Tag: USA

COVID-19 Predicting Infections for several Countries

USA	Italy	UK
France	Spain	Germany
Canada	Australia	China
South Korea	India	Methodology

USA

Day-1 of Exponential Growth Phase	07 Mar 2020 = Day-1
Model Factor (MF) – VARIES	0.3 to 0.6
Point of InfLection based on Real Data best fit ##	09 Apr 2020 = Day-34
Day-15	21 Mar 2020
DAY-16	22 Mar 2020
Day-23	29 Mar 2020
Model Validity with Real Data: Day-1 to Day-15	VALID for MF=0.30
Model Validity with Real Data: Day-16 to Day 23	VALID for MF=0.6
Infections at Day-30 (05 Apr 2020) – PREDICTED*	532,511
Infections on 05 Apr 2020 (Real Measured Data)*	337,072
Infections at Day-45 (20 April 2020) – PREDICTED#	8,566,413
Infections on 20 Apr 2020 (Real Measured Data)*	784,326

* & # Real Measured Data for COVID-19 infections in the US are lower than Predicted Data as per the model for Day-30, by 37%. This is because the model deviates from real world data after 29 Mar 2020. The tested cases are only a subset of the population. Therefore, to obtain the infections in the entire population, we can extrapolate the tested data to the whole population by a multiple.

## InfLection is at Day=40, which falls on 15 Apr 2020. As on this this day the rate of inFection growth has definitely slowed down.

Italy

Day-1 of Exponential Growth Phase	26 Feb 2020 = Day-1
Model Factor (MF)	1.00
Point of InfLection based on Real Data best fit ##	27 Mar 2020 = Day-31
Model Validity with Real Data from Day-1 to	18 Mar 2020 = Day-22
Infections at Day-30 (26 Mar 2020) – PREDICTED*	163,309
Infections on 26 Mar 2020 (Real Measured Data)*	80,589
Infections at Day-45 (4 April 2020) – PREDICTED#	2,627,126
Infections on 04 Apr 2020 (Real Measured Data)#	1,24,632
Asymptote based on Real Data – PREDICTED ###	08 Apr 2020 = Day-43

* & # Real Measured Data for COVID-19 infections in Italy are lower than Predicted Data as per the model for Day-30 and Day-45. This is because the model deviates from real world data after 18 Mar 2020. The tested cases are only a subset of the population. Therefore, to obtain the infections in the entire population, we can extrapolate the tested data to the whole population by a multiple.

## InfLection is at Day=31, which falls on 27 Mar 2020. As on this this day the rate of inFection growth has definitely slowed down. Based on this, other countries may also show a slow in growth at about 30 days, provided the steps taken there are similar to the protocols taken in Italy.

### Asymptote is the point where the rate of new InFections drops dramatically, in mathematical terms to zero. However, in the case of real world inFections, the numbers of new inFections would be small compared to the previous days. This is the point when the curve becomes flat.

Day-1 of Exponential Growth Phase	10 Mar 2020 = Day-1
Model Factor (MF)	0.70
Point of InfLection based on Real Data best fit ## (Day-37 = 15 Apr 2020 – PREDICTED)	15 Apr 2020
Day-17	26 Mar 2020
Model Validity with Real Data from Day-1 to Day-17	VALID
Infections at Day-30 (08 Apr 2020) – PREDICTED*	127,495
Infections on 08 Apr 2020 (Real Measured Data)*	60,733
Infections at Day-45 (23 April 2020) – PREDICTED#	2,050,983

* & # Real Measured Data for COVID-19 infections in the UK are lower than Predicted Data as per the model for Day-30, by 52%. This is because the model deviates from real world data after 26 Mar 2020. The tested cases are only a subset of the population. Therefore, to obtain the infections in the entire population, we can extrapolate the tested data to the whole population by a multiple.

## InfLection for UK is PREDICTED on 15 Apr 2020 (as per data available on 13 Apr 2020). That’s a total of 37 days from the start of exponential growth.

France

Day-1 of Exponential Growth Phase	05 Mar 2020 = Day-1
Model Factor (MF)	0.80
Point of InfLection based on Real Data best fit	07 Apr 2020 = Day-34
Day-21	25 Mar 2020
Model Validity with Real Data from Day-1 to Day-21	VALID
Infections at Day-30 (03 Apr 2020) – PREDICTED*	140,529
Infections on 03 Apr 2020 (Real Measured Data)*	64,338
Infections at Day-45 (18 April 2020) – PREDICTED#	2,260,661

* & # Real Measured Data for COVID-19 infections in France are lower than Predicted Data as per the model for Day-30, by 54%. This is because the model deviates from real world data after 25 Mar 2020. The tested cases are only a subset of the population. Therefore, to obtain the infections in the entire population, we can extrapolate the tested data to the whole population by a multiple.

## InfLection is at Day=34, which falls on 07 Apr 2020. As on this this day the rate of inFection growth has definitely slowed down.

Spain

Day-1 of Exponential Growth Phase	06 Mar 2020 = Day-1
Model Factor (MF)	2.5
Point of InfLection based on Real Data best fit ##	31 Mar 2020 = Day-26
Model Validity with Real Data from Day-1 to	26 Mar 2020 = Day-21
Infections at Day-30 (04 Apr 2020) – PREDICTED*	315,134
Infections on 04 Apr 2020 (Real Measured Data)*	126,168
Infections at Day-45 (19 April 2020) – PREDICTED#	5,069,498

* & # Real Measured Data for COVID-19 infections in Spain are lower than Predicted Data as per the model for Day-30, by 60%. This is because the model deviates from real world data after 26 Mar 2020. The tested cases are only a subset of the population. Therefore, to obtain the infections in the entire population, we can extrapolate the tested data to the whole population by a multiple.

## InfLection is at Day=26, which falls on 31 Mar 2020. As on this this day the rate of inFection growth has definitely slowed down.

Germany

Day-1 of Exponential Growth Phase	05 Mar 2020 = Day-1
Model Factor (MF)	0.9
Point of InfLection based on Real Data best fit ##	01 Apr 2020 = Day-28
Day-22	26 Mar 2020
Model Validity with Real Data from Day-1 to	26 Mar 2020 – Day-22
Infections at Day-30 (03 Apr 2020) – PREDICTED*	202,728
Infections on 03 Apr 2020 (Real Measured Data)*	91,159
Infections at Day-45 (19 April 2020) – PREDICTED#	3,261,248

* & # Real Measured Data for COVID-19 infections in Spain are lower than Predicted Data as per the model for Day-30, by 55%. This is because the model deviates from real world data after 26 Mar 2020. The tested cases are only a subset of the population. Therefore, to obtain the infections in the entire population, we can extrapolate the tested data to the whole population by a multiple.

## InfLection is at Day=28, which falls on 01 Apr 2020. As on this this day the rate of inFection growth has definitely slowed down.

Canada

Day-1 of Exponential Growth Phase	16 Mar 2020 = Day-1
Model Factor (MF)	1.25
Point of InfLection based on Real Data best fit ##	08 Apr 2020 = Day-24
Day-11	26 Mar 2020
Model Validity with Real Data from Day-1 to Day-11	VALID
Infections at Day-30 (14 Apr 2020) – PREDICTED*	126,126
Infections on 14 Apr 2020 (Real Measured Data)*	NOT YET THERE!
Infections at Day-45 (29 April 2020) – PREDICTED#	2,028,971

* & # Real Measured Data for COVID-19 infections in Canada will be lower than Predicted Data as per the model for Day-30. This is because the model deviates from real world data after 26 Mar 2020. The tested cases are only a subset of the population. Therefore, to obtain the infections in the entire population, we can extrapolate the tested data to the whole population by a multiple.

## InfLection is at Day=24, which falls on 08 Apr 2020. As on this this day the rate of inFection growth has definitely slowed down. Canada has carried out 8,732 tests/million.

Australia

Day-1 of Exponential Growth Phase	16 Mar 2020 = Day-1
Model Factor (MF)	1.40
Point of InfLection based on Real Data best fit ##	28 Mar 2020 = Day-13
Model Validity with Real Data from Day-1 to	26 Mar 2020 = Day-11
Infections at Day-30 (14 Apr 2020) – PREDICTED*	95,166
Infections on 14 Apr 2020 (Real Measured Data)*	NOT YET THERE!
Infections at Day-45 (29 April 2020) – PREDICTED#	1,530,911

* & # Real Measured Data for COVID-19 infections in Australia will be lower than Predicted Data as per the model for Day-30. This is because the model deviates from real world data after 26 Mar 2020. The tested cases are only a subset of the population. Therefore, to obtain the infections in the entire population, we can extrapolate the tested data to the whole population by a multiple.

## InfLection is at Day=13, which falls on 28 Mar 2020. As on this this day the rate of inFection growth has definitely slowed down. The infLection point for Australia is much faster than for Italy, Spain and Germany.

China

Day-1 of Exponential Growth Phase	22 Jan 2020 = Day-1
Model Factor (MF)	0.1
Point of InfLection based on Real Data best fit ##	12 Feb 2020 = Day-22
Model Validity with Real Data from Day-1 to	10 Feb 2020 = Day-20
Infections at Day-30 (20 Feb 2020) – PREDICTED*	386,705
Infections on 20 Feb 2020 (Real Measured Data)*	75,077
Infections at Day-45 (06 Mar 2020) – PREDICTED#	6,220,851
Infections on 06 Mar 2020 (Real Measured Data)#	80,690
Start of Asymptote (Day-46) – Real Data	07 Mar 2020
Current Asymptote Value – Real Data as on 26 Mar 2020 (Day-65)	81,782

* The Day-30 Predicted Data is higher that the Real Measured Data by a factor of 5.

# Day-45 Predicted Data is orders of magnitude than the Real Measured data.

## InfLection is at Day=22, which falls on 02 Feb 2020. As on this this day the rate of inFection growth has definitely slowed down. The infLection point for China is in the same ball park for Italy (Day-30), Spain (Day-27), and Germany (Day-28).

Global estimates of infections as on 26 Mar 2020 was 566,269.

China’s reported infection data for 26 Mar 2020 is 81,792. Out of this, Hubei Province (of which Wuhan is the Capital) has 67,801 reported infections – which is 83% of China’s number. Though infections had spread to 31 other Provinces, they account for only 17% of infections. China’s numbers begin to asymptote around 17 Feb 2020 (Day-27) at reported 72,434 cumulative infections.

China must have been brilliant at curtailing the spread that began the exponential growth phase on 22 Jan 2020, had a point of inflection on 09 Feb 2020 (Day-19) and started the asymptote on 17 Feb 2020 (Day-27). Additionally they supposedly curtailed the spread in other Provinces, but left gaps in the control to spread COVID-19 to 199 Countries and Territories. This very difficult to accept!

Another way to look at it is that China was done with the worst in 27 Days. If you see the country models above, none of the major countries are anywhere near the begin of an asymptote as on 26 Mar 2020 – USA (Day-20), Italy (Day-24), UK (Day-17), France (Day-22), Spain (Day-21), Germany (Day-22), Canada (Day-11), Australia (Day-11).

Finally, China has a population of 1400 million and the above mentioned countries have populations in the range 35 to 65 million. So, we would expect China to have been much worse.

Therfore I will leave the reader to conclude whether we should trust the Prediction or the Real Measured Data for China. My personal opinion is that China’s reported data is a suspect.

South Korea

Day-1 of Exponential Growth Phase	22 Feb 2020 = Day-1
Model Factor (MF)	1.0
Point of InfLection based on Real Data best fit ##	15 Mar 2020 = Day-23
Model Validity with Real Data from Day-1 to	05 Mar 2020 = Day-13
Infections at Day-30 (22 Mar 2020) – PREDICTED*	186,496
Infections on 22 Mar 2020 (Real Measured Data)*	8,961
Infections at Day-45 (06 Apr 2020) – PREDICTED#	3,485,646
Infections on 06 Apr 2020 (Real Measured Data)#	NOT YET THERE
Current Asymptote Value – Real Data as on 26 Mar 2020 (Day-34)	9,137

* The Day-30 Predicted Data and Real Measured Data vary by a huge factor. Since the model is anyway NOT valid beyond Day-13, Predicted Data should be discarded.

# The Predicted Data for Day-45 is a very big number, compared to the Real Measured Data of 9,137 as on 26 Mar 2020. Again, since the model is anyway NOT valid beyond Day-13, Predicted Data should be discarded.

## InfLection is at Day=23, which falls on 15 Mar 2020. As on this this day the rate of inFection growth has definitely slowed down. The infLection point for South Korea is in the same ball park for Italy (Day-30), Spain (Day-27), Germany (Day-28) and China (Day-22).

However, in the case of South Korea, we should probably accept that the Inflection at Day-23 (15 Mar 2020) is real for the very reason that it succeeded in stoping COVID-19 in its tracks. Rather than repeat what is already out there, please see the following article. Further, post Day-23, the curve has begun to asymptote, though not completely reaching a level of zero slope.

South Korea is the only country so far, where the virus was curtailed by targeted lockdowns through the use of technology and the use of best practices via TRACING, TESTING and QUARANTINING. It is also the only country that achieved this without total lockdown.

South Korea is a democratic country. It has a population of about 51 million. It has a testing ratio of 7,502 Tests/Million, which is higher than that for even Italy (6,533 Tests/Million). This data is getting higher and higher each day. Its data and publication methods have been transparent and at par with international norms for reporting.

It is quite evident that South Korea had reached a true point of inflection (second derivate equal to zero) and thereby curtailed COVID-19.

India

Day-1 of Exponential Growth Phase	22 Mar 2020 = Day-1
Model Factor (MF)	TBD
Point of InFlection based on Real Data best fit	NOT YET
Day-X : End Date for Model Validity	TBD
Model Validity with Real Data from Day-1 to Day-X	NEED MORE DATA
Infections at Day-30 (20 Apr 2020) – PREDICTED*	TBD
Infections on 20 Apr 2020 (Real Measured Data)*	NOT YET THERE!
Infections at Day-45 (05 May 2020) – PREDICTED#	TBD

* # The data for India from 22 Mar 2020 to 05 Apr 2020 is spurious. It is far removed from the truth. It is because of insufficient testing in the country. This gives an artificially low number for inFections. The graph, with MF=0.04, is the Italian trend for inFections. The India data is 56% lower than Italy, for the same time period of exponential growth. But, Indian population is 22.56 times the Italian population.

In my previous article I had estimated that 1.44 million people will be infected with COVID-19 by 19 April 2020, but that was based on simple scaling of the Italian Raw Data for population difference between India and Italy.

The quantum of actual testing in India is very low and this may limit the quality of a mathematical model, since the factor MF will not be well known. India is currently doing only 66 Tests/Million people, as on 05 Apr 2020. It is totally inadequate to get the ground level reality. In comparison the testing in other other countries is way higher – 10,896 (Italy); 8,920 (South Korea); 5,421 (US) and 2,895 (UK). This is the reason why it was possible for me to establish a mathematical exponential predictive equation for these countries, but not for India.

As such, until the actual testing data improves in India, it may be difficult to predict the infections for the next 15 to 30 days. The only alternative is to predict the number as reported in my previous article.

Methodology – Model Equation and Real World Infection Data

The model has been developed based on the Italian trend for infection spread of COVID-19 (Coronavirus 2019). In a previous article, I had established the equation for the exponential growth model for Italy. This equation was modified for other countries based on a scaling Model Factor (MF) and their population, via a Population Ratio (PR) defined as the “population of a country” divided by “population of Italy”. I’ve used the population data from https://worldpopulationreview.com. In a nutshell the equation takes the form:

Y(t) = MF * PR * 631.06 * exp (0.1852*t)

Y(t) is the “Cumulative Number of Infections” on day “t”. The model equation works in the exponential growth phase for the spread of the virus infection. The start of the exponential growth phase is taken when the cumulative number of infections has reached about 400, based on actual published test raw data, since it works well with the equation for many countries. When this happens, I’ve designated it as Day-1 of the exponential growth phase and t is set to 1 (t=1). For succeeding days, “t” is incremented by one.

I’m comparing the predicted model with the real world data set publicly available from John Hopkins University & Medicine (JHU), Center for Systems Science and Engineering (CSSE). In my view, this is the most reliable data available in a single place for any country.

COVID-19 Monitoring Infection Spread in India – Model versus Actual Public Data

It is my intent to update the above graph for India from 22 Mar 2020 to 22 Apr 2020. The graph above will be replaced on a daily basis. In the above graph t=1 is Day-1 which is set at 22 Mar 2020. Like wise t=2 and so on will increment from the set point date of 22 Mar 2020. For those who don’t like math, they just have to look at the graph and see the trends.

The data for India from 22 Mar 2020 to 05 Apr 2020 is spurious. It is far removed from the truth. It is because of insufficient testing in the country. This gives an artificially low number for inFections. The graph, with MF=0.04, is the Italian trend for inFections. The India data is 56% lower than Italy, for the same time period of exponential growth. But, the Indian population is 22.56 times that of the Italian population.

The model has close likeness for MF = 0.02, which means that it is following a curve trend that has values lower than that for Italy. Further, we should be expecting an MF = 1, since India should scale to Italy based on the Population Ratio (PR). So, an MF = 0.02 is unreliable and should not be used as a possible model.

The real time data for the exponential growth phase for infections had started on 22 Mar 2020. However, as seen from the graph, the real data does not fit the model, for any value of practical MF. As such, it is very likely that the Real Data published for India by JHU is not of practical use and also does not represent a good sample of the population.

I’m pulling the real data for India from the data set publicly available from John Hopkins University & Medicine (JHU), Center for Systems Science and Engineering (CSSE). It’s not clear where they are getting the day-to-day data from for India, but it is surely from the list of data sources listed on their data archival portal. The Indian Government has a website page (https://www.mygov.in/covid-19 page) for COVID-19, but there is no “day-to-day” data history.

A key conclusion that can be made as on 05 Apr 2020 is that the reported data for inFections in India is totally wrong, in its entirety.

In my previous article, I had established the equation for the exponential growth model for Italy. The population ratio (PR) is 22.56673 for India. Multiplying the exponential growth model with PR should take into account the population differences. I’ve further multiplied it with a Model Factor (MF), which is a number greater than 0, but more likely about 1 for India. This factor fine tunes the scaling and at some value it will have a good match to the real world infection data. When MF is 1/PR, the curve is an identical match to the exponential best fit for Italy.

As such, for India, the modified exponential equation is:

y(t) = MF * PR * 631.06 * exp (0.1852*t)

i.e.

y(t) = MF * 22.56673 * 631.06 * exp (0.1852*t)

To predict the Infection Cases in India, select an MF between 0 and 1, plug in a value of “t”, where “t” is the day count starting from 22 Mar 2020 (t=1) for India. The computed value of y(t) is the predicted Infection Cases on day “t”.

Poverty in India – 600 Million!

Poor

I recently saw the documentary 13th and it was stunning. The revelations were startling. President Abraham Lincoln abolished slavery in the United States of America on January 31, 1865, through the 13th Amendment. It freed about 4 million Black slaves. However, successive US Presidents successfully maneuvered Government Polices and continued to keep a large percentage of Blacks in abject slavery. The US prison population was 327,000 in 1970 and this jumped to 2,220,300 (more than 2 million!) in 2013. Blacks make up 13% of the US population, but account for 40% of the prison population, which is about 900,000, say close to a million. It is shocking, but true.

How is this related to Poverty in India?

There is a connection and it is political. Poverty has been a political cancer since 1947. Every elected Government, since Independence, has made polices that have perpetuated poverty – some intentional and some not.

There are many definitions for poverty and it can be confusing for anyone. I’ll trust the World Bank’s definition of international poverty line, which was revised in 2015 to US $1.9/day (Rs. 122/day) per person, based on the concept of PPP (purchasing power parity).

Take a look at how the Government of India defines poverty. In June 2014 it was defined as a person earning Rs. 32/day in rural areas and Rs. 47/day in urban areas. A Reserve Bank of India (RBI) report in 2012 computes that 270 million people (22% of population) are below the poverty line of Rs. 33/day. This is consistent with the World Bank’s published data.

At a US $1 to Rs. 64 exchange rate, Rs. 33/day is about $0.52/day per person. The accepted 2015 poverty line of $1.9/day adjusted to 2012 figures is about $1.74/day. How do we reconcile a definition of $0.52/day by the Indian Government to $1.74/day by the World Bank, which is a difference of 235%? It’s apparent that the Indian Government is deliberately under-reporting the % of Indian population below the poverty line.

If we assume linearity in income and % population data, at $1.74/day (Rs. 111/day) the % of population is 74%. At low income levels, linearity is a reasonable assumption.

The truth is somewhere in-between. The % of Indian population below the poverty line is between 22% (Rs. 33/day) and 74% (Rs. 111/day). I’ll mid-point it out at about 48%, for 2012. This translates to about 600 million people in India below the poverty line!

If politicians say that the % of population below the poverty line is less that 22% in 2018, then we know it is not true! It just can’t come down from 48% to 22% in 6 years, which would be a reduction of 54%! There is no way they can justify it, since the Ministry of Statistics and Program Implementation (MOSPI) of the Indian Government does not have any data till date to support a counter claim.

Why is the Indian Government pegging the poverty line to Rs. 33/day per person? It’s simple – because 22% poverty level sounds politically great. If it were taken at Rs.122/day (US $1.9/day), as per the international norm, the % levels would be much higher and this does not sound great to any political party.

I want to further substantiate that the present Indian Government’s definition for poverty of Rs. 33/day is absurd. I spoke to a few of the people I work with, who have come from villages to the city to seek employment. Well, they are from South India and they love rice and sambar, a type of lentil based spicy soup, in their daily diet. They tell me that in the villages, the consumption per person would be about 400g/day of rice and about 200 grams/day of lentil in the form of sambar. Rice is about Rs.50/Kg and lentils (dal) about Rs. 90/Kg. Add to this about 250 grams/day of vegetables at Rs. 25/Kg. This works out to Rs. 44.25/day just on food. What about other things. Let’s take just rent and electricity. In villages, rent may be about Rs.900/month (Rs. 30/day) and electricity Rs.15/day. The basics works out to be about Rs. 89.25/day. One also needs clothes and health care. So, the World Bank estimate of Rs. 122/day seems more realistic than Rs. 33/day of the Indian Government. I don’t have any doubts about it, do you?

The folks I spoke to would be proud to pay for their daily sustenance without doles from the Government. They just want jobs that pay well. They don’t want a hand out of subsidized food supply (Government Ration Schemes or Coupons). Their pride to earn and live well is more valuable to them.

But, poverty is real. The Government is coming up with all sorts of schemes to dole out goodies to the poor and keep them poor. For example, in Tamil Nadu there is the Amma Scheme and in Karnataka there are Indira Canteens, and so on in other states. Such schemes just keep their hunger at bay, but provides no tools for them to progress out of poverty.

The poverty data has always been inconsistently reported and explained since 1947. In the 1950’s about 65% (215 million) of the population was below the poverty line, defined as Rs. 0.6/day per person. In the 1960’s it was 70% (289 million) below Rs. 0.6/day per person. In the 1970-1980’s the figure is reported at 50% and the poverty line was defined at Rs. 1.63/day per person. In the 1990’s poverty was reported at 77%, based on Rs.20/day per person. In the 2000’s, the estimate in 2012 pegs the poverty at 22% based on Rs.33/day per person. Look at the pattern from 1947 – 65%, 70%, 50%, 77% and then 22%. Somebody did some magic in 2012 – political mathematical chicanery!?

I think I have made the case for a poverty estimate of about 48% to 50% at the present time (2018), affecting 600 million people. Of course, I am not an economist or a political analyst or a statistician. So, if anyone disagrees and provides data, I’ll be happy to correct any errors in my analysis.

To pull people of India out of poverty, I think that the Government should re-invest its free doles into more permanent things. In layman’s terms, one way that I see is to create more jobs. For creating more jobs, the new generation must have better skills through education. For jobs that pay better, Companies should be incentivized to do so. If self employed, let the Government re-route the existing free doles to enable people to invest them in working assets, so that it can generate revenue for them.

For salaried and self employed people to earn more, the present 2018 Government of India, should be humble and make practical policies. Their demonetization and GST initiatives have a basic premise – businesses and people are corrupt.

The demonetization policy was a failure. RBI has stated that 99% (Rs. 15.28 trillion or US $239 billion) of the pre-demonetization money (Rs. 15.44 trillion or US $241 billion) in circulation came back to the banks!

I’m a supporter of GST, but the implementation is pathetic. The IT technology behind it is ancient and makes life hell for filers. It’s a system that should grant credit for all purchases, but it does not. Further, even though the businesses have legal invoices and receipts for purchases, they cannot claim credit for all of it, since the GST body wants the seller to upload the sale to the GST site. Why – since the Government thinks we will cheat!

Finally, the officers in the Central Government are still corrupt. Businesses still pay a bribe to get things done in the departments. This nasty practice must stop. So, with honest and practical reforms in GST and the way the Government works, businesses can no doubt create more jobs and also better paying ones. Self employed citizens will thrive. It’s the only way out of poverty, other than being socialistic. India is democratic and it has too large a population for socialism to work. The private sector can do what the Government just cannot.

I think that a little honesty from the Government is needed. It must think real hard and solve the poverty problem. It should stop making policies that keep the poor in poverty. The equation of poverty to votes must end now and forever.