CORONAVIRUS
PREDICTIONS: TECHNICAL VERSUS
ASTROLOGICAL ANALYSIS:
In my blog written on 19th
May 2020, I had attempted to make an astrological prediction on the end of
Corona virus based on the planetary transitions in which I had mentioned
phasing out the corona virus after September end.
Being a technical person, I
was tempted to do a technical analysis and predict the end of corona virus and
my focus is more on the Indian scenario as it’s the most unpredictable one at
the moment.
Many Astrological predictions
have failed before as Corona has no date of birth, place of birth and time of
birth. All astrological predictions are based on the planetary transits or
eclipses. Similarly many mathematical models have failed to predict about
corona because of several reasons.
I have attempted to make a
mathematical model taking into consideration the weakness of earlier models and
incorporating many practical factors which other models failed to incorporate.
Let me pen down the factors,
assumptions and procedure that I have incorporated into my model for predicting
the end of Corona virus (Specifically for India)
1.
I have analyzed
the Corona cases (date wise) for countries whose graphs have already flattened
or have started to flatten. These countries include Spain, Italy, France, South
Korea & China. We have not included New Zealand here as there were just 1500
cases there. The above countries have been considered as the base countries for
my model.
2.
Below are the
graphs of the base countries with flattened corona virus cases curves (or about
to flatten)
ITALY
Italy has a population of about 60 million (6 cr) and started its
lockdown very late after the outbreak of corona virus. It had a lockdown of
about 52 days and per million population infected is about 3842. The R naught
number which started from 2.8 was brought down to 0.9.
SPAIN
Spain has a population of about 47 million (4.7 cr) and started its
lockdown very late after the outbreak of corona virus. It had a lockdown of
about 48 days and per million population infected is about 5091. The R naught
number which started from 4.7 was brought down to 1.
France has a population of about 67 million (6.7 cr). It had a lockdown
of about 52 days and per million population infected is about 445. The R naught
number which started from 2.7 was brought down to 1.
South Korea has a population of about 52 million (5.2 cr). It had no
lockdown and per million population infected is about 230. The R naught number
which started from 3 was brought down to 0.7.
GERMANY
Germany has a population of about 83 million (8.3 cr). It had a lockdown
of about 34 days and per million population infected is about 2219. The R
naught number which started from 2.9 was brought down to 0.7.
INDIA
India
has a population of about 1300 million (130 cr) and started its lockdown from
23rd of March 2020. It had a lockdown of about 70 days and per
million population infected is about 209. The R naught number which started
from 1.83 was brought down to 1.22 (as on 12th June 2020).
3.
My model has
given weight-age to the following factors:
a)
Lock-down period
of the countries taken as base countries.
b)
Social structure
of the countries.
c)
R naught number
of various countries.
d)
Recovery rate of
various countries.
e)
Immunity of the
people in those countries (based on the research of the previous history).
f)
Population of
those countries (I have given very little weight-age to this as Corona data of
China may or may not be correct).
g)
Climatic
conditions of those countries.
h)
Previous vaccines
given to the people like in India most of the people have taken BCG vaccine.
i)
Discipline in the
lock-down in those countries
j)
Per million
infections in various countries.
4.
The approach was
to gather the corona cases data (date wise) of the base countries and India in
excel and develop two graphs, one of the corona cases versus date and the
second one of the Daily cases versus date.
5.
Earlier
mathematical models have developed differential equations with variables such
as Susceptible (S), Infected (I) and Recovered/died (R). But these models
failed to predict as different countries have different factors such as
immunity of people, social structures, climatic conditions, R naught number,
per million infections etc. It is practically impossible to develop a general mathematical
model for all countries at once. I attempted to develop the model only for the Indian
scenario by introducing a factor for India in the differential equations.
6.
I used the
following three differential equations using the same variables as the S-I-R
model with variables such as Susceptible (S), Infected (I) and Recovered/died
(R), but used additional factor for Indian scenario for the factors discussed
above. The three equations are:
i)
dS/dt = -r*f*I*S
ii)
dI/dt = r*f*I*S-a*I
iii)
dR/dt = a*I
where,
r is the rate of spread of infection
a is the rate of recovery
f is the factor we have introduced for Indian
scenario
I =
number of Infected people
S =
number of susceptible
R =
recovered or dead patients
with
the initial data,
I =
I0
S =
S0
R =
0
dS/dt
+ dI/dt +dR/dt = 0
d/dt
(S+I+R) = 0
S +
I + R = S0 + I0
&
R0 = r/a* S0
7.
The above
equations were solved and the results are as follows:
Based on the results on my
analysis, it is evident that India will reach its peak by the end of July 2020
and at that time daily cases will be around 20k and the number of cases might
reach to 1 million (10 lakhs) and the case will then start to decrease
gradually and will subside till mid of October 2020. By October end the total
infections in India will be around 1.56 million (15.6 lakhs).
So friends our astrological predictions gave an
indication of the end of corona virus by September end and the technical data
analysis gave us the indication by mid of October 2020. It’s a difference of
about 15 days between both the analysis. So friends, keep your fingers crossed,
we will get a relief from corona virus between September end to mid October.
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