The post “Investigating
Wuhan Pandemic Spread and Determinants” discussed the statistical
determinants of the Wuhan pandemic. The analysis used the death incidence as
the dependent variable and tested its susceptibility to changes in different
independent variables. A couple of interesting pointers emerged. The first was
the significant differences in death incidence between advanced economies and
emerging or underdeveloped economies. One of the possible explanations was the
probability of deaths being attributed to several other causes rather than
Chinese virus induced Wuhan flu. There was a possibility that cases were being
under-reported and deaths occurring were delinked from the COVID-19.
Secondly,
contrary to arguments by experts, there was no significance of BCG vaccine
programmes on the death incidence. This was little puzzling given the opinion
of experts. In the meantime there are anecdotal notes about the impact of
temperature on the incidence of coronavirus. It is believed that higher
temperatures reduce the spread of the disease. Yet there were some reports,
rather than higher temperature it is the sunshine that is critical to the
spread of the disease or lack of it. Higher sunshine countries are likely to be
less susceptible to the disease.
There was also
the question about the linkages of incidence to the population. Higher
densities are expected to increase the spread of the disease in contrast to
those regions with low population densities. In Italy, mortality was linked to
the old age population in the country. Higher incidence of old age population
coupled with the co-morbidities they usually carry caused the higher death
rate.
These additional
factors need investigation. Hence there was a refined analysis incorporating
these new variables. There was further need to refine the death incidence to
account for differences in advanced and emerging economies. Hence death
incidence was replaced by case fatality ratio. The case fatality ratio (CFR) is
the ratio of deaths to total positive patients. The data was collected for the
latest date available in each country from databases like John Hopkins and
others. This was tested to its susceptibility to factors enumerated above in
addition to the factors tested in the previous analysis, the link of which is
indicated above.
The variable CFR
was used to denote case fatality ratio and thus the dependent variable. The following
variables were used to denote the independent variables.
ECOTYPE- a dummy
to denote an economy is advanced or not
MALREG- a dummy
to indicate a presence or lack of it of malaraial region
BCGREG- dummy to
indicate countries which have active BCG vaccine programs
BCGMAL- an
interaction variable to test the anecdotal belief that countries with malarial
region are not impacted by the virus given the prevalence of use of
anti-malarial drugs which are now being recommended as cure or prophylaxis for
Wuhan flu.
TESTINCI- number
of tests conducted per million of population
POPINCI- how
many square kms per million people in a country. The inverse of the population
density.
TEMP- average
temperature of a country
AGE- median age
of the country
SUNSHINE- dummy
indicating the country is sunshine rich or lack of the same
The data for the
above was sourced from multiple databases. The age was sourced from UN and
World Bank databases on population and validated. Average temperature of the
country was sourced again from different databases. The data for sunshine rich
countries was sourced from International Solar Alliance database and its
membership was used as proxy for many countries to indicate they are sunshine
rich. The population incidence was calculated after getting the data on density
again from sources like the UN population database. The database on testing
pattern in different countries was taken from Worldometer which tracks the
cases of coronavirus from different sources.
The regression
statistics are indicated in the below table.
Regression
Statistics
|
Multiple R
|
0.387466
|
R Square
|
0.15013
|
Adjusted R Square
|
0.085854
|
Standard Error
|
0.035907
|
Observations
|
129
|
R-square is at
0.15 which is small but either way, some pointers can be obtained by looking
the significant p for different variables.
|
Coefficients
|
P-value
|
Intercept
|
0.050423
|
1.43E-05
|
ECOTYPE
|
0.018445
|
0.056282
|
MALREG
|
-0.00657
|
0.543714
|
BCGREG
|
-0.02711
|
0.004179
|
BCGMAL
|
0.023
|
0.107224
|
TESTINCI
|
-7.7E-07
|
0.002444
|
POPINCI
|
-2.2E-08
|
0.585027
|
TEMP
|
0.004278
|
0.679093
|
AGE
|
0.000274
|
0.975632
|
SUNSHINE
|
-0.00348
|
0.715449
|
The table above
indicates p-significance for TESTINCI which is perhaps obvious. What is however
interesting is the significance of ECOTYPE which is at 95% level compared to
99% level before. The co-efficient too is small compared to the data discussed in
the previous post. There is p-significance
for BCGREG at 99% which is quite interesting. There is no significance for the
rest of the factors.
As one observes
the findings. It is clear that there seems to be no evidence at least on the
first look that the rising temperatures or the presence of sunshine will slow
the down the spread of the disease as measured by the fatality rate. There might
be more to investigate but it appears to be a myth at this moment. The death
incidence might be high in the advanced economies but that could be due to the
higher case reporting. The fatality rate is less significant when compared
between developed and developing countries. Yet, what cannot be ruled out is
the disease has struck the rich countries more than the developing ones. It is
also perhaps due to inbuilt viral immunity in the developing world thanks to
the living conditions that might have created some differences in fatality
rates. However, the difference is not wide as was being in reported in the
previous analysis.
BCG vaccine
seems to play a role in reducing the fatality rate. While the death incidence
might not be susceptible to the BCG vaccine programmes, the vaccine programmes certainly
reduces the fatality rate. As the countries grapple to contain the fatality
rate especially in the US, Italy, Spain, France, Britain among others, it
perhaps would make sense to have a BCG vaccination up and running at least in
the high risk groups to reduce the possible fatality rates. The study might be
a back of envelope mode or exploratory model but the significance level of 99%
cannot be discounted. The coefficient is small, yet there would be perhaps a
benefits relatively more than the costs as one evaluates the BCG programme.
There is also no
significance of age or population density on the fatality rate. Therefore it
can be safely set aside the impact of population and its characteristics on the
fatality rate. However, there might be a need to decompose deeper into those
micro-characteristics to generate some additional insights. The contribution of
this analysis is to examine further and dig little deeper the statistical
models to predict the trends in coronavirus.
Comments
Post a Comment