring issues with the way these figures are reported and, more importantly, interpreted by the public and seemingly the government. In the next few minutes I attempt to highlight a few of these and offer ways to put these numbers into a different perspective.

prevalence markov-chain-monte-carlo coronavirus python data-science

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

Applied Data Science with Python Certification Training Course -IgmGuru

Master Applied Data Science with Python and get noticed by the top Hiring Companies with IgmGuru's Data Science with Python Certification Program. Enroll Now

50 Data Science Jobs That Opened Just Last Week

Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

Basic Data Types in Python | Python Web Development For Beginners

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Data Science With Python | Python For Data Science | Data Science For Beginners

This Data Science with Python Tutorial will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python.

Data Science Course in Dallas

Become a data analysis expert using the R programming language in this [data science]( "data science") certification training in Dallas, TX. You will master data...