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Covid19 the real path to recovery

Covid19 cases have started showing a declining trend in the countries most affected by the virus. India, on the other hand has started showing a disturbing trend. The ‘42 day theory’ has held true for all the countries under study (except S.Korea, an outlier). Will it hold true for India or will India also be an outlier to this theory?

Since our last article, new Covid19 cases have started showing a declining trend in the countries most affected by the virus. India, on the other hand, has started showing a disturbing trend. The ‘42 day theory’ has held true for all the countries under study (except S.Korea, an outlier). Will it hold true for India or will India also be an outlier to this theory?

With declining new cases, the recovery rates and the mortality rates start to come in play. Countries that we studied are all exhibiting different recovery rates. What factors influence recovery rates? Do some countries have an advantage over the other?

India has also crossed 20K cases. Are the same states still contributing to Indian cases or are there new states with high growth? 

These are some of the questions we will try to answer in this article with the data that is available with us. And finally we will look at this week’s performance of states that we had identified as “Encouraging” states and “Worry” states in our last article.

But first we look at the CPM19 model Performance

CPM19 – On the Mark

The model has now caught the trend of all the 9 countries in study and for the last 3 days it has been predicting with almost 0% error.

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The significance of this is that now we are able to predict daily growth rate and extrapolate the growth rate for the next 10 days with little error. We used this to look at the next 7 days for the countries in study and it definitely looks like all of them are on the path to recovery, except India.

The 42 day Theory – Update

In our last article, we had postulated the 42 day theory. (Read it again) All but the USA, India and Sweden had gone below the 5% threshold as on last update.

USA – US was on its 45th day and we had predicted it will go below the 5% threshold on the 48th day. It went below the threshold on 48th day

Sweden – Sweden was on its 41st day and we had predicted it will go below the 5% threshold on the 42nd day. It went below the threshold on the 42nd day.

So, for the USA and Sweden, our predictions were right on the mark. Also, we had postulated that once any country goes below the 5% threshold on a 7 day rolling average, they really slow down. That theory also seems to be holding true. (See table)

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Except for Sweden which showed a slight increase on one day, all the other countries have shown a steady decline in their daily growth rates over the last week.

Unfortunately, the theory does not seem to be holding true for India. India is on its 38th day and with the current daily growth rate of India, we do not see India going below the 5% threshold till day 50. We are unable to predict beyond the 50th day for India as India has shown erratic trends recently.

On the 38th day, India stands 3rd behind USA and France in terms of seven day rolling average of the daily growth rate. With the trend seen in the graph India might breach the max mark.(See graph)

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The Recovery Rate

Different countries are showing different rates of recovery, even though they may be on the same life cycle of the virus. We looked at various parameters to see if we could identify the reason for difference in recovery rates. 

To begin with, let’s define recovery rate.

Recovery Rate  = Total number of recoveries / Total number of outcomes (death + recoveries)

We analyzed correlations between various parameters and recovery rate. We also checked the correlation of these parameters with the average daily number of cases reported after the 100th case. 

Parameters Studied

Test/Million – We looked at test/million as the first factor that may aid recovery and also may explain the number of cases. Surprisingly, test per million parameter had very poor correlation with number of cases and negligible correlation with recovery rate

Obesity Rate – We looked at the obesity rate of the country that is percent of people in the country who are obese. This had a strong positive correlation with the number of cases. This means that higher obesity rate resulted in a higher number of daily cases. There was also a moderate to weak negative correlation with recovery rate. Higher the obesity rate, lower was the recovery rate.

Overall Population Age – Average age of the population had no correlation with either recovery rate or cases reported

Percentage of population infected over 50 – Since age had no correlation, we looked at the percentage of infected out of total infected, that were over 50 years. This showed a strong negative correlation to recovery rate. If the percentage of infected over 50 years out of total infected was less, the recovery rate was higher

T Factor – We looked at the amount of tourists that visit the country. Our hypothesis here was that a popular tourist destination would be more susceptible to the infection. We indexed the tourists basis the total tourists that visited the country in 2019 and called it the T factor, This showed a strong positive correlation to the daily number of cases reported

CD Factor – We also looked at the Chinese Diaspora. We hypothesized that a higher Chinese Diaspora would mean more travel of infected population from China to that country, both business and tourist. This also has held true as there is a huge positive correlation between CD factor and daily number of cases reported

We take a look at the impact of these factors for each country. 

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India

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India is green or light green on all the parameters except test/mil. This clearly shows in the recovery rate. India needs to take care that its %infected over 50 does not increase. 

Also India’s total outcome percentage that is total cases that have had an outcome over total reported cases is very low (23%). So the recovery rate may fluctuate.

Italy

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Italy has a medium obesity rate and high T factor along with a moderate CD factor. Italy is also a favorite destination amongst the Chinese. This was one of the factors for initial infection. The fact that 71.2% of its infected cases are above 50, it has a low recovery rate also

USA

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USA has a high T and CD factor. Combined with the high obesity rate, it has the highest number of cases. Also the high obesity rate and 50% of infected people being above 50 has led to its low recovery rates. The outcome percentage for the USA is only 16%. We still await results in 84% of the cases

Iran

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We did not have the age of infected people in Iran so could not review the recovery rate versus age. Iran has low T and CD factors, however, we know from news report that the initial infections in Iran were from closer interactions with China, that rose exponentially because of a religious event in the area of the outbreak.

South Korea

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South Korea is the anomaly in our study with respect to Per Day Cases. South Korea has both a high tourist population from China and a huge Chinese Diaspora. But proper management of people inflow from China helped control the spread in initial days, though they did not ban travel from China. The latter increase in cases were attributed to patient 31 who was a super spreader. The source of that infection has never been identified but post that Korean administration did well to control the spread with aggressive contact tracing.

Spain

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Spain also suffered a lot in the initial days of the outbreak. However it was able to stabilize the growth rate. With a high obesity rate and a high T factor it has reported moderate per day cases and its recovery rate is also moderate. This is due to a high percentage of infections being reported in those above 50. Also 49% of the cases still are awaiting outcome, so the recovery rate may fluctuate.

Germany

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Germany has had the fifth highest cases worldwide. Most factors were poor or moderate. This has meant that Germany though has reported a higher number of cases has managed the infection well amongst the elderly. Infact, Germany was lucky as the infection came in the country through youngsters holidaying in Italy. Rigorous testing ensured that the asymptomatic cases were also identified so that they were not able unknowingly to spread the infection amongst the older population.

France

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Due to a high T factor and its popularity amongst the Chinese, France has seen high per day cases. It has a low recovery rate also as the majority of infections are amongst the greater than 50. Outcome% for France is also low at 38% so there could be an impact on the recovery rate.

Sweden

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Everything was in favor of Sweden, except the fact that it has a moderate obesity rate and it has let the infection spread amongst the elderly. It has the poorest recovery rate and the outcome% is also very low so that recovery rate may further worsen. Sweden’s main concern is the spread of infection in old age homes.

With only 34% of the cases that have had an outcome, this might be a little early to be looking at recovery rates but this gives us a direction of things to come. We will continue to track recovery rates across countries.

India – 20,000 and beyond.

India has become the 17th country to go beyond the 20K mark. It has crossed that mark almost after 3 months since the first reported case. Let’s take a look at the daily growth rate of new cases after India reported its 100th case.

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Although the trend is downward, it is not a rapid downward trend which is not resulting in a slow down for growth rate. India’s problem seems to be arising from the fact that new states keep emerging as growth drivers while not enough states seem to be slowing down. 

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We looked at which states contributed the most in the first 10K cases and then in the next 10K cases. While Maharashtra remained top in terms of contribution, Gujarat, MP and UP have taken over the top 5. These states are also growing at a much faster pace than even Maharashtra.

Story of the States

The Worry States

In our last article we had identified states that were a worry or were showing worrisome signs. The graph below shows their 7 day rolling average of daily growth rate since the last update

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The good news is that Delhi has definitely slowed down and the growth rate has now gone below 5%. Rajasthan is also showing some slowing trends as the growth rate has just gone below 10%. 

Gujarat, MP, UP and WB are the major cause of concern currently. All these states are trending higher than Maharashtra. Although, Maharashtra is slowing down its not slowing down fast enough. Since a high number of cases have already been reported in Maharashtra, a 10% growth rate also means around 500 to 600 cases daily in the current scenario.

Encouraging States

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The empty space above 10% tells the story here. All these states have now come below 10% and have stayed below 10% over the last 7 days. Haryana has joined Kerala in the below 5% club of 7 day average growth rate. Tamil Nadu also seems to be following suit. Karnataka and AP are two states that also need to slow down and get below 5%.

India’s Road To Recovery 

The road to recovery is highly dependent on the UP, MP, Rajasthan, Gujarat, Maharashtra and WB. 

These are also the most populated states of India. Except Rajasthan, they are showing a growth rate in excess of 10%.

Extreme poverty in these states means that they also have a huge migrant population.Residents of UP, WB and Rajasthan travel all around the country as migrant laborers which will put all the other states which have controlled the cases during the lockdown at risk. 

Daily wage workers form an important part of our agricultural and infrastructural economy, keeping them under lockdown for a longer period will also be detrimental to our economy. 

Unless these states show dramatic improvement May 3rd does not seem enough for India.

About the Author

Sanjeev Prakash is an Analytics and Marketing professional with more than 12 years of experience in Analytics, Data Management, Sales, Brand Management, Corporate Communications, Market Research and Customer Relationship Management. Sanjeev has an MBA from IMT Ghaziabad and a degree in economics.

In Collaboration with Parinay Pande

(Also published on LinkedIn https://www.linkedin.com/pulse/covid-19-real-path-recovery-sanjeev-prakash/?trackingId=4oI3Xc4ESvOb3m%2FU%2B2jOcw%3D%3D)

One reply on “Covid19 the real path to recovery”

I could not find prediction for 21st April in your last article. How you are calculating the error from your model?

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