I am reprinting this on my blog: As infectious disease epidemiologists and public health scientists we have grave concerns about the damaging physical, and mental health impacts of the prevailing COVID-19 policies and recommend an approach we call Focused Protection. Coming from both the left and right, and around the world, we have devoted our
Dear Trish, I’m writing to you to express my concern about the medical consensus that has slowly formulated around the use of face masks in public settings as a measure to mitigate against the spread of COVID-19. I read your bmj article on the subject and was frankly alarmed at the arguments you used to
An age of Twitter-fuelled ‘truthiness’ I have rarely felt so profoundly disappointed with the state of the rational world as I do these past few weeks. Perhaps it was my own cognitive dissonance at play, but if you had asked me a few short months ago, I would have rated our level of collective rationality
In order to meaningfully compare costs and benefits of lockdown, it is necessary to have some assumptions about the value of human life. There are different approaches that can be taken to this, such as using the (quality-adjusted) life year thresholds used by public heath systems to ‘price’ a year of healthy human life. Of
Whether lockdowns make sense or not is an important question, and it is hard to answer definitively. Hard, for a number of reasons. First, as is often said, we do not have all the data at our disposal. On the epidemiological side, we are only just beginning to understand how the virus has been spreading
There is a growing body of evidence which suggests that the coronavirus related disease COVID-19, is not nearly as deadly as is being widely reported in the media and as is believed by the public. Here is one of many serologic studies which uses the best scientific evidence available to demonstrate this fact: https://www.washingtonexaminer.com/news/random-sampling-test-shows-coronavirus-more-widespread-and-less-deadly-than-previously-thought-study “Fear
Turning and turning in a widening gyre A conventional narrative among political commentators of our day has been one of increasing polarisation and a collapsing ‘middle’. In economics, leading international think-tanks like the OECD are writing reports with catchy titles like “Under Pressure: The Squeezed Middle Class“. In the media, pundits are falling over each
Divided we tweet It seems that the public debate is divisive as never before. Left versus right. Social Justice Warriors (whatever they are) versus the alt-right (whatever that is). Admittedly, this impression may simply be a result of our unhealthy addiction to social media, which through its Algorithms of Hate and its cloak of anonymity
United we chat; divided we shout at each other I promised myself I would not write a post about the Charlottesville riots, the near universal condemnation of Donald Trump’s nuanced position and the taking down of Confederate-era statues. Yet a recent conversation has forced my figurative hand onto the keyboard. I’ll keep my promise nonetheless,
A gap by Any Other Name… The official website for statistics of the European Union is called Eurostat. If you visit their website, and I highly suggest you do, you will find among the many interesting databases one for the Gender Pay Gap, which measures the difference in average gross hourly earnings between men and
As with birth rates, we use data for 4 categories of countries from 1990 to 2015 (100 observations total). We have two explanatory variables, AGE and Y, where AGE is defined as the percentage of the population aged over 65 and Y is per capita GDP.
After eyeballing the scattergrams, we test the following functional form:
d = (minY^a)/Y^a * (1/AGE^g)
Where minY is the constant equal to the smallest value of Y in the series.
Logarithmic transformation gives:
ln(d) = ln(minY^a) – a*ln(Y) – g*ln(AGE)
which we test on the data using OLS. Here are the results:
Adjusted R square: 75.191
Intercept coefficient: 7.37384
t-Stat: 20.4011
Y coefficient: -1.01444
t-Stat: -13.1059
AGE coefficient: 2.0097
t-Stat: 11.5208
The estimated intercept is a good, but not perfect, approximation of ln(minY^a)
Here are the fitted against actual values of the scattergram for death rate against per capita GDP:
While the results are not as good as with the birth rates calculations, it is nevertheless a good enough fit and the explanatory variables have a strong enough confidence factor to be usable in our estimations.
We begin by examining the scatter of data for 100 observations of per capita GDP and per capita emissions for 4 categories of countries, over 25 years (1990 – 2015).
The scatter suggests a cubic functional form, so we test:
GHG = a + b*Y + c*Y^2 + d*Y^3
where GHG are per capita emissions of GHG, and Y is per capita GDP.
The results from OLS regression are:
Adjusted R square: 0.980438073
coefficient a: 1090
t-stat a: 3.06
coefficient b: 0.709310153
t-Stat b: 8.241453
coefficient c: -0.0000047025
t-Stat c: -1.01233
coefficient d: -0.000000000105314
t-Stat d: -1.47005
While the t-scores on the squared and cubed terms are low, the number of observations are also limited.
Here is the plot of the fitted against actual values:
