overview of covid-19...delhi 10.46% maharashtra 29% tamil nadu 6.7% overview of covid-19 an india...

OVERVIEW OF COVID-19AN INDIA PERSPECTIVE

17th AUGUST, 2020

OVERVIEW OF COVID-19

AN INDIA PERSPECTIVE

Wave 1417th Aug 2020


17th AUGUST, 2020

EXECUTIVE SUMMARY

This document presents a study performed by Protiviti and Times Network as of 17th August 2020, toestimate the extent to which COVID19 is expected to influence India as a country using aggregatedstatistical measures computed based on 3 types of forecasting models:

% Based Models, Time Series model & SEIR Model.

We analyzed India as a whole, top 10 Indian states and 26 cities/hot-spots along with the entire countryfor a period of close to 50 days to project the evolution of the COVID19 pandemic using the ‘Number ofdaily new cases’ along with the total ‘active’ patients count.

The projected evolution of infected cases initially assumed an inherited effect of countrywide lockdownapplied from 24 March 2020 for 45 days or so.

The projections provided in the study are primarily based on time series modeling and therefore, weunderstand that this complex and dynamic nature of the influence of COVID19 shall evolve on a daily basisand is a continuous work in progress.

The models that are used suggest that the peak ranges from around 780,354 cases on an optimisticside to around 938,306 on a higher side and expected to happen around early September.

Additionally, based on SEIR model, primarily designed to analyze the time based influence of suchpandemics both in smaller and larger cohorts, on an average run down suggests that the closing dateat an India level may fall somewhere towards the end week of November, 2020.

Some states have already started to peak out and some might come out of it during later part ofAugust to early September while some highly infected ones may take a month longer.

The lockdown plays a key role on the exit. In case of a longer period of lockdown, we can expect thecurve to be flattened but can expect a later exit date (i.e. a flat curve will stretch the pandemic). Forthe lockdown to eradicate the pandemic, a robust lockdown and containment is required for asubstantial period of time.

Also, when a lockdown is lifted we expect the cases to rise if proper measures are not taken. Themode in which lockdown is lifted will impact the outcome / # of cases.


17th AUGUST, 2020

SETTING THE CONTEXT

This document presents a study performed by Protiviti and Times Network as of 17th August2020, to estimate the extent to which COVID19 is expected to influence India as a country usingaggregated statistical measures computed based on 3 types of forecasting models: % BasedModels, Time Series model & SEIR Model.

We analyzed top 10 Indian states and 26 cities/districs along with the entire country for aperiod of close to 50 days to project the evolution of the COVID19 pandemic using the ‘Numberof daily new cases’ along with the total ‘active’ patients count.

The projected evolution of infected cases assumes an inherited effect of countrywide lockdownapplied from 24 March 2020 and the numbers proposed for the future also consider that thisperiod of lockdown continues for the next 45 days or so.

The projections provided in the study are primarily based on time series modeling andtherefore, we understand that this complex and dynamic nature of the influence of COVID19shall evolve on a daily basis and is a continuous work in progress.


17th AUGUST, 2020

SETTING THE CONTEXT

Point to be noted:

The outputs of the modelling are not to be consumed directly for publication by any other source.

The models presented in the study are based on the fundamental assumptions of time series modeling well stated in the published academic sources.

The models which are developed using existing standard epidemiological modeling techniques like SIR and SEIR are with the limitations of limited data, dynamic environmental factors and external government based interventions.

The proposed SEIR model does not take into account the asymptomatic transmission explicitly, however, it cannot also be fully discarded in the actual scenario.

Additionally, we have not considered stratified cohorts of population for studying this transmission based COVID19 pandemic. A later version of the model might include age based or other stratifying features that may account for differing demographics in India.

We have taken utmost care while collecting, preparing and processing the required infected count data, however, the overall quality of data will have a material impact on the quality and reliability of outputs.

Although the modeling period included both the lockdown and pre-lockdown phases in India, but there is no specific designed parameter available in the proposed model that may help analyze Government intervention in retarding the growth of this pandemic in India. Therefore, an effect of an external policy level change might only reflect in our modelling output after a lag of 3-4 days.

This work is completely a scenario based modeling framework wherein Protiviti along with the Times Network group have analyzed this pandemic situation and have come up with a robust statistical way to model it under a number of key assumptions. Therefore, the outputs of the study are not an indication of the future but rather probable situations for consideration and further deliberation.

This study does not take into account COVID-19 testing data in India and all the states, timing of testing, external factors like immunity, BCG vaccination, and international travel history data for a specific time period, specific cluster density, demographics etc.

This predictive modeling considers the learning of progression rate from other countries like US, Italy, China, Malaysia, South Korea and incorporate in the models to create scenarios with certain assumptions.

The study depends on a number of assumptions which may vary at a granular level.

Lastly, the modeling here is regulated assuming that the cases listed on reliable government sourced websites are an average over time. We completely understand the public health care system in India and can reasonably assume that the number of cases considered in our study are on a lower side as many cases are not getting reported and the testing rate of India is towards the lower side when compared globally.


17th AUGUST, 2020

INDIA’s FIGHT AGAINST COVID-19

India's response to COVID-19 has been pre-emptive, pro-active and graded with high-level political commitment and a ‘whole government’ approach to respond to the COVID-19 pandemic. Ministry of Health and Family Welfare is working to strengthen surveillance, build capacity of health system and optimize ‘window of opportunity’ created by mandatory physical distancing in India.

2,766,511Total Cases on 17th

Aug 2020, 3 month ago, total active cases

were at 26

1.2 to 1.6R0 –reproduction rate

of the virus, with most estimates

averaging at 1.36

16 WeeksOf mandatory social

Distancing, has helped contain virus within

404 districts

30,041,400Tests completed, this has increased by 8.4 times in 1

month

Major Affected States

The most affected states / union territories are Maharashtra, Tamil Nadu and Delhi. Out of 736 districts, 411 districts have reported confirmed numbers. The silver lining is that the poorest of districts have the least number of cases. Additionally, many of the remaining 325 districts, are also the least capable of handling an exponential increase in cases. Fewer cases also mean, quarantine and track & trace procedures are easier to apply, and form an effective first line of defense.

However, it is too early, and it is paramount for these states & districts to continue social distancing until appropriate improvements in medical care, and access to medical facility has improved in these districts.

<=3% of total

>3% - 6% of total

>6% - 15% of total

>15% - 20% of total

Delhi10.46%

Maharashtra29%

Tamil Nadu6.7%


17th AUGUST, 2020

GLOBAL SCENARIO OF COVID-19 EPIDEMIC

The World Health Organization (WHO) declared the 2019–20 coronavirus outbreak a Public Health Emergency of International Concern (PHEIC) on 30 January 2020, and since then, 21,845,024* new cases have been registered, of which approx. 93% recovered, and 7% were fatal. A total of 215 countries have since reported an outbreak in a period of 4 months.

* - as of 16th August 2020, 11.59 PM GMT. This is an ever evolving situation, and is also growing exponentially.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%ItalyUSASpainIranSKIndia

MUCH OF THE BATTLE IS AHEAD OF US!

The total number of cases confirmed is a function of total tests performed, and seems exaggerated for countries with smaller infected case count. While this is the case for India, the worrying trend line for rate of change of cases in India is indicating an upward linear trend , currently at around 21%. Just like countries battling Covid-19 at least a month prior to India, early indicators depict a movement of this rate of change up to 30% - 40% range before it will start to drop by around 3rd or 4th week of April.

Nevertheless, this indicates one thing, as countries improve the rate of testing, and turnaround time for results from each test, the rate of change increases proportionally, indicating a still persistent exponential growth rate across the world, and an greater exponent driving the Indian scenario.

INDIA11.3%

% Change of Total Cases over the last 1 month

WO

RL

DIN

DIA

21,845,024 772,900 93.1% 9.5%

2,766,511 53,026 50% 3%

Total Cases Total DeathsRecovered / Total

cases with outcomeDeath / Total cases

with outcome


17th AUGUST, 2020

COUNTRIES NEARING THEIR PEAK

Understanding if a country is nearing its peak of cases is difficult to estimate, especially while the pandemic is unraveling its full potential. But, fundamentally, the total number of log-log chart of no. of tests per million vs. no. of cases per million, provides a clear picture of those countries which are starting to turn the curve.

South Korea is a success story, but from the graph it is clear that Italy has turned the curve, and will start recording fewer cases going forward. It is also worth noting that when the number of tests reach around 10,000 individuals per million, South Korea was able to successfully curb the spread. This was due to quick action, and effective social distancing, and quarantine policies.

India could take this road if the current containment policies are effective and peak by the time we reach 10,000 individuals tested per million. Without proper containment policy, or delayed implementation of the same, will result in a steeper rise, and subsequently steep fall in cases.

1

10

100

1,000

1 10 100 1,000 10,000 100,000 1,000,000

Num

ber

of C

ase

s pe

r M

illio

n(l

og s

cale

)

Number of Individuals tested per Million(log scale)

UK

USSpain

Italy

South Korea

INDIA

FLAT CURVE

THIN CURVE

This scenario analysis will help us assess the model that we can use to estimate the current state of the pandemic, especially in relation to India.

The goal of the country is to hit a flat curve that will sufficiently spread the cases encountered to meet the medical supply constraints. Our best case scenario therefore is to aim for the flat curve.


17th AUGUST, 2020

WHERE ARE WE & WHAT TO EXPECT

What will be the impact of the novel coronavirus (COVID-19) in India? Answering this question requires an accurate forecast of the spread of confirmed cases as well as analysis of the number of deaths and recoveries. Forecasting, however, requires ample historical data, often times, the correct data useful in forecasting is not available. At the same time, no prediction is certain as the future rarely repeats itself in the same way as the past. The below graph is an aggregation of 3 types of forecasting model: % Based Models, Time Series model & SEIR Model.

SEIR Model% Based Model

The model assumes that the Indian COVID-19 situation closely approximates one of the following countries:1) Approximation of Italy

(Italy)2) Approximation of US (US)

Time Series Model

The model assumes that past events and observations predict the future situation accurately.1) Hybrid (China & South

Korea)2) Poly Regression (2nd

Degree)3) Poly Regression (3rd

Degree)

A mathematical prediction model that divides the population into Susceptible population (S), Exposed Population (E), Infected (I) and Recovered (R) population. It predicts future values by assuming or predicting values for coefficients for the mathematical equations.

For each of these model categories, multiple models were built to simulate the situation in progress in India. These Models are

100

1,000

10,000

100,000

1,000,000

20/03 03/04 17/04 01/05 15/05 29/05 12/06 26/06 10/07 24/07 07/08 21/08 04/09 18/09 02/10 16/10 30/10

Total Number of Active Forecasted Cases

Best Case Worst Case Most Likely

10^2

10^3

10^4

10^5

10^6


17th AUGUST, 2020

METHODOLOGY

Percentage Based Model Apply the growth progression rate of the infected cases for USA and Italy on India data and project the

cases using the same trends over time to get the forecasted figures and no mathematical/statistical model has been applied

Considering the infected cases as a function of total population and use the percentage of Hubei to project the trend in India based on early data points available for India

Hybrid Model Segregate the data on two major segments – before lockdown and after lockdown and 2 distinct

models have been built for two segments considering the trends only and no other external factors have been taken into account

Within lockdown there are 3 stages- Progression rate, Flattening rate and Decay Rate : 3 different time series models has been fit separately based on the learning from China, Malaysia and South Korea

All the coefficients are based on Ordinary Least Square methodology and adjusted R-Square is the primary metric considered for accuracy; R-Square is more than 90% for all the models

Polynomial Regression Models To overcome the problem of Bias-Variance tradeoff while selecting an appropriate and effective

statistical model, we made use of Polynomial Regression models to model the COVID19 infected Indians data.

Polynomial Regression model assumes that the relationship between the dependent (Daily Count of Infected Patients) and independent variable (Time - Days) is curvilinear (specifically Polynomial)

The coefficients of the proposed models are estimated using Ordinary Least Square (OLS) method while the model accuracy and performance is assessed using adjusted R-square values along with observing the Mean Squared Error (MSE) values

Polynomial Regression of degree 2 and 3 provided for relatively a better fit along with an average forecasting accuracy of over 94% & 98% respectively

Susceptible Exposed Infected Recovered (SEIR) Model

The basic Reproduction Number (R0) is the most common measure of estimating the strength of an epidemic. In simple words, R0 is the secondary cases generated by one primary case in a susceptible population.

In order to estimate the extent of the Covid19 pandemic in India, we made use of the SEIR model proposed by Betten Court and Ribeiro1 & Gani et al.2,

Estimation of basic reproduction number (R0) is performed through initial intrinsic growth rate method using standard deterministic SEIR model.

This standard deterministic SEIR epidemic model classifies each individual into susceptible S(t), latent/exposed E(t), infective I(t) and recovered R(t) individual.

Our estimate of reproduction number for India is 1.55 with 95% CI [1.52 , 1.61 ], whereas for different states the effective reproduction number varies in between 1.16 to 1.52

Model assumes that the infectious period, latent period, transmission probability and infection rate per person per unit time is dynamic and varies based on individuals and their respective location

The pandemic spread curve presents a transmission rate/infection rate and its evolution over a period of time

Based on the nuances and the complexity associated with limited pandemic data, we adopted a methodology of forecasting using aggregated measures computed using 3 types of forecasting models: % Based Models, Time Series model & SEIR Model.

1. Bettencourt, L. M. A. and Ribeiro, R. M., Real time Bayesian estimation of the epidemic potential of emerging infectious diseases. PLoS One, 2008, 3(5), e2185.2. S. R. Gani, Sk. Taslim Ali and A. S. Kadi, The transmission dynamics of pandemic influenza A/H1N1 2009–2010 in India, CURRENT SCIENCE, 101(8) 25 Oct. 2011


17th AUGUST, 2020

India is undermanned, underprepared, and is facing the crisis, with an uphill battle of improving the health infrastructure and increasing capacity to meet the demands of COVID-19 patients. Worryingly, the total ICU beds with working Ventilators, which is crucial for severely affected COVID-19 patients, is in the range of few thousands. Total susceptible cases requiring intensive care from a population standpoint is approximately 322 Million Indian citizens.

At its probable worst, COVID-19 might reach a peak of 7.87 lakh patients. We expect that in the best case scenario, India will reach around 7.80 lakh cases, around 7.87 lakh cases in a most likely scenario and 9.38 lakh cases in the worst case scenario. In the most likely scenario we expect that the India will be 95% free around 1st week of September.

The problem of community spread is greatly concerning, and the country is scrambling to add new beds, increase capacity of intensive care units, to mitigate the situation of 100,000 patients not having access to life saving facilities. Additionally complicated is the scenario where the distribution of beds are not in proportion to the affected. The cites are disproportionately affected, and therefore will be the first to run out of beds, even though they are the ones with better facilities. The % of cases that are fatal in rural regions may be higher due to this reason.

The government is undertaking medical colleges and railway coaches into isolation wards, stadiums into quarantine facilities, and readying part of hospitals under defense, central police forces and railways for Covid-19 patients.

PROPENSITY TO MANAGE CASES

1,872,971Total non ICU Beds

available across India

164,886ICU beds, or 6.4% of total hospital beds

TOTAL BEDS

40% - 45%Average % of beds

currently occupied, and is un-usable.

126Will have ICU Beds for

every million susceptible population, (i.e. all of the

1Million may need intensive care)

20/03 03/04 17/04 01/05 15/05 29/05 12/06 26/06 10/07 24/07 07/08 21/08 04/09 18/09

Total Number of Active Forecasted Cases

Best Case Worst Case Most Likely

10^2

10^3

10^4

10^5

10^6


17th AUGUST, 2020

11

STATEWISE ANALYSIS

This section represents how some of the states has been doing in the last one month. We can see from the graphs below that some of the states have started showing of decline while some of the states show growth.

The first graphs talks about the growth of the number of active cases and 7 day moving average.

The second graph talks about the Doubling Rate or the number of days it took to double.

If we look at these information we can clearly see that there are 3 buckets of states

1. Where the growth has gone done considerably

2. Where the growth has stabilized and

3. Where there are still very high rate of growth.

We can categorize Tamil Nadu

(TN), Telangana (TL), Delhi (DL)

and Haryana (HR) in the first

bucket.

Maharashtra (MH), Kerala (KL),

Uttar Pradesh (UP), Karnataka

(KA), Madhya Pradesh (MP),

Rajasthan (RJ), West Bengal (WB)

and Gujarat (GJ) falls under the

second bucket with a 7 day moving

average ranging between 2%- 6%

and a Doubling Rate of 8-15 Days.

Finally, there are the high growth

states Andhra Pradesh (AP) and

Odisha (OD) where the growth

rate is in 7% - 10% and the

Doubling rate hovering around 6/7

days.

These states are further analyzed in details in the following sections

0

5

10

15

20

25

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40

1K+ 2K+ 4K+ 8K+ 16K+ 32K+ 64K+ 128k+ 256K+ 512K+

No

of

Da

ys t

o D

ou

ble

(50

0+

)

No of Cases

Doubling Time for Each State (500+)

MH TN DL TG KLUP RJ AP MP KAGJ JK HR WB OR

0

10000

20000

30000

40000

50000

60000

70000

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100000

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/03

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/03

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/04

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/04

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/05

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/05

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/05

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/05

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/06

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/06

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/06

24

/06

28

/06

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/07

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/07

18

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22

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26

/07

30

/07

03

/08

07

/08

11

/08

15

/08

MH TN UP KA KL RJ TG

AP GJ WB DL MP HR OD

# of Active Cases by Staes over time and AverageGrowth Rate ( 7 Days Average)

Averagegrowth rate (Last 7 Days)MH : 2%TN : 1% UP : 4%KA : 6%KL : 5%RJ : 5%TG :-1%GJ : 2%AP : 9%WB : 3%DL : -4%MP : 3%HR : 1%OD : 7%


17th AUGUST, 2020

STATEWISE PROJECTIONSThe above model is broken down into below numbers for each of the top 10 affected states. The values identify the peaks in worst case scenarios

Maharashtra

10^4

10^12,46,936

Earliest End Date: End OctoberWorst Case : Mid December

Earliest End Date: Mid OctoberWorst Case : Early November

Earliest End Date: Late OctoberWorst Case : Mid NovemberEarliest End Date: Early November

Worst Case : Early November

Earliest End Date: Late OctoberWorst Case : Early November

Earliest End Date: Mid NovemberWorst Case : Mid December

Tamil Nadu

57,96810^2

10^3

10^4

10^5

10^1

10^3

10^4

10^1

Uttar Pradesh

73,243

10^2

10^2

10^3

10^1

Kerala

26,386

10^4

10^3

10^4

10^1

Rajasthan

14,762

10^2

Karnataka

10^5

10^197,163

10^6


17th AUGUST, 2020

STATEWISE PROJECTIONSThe above model is broken down into below numbers for each of the top 10 affected states. The values identify the peaks in worst case scenarios

10^2

10^3

10^5

10^1

Andhra Pradesh

99,827

Earliest End Date: Mid octoberWorst Case : Early November

Earliest End Date: Mid OctoberWorst Case : Early November



10^2

10^3

10^4

10^5

10^1

West Bengal

33,820

10^2

10^3

10^4

10^1

Telangana

29,966

Gujarat

15,33110^2

10^3

10^4

10^5

10^1

10^4


17th AUGUST, 2020

14

MAHARASHTRA’S FIGHT AGAINST COVID-19

Projections

Oct 12End of Infection

30.66 DaysDoubling Rate

0.05Death to Recovery Ratio

32,08,735# of tests done

60,4358Total Cases

20,686 Total Deaths

4,37,870Total Recovered

As of 17th August, total 145,802 active COVID-19 cases and20,686 deaths have been reported in Maharashtra, thefinancial capital of India, Mumbai, being the worst affected.COVID-19 cases reported from Asia’s largest slum, Dharavi hasaggravated the situation Maharashtra government officialsand health care workers are working assiduously to contain thespread.

1.30SEIR Model Reproduction Rate (R0)

0 - 10000

10000 - 20000

50000+

20000 - 30000

30000 - 40000

40000 - 50000

Confirmed Cases


17th AUGUST, 2020

15

ANDHRA PRADESH’S FIGHT AGAINST COVID-19



0.o1Death to Recovery Ratio

29,05,521 # of tests done

2,96,609Total Cases

2,820Total Deaths



0 - 1000

1000 - 2000

5000+

2000 - 3000

3000 - 4000

4000 - 5000

Confirmed Cases

As of 17th July, the total active cases of COVID-19 in Andhra Pradesh is 75,478, of which the maximum cases are in Kurnool, Guntur and Krishna districts. %age of deaths is at 2.8% in comparison to the total number of cases.

Projections


17th AUGUST, 2020

16

GUJARAT’S FIGHT AGAINST COVID-19

Sep 30End of Infection




79,816Total Cases

2,821Total Deaths

63,811Total Recovered


As of 17th August, the total active cases of COVID-19 inGujrat is 13,184, of which the maximum cases are inAhmedabad, Surat, Vadodara, Rajkot districts. % ofdeaths is at 4.6% in comparison to the total number ofcases.

0 - 3000

3000 - 6000

15000+

6000 - 9000

9000 - 12000

12000 - 15000

Confirmed Cases

Projections


17th AUGUST, 2020

17

KARNATAKA’S FIGHT AGAINST COVID-19





2,33,283Total Cases

4,208Total Deaths



0 - 1000

1000 - 2000

5000+

2000 - 3000

3000 - 4000

4000 - 5000

Confirmed Cases

As of 17th August, the total active cases of COVID-19 in Karnataka is 72,125, of which the maximum cases are in Bengaluru, Mysuru and Belagavi districts. % of deaths is at 3.8% in comparison to the total number of cases.

Projections


17th AUGUST, 2020

18

KERALA’S FIGHT AGAINST COVID-19

Sep 30End of Infection




46,140Total Cases

176Total Deaths



As of 17th August, the total active cases of COVID-19in Kerala is 14,575, of which the maximum cases arein Kasaragod, Kannur Kozhikode districts. %age ofdeaths is at 0.6% in comparison to the total numberof cases.

0 -1000

1000 - 2000

2000+

Confirmed Cases

Projections


17th AUGUST, 2020

19

RAJASTHAN’S FIGHT AGAINST COVID-19





62,630Total Cases

898Total Deaths


Rajasthan recorded its first COVID-19 case on 3rd March 2020and by the mid of March, Bhilwara, a district in Rajasthan,seemed on the cusp of a large-scale outbreak. However, sinceMarch 30, Bhilwara has witnessed a huge turnaround and therehas been only a few reported cases. A 'ruthless containment'strategy, timely action, comprehensive screening and strictenforcement of curfew and lockdown restrictions has helpedBhilwara immensely.


0 - 1500

1500 - 3000

7500+

3000 - 4500

4500 - 6000

6000 - 7500

Confirmed Cases

Projections


17th AUGUST, 2020

20

TAMIL NADU’S FIGHT AGAINST COVID-19





3,43,945Total Cases

6,007Total Deaths


Tamil Nadu has the second highest number of COVID-19patients in India with 48,151 active cases on 17th Aug. It haswitnessed a massive rise in positive cases since the beginning ofApril and has identified the majority of cases to a single sourcei.e. a religious congregation that took place in Delhi in March. Inlight of recent events, the state has allocated ₹9,000 crore tofight against the coronavirus pandemic.


0 - 8000

8000 - 16000

40000+

16000 - 24000

24000 - 32000

32000 - 40000

Confirmed Cases

Projections


17th AUGUST, 2020

21

TELANGANA’S FIGHT AGAINST COVID-19





92,255Total Cases

711Total Deaths


The first confirmed COVID-19 case in the Indian state ofTelangana was on 2nd March 2020. Less than 100 cases werereported in month of March, however there has been a sharpincrease in the number of cases since the start of April. Therapid increase in Covid-19 cases in districts has forced the statehealth department to declare nearly 100 villages and areas invarious districts as ‘hotspots’ and impose more restrictions onthe movement of people.


0 - 200

200 - 400

1000+

400 - 600

600 - 800

800 - 1000

Confirmed Cases

Projections


17th AUGUST, 2020

22

WEST BENGAL’S FIGHT AGAINST COVID-19





1,19,578Total Cases

2,528Total Deaths



As of 17th August, the total active cases of COVID-19 inWest Bengal is 24,361, of which the maximum cases arein Kolkata, Howrah and North 24 Parganas districts.%age of deaths is at 2.9% in comparison to the totalnumber of cases.

0 - 2000

2000 - 4000

10000+

4000 - 6000

6000 - 8000

8000 - 10000

Confirmed Cases

Projections


17th AUGUST, 2020

23

UTTAR PRADESH’S FIGHT AGAINST COVID-19





1,58,216Total Cases

2,585Total Deaths


The coronavirus pandemic was first confirmed in the Indianstate of Uttar Pradesh on 5th March 2020, in Ghaziabad. On 17March, the Uttar Pradesh government extended the closure ofall educational institutions, cinemas, shopping malls, swimmingpool, gyms, multiplexes and tourists places in the state till June30th.


0 - 2000

2000 - 4000

4000 - 6000

6000+

Confirmed Cases

Projections


17th AUGUST, 2020

APPENDIX : INFORMATION SOURCES

PERCENTAGE OF DISTRIBUTION OF INFECTION FOR BELOW AND OVER 60 YEARS:

For India: We have taken an 80-20 consideration based on initial trends. The most recent number given out by the MHA was 83% and 17% respectively, but these numbers may keep changing.

For South Korea: https://www.cdc.go.kr/board/board.es?mid=a30402000000&bid=0030

For Italy: https://www.statista.com/statistics/1105061/coronavirus-deaths-by-region-in-italy/

For United States: https://www.businessinsider.in/slideshows/miscellaneous/nearly-30-of-us-coronavirus-cases-have-been-among-people-20-44-years-old-the-cdc-says-showing-that-young-people-are-getting-sick-too/29-of-confirmed-us-coronavirus-cases-have-been-in-people-between-the-ages-of-20-and-44-/slideshow/74720295.cms

PERCENTAGE OF MILD, SEVERE AND CRITICAL CASES

WORLD FIGURES - https://www.worldometers.info/coronavirus/#countries (AS OF MARCH 29)CHINA FIGURES - https://www.worldometers.info/coronavirus/country/china/ (AS OF MARCH 29)(These are critical and non-critical cases)

For An Overall Estimate Of The Number Of Mild, Severe And Critical Cases Around The World, We Have Taken A Study Conducted Involving More Than 44,000 Patients In China Who Tested Positive For Covid-19, With Respect To Their Different Levels Of Severity.

80.9% Of The Cases (36160) Were Categorised As Mild, 13.8% (6168) Were Categorised As Severe And 4.7% (2087) Were Categorised As Critical. 0.6% (257) Were Categorised As Missing. This Roughly Meets The Global Trend Of Critical And Non-Critical Cases Of 95% And 5%, But We Have Gone With This Three-Way Break-Up Of Cases And Mapped It To The World And India Figures.

AGE GROUP DIVISION FOR CASES

We have an average for India based on the cases currently – and that works out to an 80% below 60 and 20% above 60 estimate for the total number of infections. We have three models for the age group division of cases: The World Model of 95% non-critical cases and 5% critical cases. For below 60, we have applied the same numbers to extrapolate a figure

However, for above 60, we have factored in a small difference in margin: made it 90% non-critical cases and 10% critical cases –keeping in mind the fact the possibility of criticality increasing for the older people.

We have applied the China Model of 72% non-critical cases and 28% critical cases – that's the current number among the ACTIVE cases in China. This gives a possible worst case-scenario. For this, we have applied 72% and 28% for the India data, and made it 70% non critical and 30% critical for the above 60 category.

We have a third model, which is the Mild, Severe and Critical Model based on a study of 42000 infections in China conducted in February. We have applied 81% (mild), 14% (severe) and 5% (critical) for the below 60 category, and slightly different numbers for the above 60 category which is 70% mild, 20% severe and 10% critical.

https://www.statista.com/statistics/1105061/coronavirus-deaths-by-region-in-italy/

https://www.businessinsider.in/slideshows/miscellaneous/nearly-30-of-us-coronavirus-cases-have-been-among-people-20-44-years-old-the-cdc-says-showing-that-young-people-are-getting-sick-too/29-of-confirmed-us-coronavirus-cases-have-been-in-people-between-the-ages-of-20-and-44-/slideshow/74720295.cms

https://www.worldometers.info/coronavirus/#countries

https://www.worldometers.info/coronavirus/country/china/


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APPENDIX : INFORMATION SOURCES

OTHER ASUUMPTIONS:

India data as per date of hospitalisation to recovery or continued hospitalisation: https://www.covid19india.org/

The Mild, Severe and Critical Modelhttp://weekly.chinacdc.cn/en/article/id/e53946e2-c6c4-41e9-9a9b-fea8db1a8f51

Factoring in China data of hospitalisation in ICU and hospital in WUHAN based on LANCET study:https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30566-3/fulltext

Factoring in China reports of hospitalisation inside and outside Wuhan (though no specific reference here to ICU data):http://www.xinhuanet.com/english/2020-02/04/c_138755345.htm

Factoring in US studies of potential hospital and ICU days for Covid-19http://www.healthdata.org/sites/default/files/files/research_articles/2020/covid_paper_MEDRXIV-2020-043752v1-Murray.pdf

Note that we have not applied the standard ICU and ward duration of stay because the averages for the US and China and our numbers too from data available for CORONA is significantly higher.

https://www.covid19india.org/

http://weekly.chinacdc.cn/en/article/id/e53946e2-c6c4-41e9-9a9b-fea8db1a8f51

https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30566-3/fulltext

http://www.xinhuanet.com/english/2020-02/04/c_138755345.htm

http://www.healthdata.org/sites/default/files/files/research_articles/2020/covid_paper_MEDRXIV-2020-043752v1-Murray.pdf


17th AUGUST, 2020

COVID-19DISCLAIMER

The pandemic novel coronavirus 2019, is rapidly evolving, with new findings and insights being discovered daily. This document

is a series of observations that represents Protiviti and Times Network viewpoint, at a certain point in time. This document does not constitute or claims to serve as an advisory for any

medical, safety or regulatory action.


17th AUGUST, 2020

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overview of covid-19...delhi 10.46% maharashtra 29% tamil nadu 6.7% overview of covid-19 an india...

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