I have written a CORONAVIRUS COVID-19 R shiny app that shows the deaths in Scotland adjusted by population. By this measure Drumchapel South, was worst hit by Covid-19.
Google Covid-19 Mobility data is obviously Android users and Apple Coronavirus/Covid-19 mobility data IOS users. So the data is in that sense determined by the type of personality that uses each OS and how they care about privacy.
Continue reading Coronavirus Covid-19: A brief look at who is detected in the Apple and Google Mobility Reports
This article will try to answer the question if a regions Brexit vote determines adherence to lock-down. In very simple terms this means if a region has voted for Brexit with a higher percentage does show more mobility.
Many scientific studies have looked at Brexit. Reasons listed are:
Age of voters
Order vs openness
The “left behind”
Britons felt less integrated into the EU
Identity and Change
English national identity
Since all this research classifies the “classic Brexit voter” as an old, low educated, xenophobic, anti-establishment, English nationalist populist, it would be interesting if these people show a different behaviour to younger, higher educated, inclusive, establishment friendly non English nationalist reasonable voters. Please see the ecological fallacy and Robinson’s paradox.
Continue reading Coronavirus Covid-19: Brexit vote determines adherence to lock-down
Springer Nature has released 407 free books to help everybody with the knowledge around Coronavirus and Covid-19. This includes many disciplines and 65 books on data science.
Continue reading Coronavirus Covid-19: Springer releases books of essential textbooks from all disciplines
Introduction to Mobility and Covid-19
This article sets out to analyse Mobility and Covid-19 case numbers, or in more detail, the effect of changes in mobility between different sub-regions of the UK on these sub-regions’ Covid-19 case loads.
The Coronavirus pandemic has led to many countries introducing lock-downs and restrictions on movements of citizens to reduce interactions between people, so that the SARS-COV-2 virus can not spread. In the United Kingdom this lock-down started on March 23, 2020. This led to a decline in mobility in many regions. Measuring movements on a large scale like this is impossible for single individuals or small companies.
Continue reading Coronavirus Covid-19: Mobility and Cases, why shopping is more dangerous than going to work.
There is a lot of discussion about mortality rates, population factors, population density and health systems around in this coronavirus, Covid-19 pandemic. Doubtlessly some are contributing factors but are they on they exclusively so? Their is a certain fallacy of some simple arguments why countries differ in this pandemic. That they differ is obviously not under question but why they differ is a very complex matter, on which doubtlessly many billions will be spent over the coming decades going to the best connected academics in each country.
Please see this paper for detailed analysis on mobility and influenza.
Please go here: Paper on Researchgate
This article will discuss if there are any rational arguments to distrust any numbers given during the corona virus crisis by individual countries based on Benford’s law.
China is often accused that it forges or manipulates it’s data. That there are possible millions of undetected cases. There are even scientific papers about this (1). But no one seems to look at how other countries fare by this standard. Hence we will have a look at how China’s reporting fares if compared with 6 European countries and the USA via the Newcomb–Benford law.
One can use two data sets to analyse this data, the cumulative data set and the daily reports, not cumulative. The inherent problem with Benford’s law in an ongoing pandemic is WHEN do you take the analysis. The results of the Benford’s analysis can change daily.
So what will our societies look like after the coronavirus/COVID-19 pandemic when artificial intelligence (AI) is everywhere?
Expect more #techlash when more and of the non tech oriented population understands their life is ruled and ruined by algorithms largely based on profiteering. Additionally enhancing people’s often faulty preferences with recommender systems or looking for conflict and “engaging” subjects like FB will further polarise societies.