OVERVIEW OF COVID-19 - imgk.timesnownews.com · OVERVIEW OF COVID-19 AN INDIA PERSPECTIVE 27th...
Transcript of OVERVIEW OF COVID-19 - imgk.timesnownews.com · OVERVIEW OF COVID-19 AN INDIA PERSPECTIVE 27th...
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 2020
OVERVIEW OF COVID-19
AN INDIA PERSPECTIVE
Wave 1327th July 2020
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 2020
EXECUTIVE SUMMARY
This document presents a study performed by Protiviti and Times Network as of 26th July 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 3 top hot-spots along with the entire country for aperiod of close to 50 days to project the evolution of the COVID19 pandemic using the ‘Number of dailynew cases’ along with the total ‘active’ patients count.
The projected evolution of infected cases assumes an inherited effect of countrywide lockdown appliedfrom 24 March 2020 and the numbers proposed for the future also consider that this period of lockdowncontinues for the next 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 999,087 cases on an optimisticside to around 1,015,231 on a higher side and expected to happen around beginning of 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 might come out of it during later part of August to early September while some highlyinfected 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.
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 2020
SETTING THE CONTEXT
This document presents a study performed by Protiviti and Times Network as of 26th July 2020,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 3 top hot-spots 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.
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 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.
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 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.
1,435,916 Total Cases on 26th
July 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
14 WeeksOf mandatory social
Distancing, has helped contain virus within
404 districts
10,473,771Tests 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%
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 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, 16,399,010* 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 26th July 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
16,410,960 651,881 93.1% 9.5%
1,435,916 32,826 50% 3%
Total Cases Total DeathsRecovered / Total
cases with outcomeDeath / Total cases
with outcome
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 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.
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 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
20
/03
27/
03
03/
04
10/0
4
17/0
4
24
/04
01/
05
08
/05
15/0
5
22
/05
29
/05
05/
06
12/0
6
19/0
6
26
/06
03/
07
10/0
7
17/0
7
24
/07
31/0
7
07/
08
14/0
8
21/
08
28
/08
04
/09
11/0
9
18/0
9
25/
09
Total Number of Active Forecasted Cases
Best Case Worst Case Most Likely
10^2
10^3
10^4
10^5
10^6
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 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 1.63 with 95% CI [1.59 , 1.69 ], whereas for different states the effective reproduction number varies in between 1.2 to 1.6
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
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 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 10.15 lakh patients. We expect that in the best case scenario, India will reach around 9.99 lakh cases, around 9.85 lakh cases in a most likely scenario and 10.15 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 October.
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
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 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), Uttar Pradesh
(UP), Kerala (KL), 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
10000
20000
30000
40000
50000
60000
70000
80000
90000
100000
20
/03
24
/03
28
/03
01
/04
05
/04
09
/04
13
/04
17
/04
21
/04
25
/04
29
/04
03
/05
07
/05
11
/05
15
/05
19
/05
23
/05
27
/05
31
/05
04
/06
08
/06
12
/06
16
/06
20
/06
24
/06
28
/06
02
/07
06
/07
10
/07
14
/07
18
/07
22
/07
26
/07
MH TN UP KA KL RJ TGAP GJ WB DL MP HR OD
# of Active Cases by Staes over time and Average Growth Rate ( 7 Days Average)
Average growth 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%
0
5
10
15
20
25
30
35
40
1K+ 2K+ 4K+ 8K+ 16K+ 32K+ 64K+ 128k+ 256K+No
of
Da
ys t
o D
ou
ble
(50
0+
)
No of Cases
Doubling Time for Each State (500+)
MH TN DL TG KL UP RJ AP
MP KA GJ JK HR WB OR
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 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
10^1203,915
Earliest End Date: Late SeptemberWorst Case : Mid October
Earliest End Date: Late SeptemberWorst Case : Mid October
Earliest End Date: Late SeptemberWorst Case : Mid OctoberEarliest End Date: Late September
Worst Case : Mid October
Earliest End Date: Late SeptemberWorst Case : Mid October
Earliest End Date: Mid SeptemberWorst Case : Mid October
Tamil Nadu
76,14410^2
10^3
10^4
10^5
10^1
20/03 20/04 20/05 20/06 20/07 20/08 20/09
10^3
10^4
10^1
Uttar Pradesh
51,659
10^2
20/03 20/04 20/05 20/06 20/07 20/08 20/09
10^2
10^3
10^1
Kerala
15,672
10^4
20/03 20/04 20/05 20/06 20/07 20/08
10^3
10^4
10^1
Rajasthan
20,150
10^2
Karnataka
10^5
10^1147,974
10^6
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 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
123,336
Earliest End Date: Late SeptemberWorst Case : Mid October
Earliest End Date: Mid SeptemberWorst Case : Mid October
Earliest End Date: Late SeptemberWorst Case : Mid October
Earliest End Date: Late SeptemberWorst Case : Mid October
10^2
10^3
10^4
10^5
10^1
West Bengal
27,193
10^2
10^3
10^4
10^1
Telangana
22,530
Gujarat
17,39110^2
10^3
10^4
10^5
10^1
10^4
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 2020
14
MAHARASHTRA’S FIGHT AGAINST COVID-19
Projections
Oct 12End of Infection
22.0 DaysDoubling Rate
0.06Death to Recovery Ratio
1,890,256# of tests done
375,799Total Cases
13,655 Total Deaths
213,238Total Recovered
As of 26th July, total 148,906 active COVID-19 cases and 13,655deaths have been reported in Maharashtra, the financialcapital of India, Mumbai, being the worst affected. COVID-19cases reported from Asia’s largest slum, Dharavi hasaggravated the situation Maharashtra government officialsand health care workers are working assiduously to contain thespread.
1.42SEIR Model Reproduction Rate (R0)
0 - 10000
10000 - 20000
50000+
20000 - 30000
30000 - 40000
40000 - 50000
Confirmed Cases
Maharashtra
10^4
10^1
203,915
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 2020
15
ANDHRA PRADESH’S FIGHT AGAINST COVID-19
Oct 15End of Infection
7.42 DaysDoubling Rate
0.o2Death to Recovery Ratio
1,643,319 # of tests done
96,298Total Cases
1,041Total Deaths
46,301Total Recovered
1.37SEIR Model Reproduction Rate (R0)
0 - 1000
1000 - 2000
5000+
2000 - 3000
3000 - 4000
4000 - 5000
Confirmed Cases
As of 26th July, the total active cases of COVID-19 in Andhra Pradesh is 48,956, 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
10^3
10^5
10^1
Andhra Pradesh
123,336
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 2020
16
GUJARAT’S FIGHT AGAINST COVID-19
Sep 30End of Infection
49.71 DaysDoubling Rate
0.06Death to Recovery Ratio
642,370# of tests done
55,822Total Cases
2,326Total Deaths
40,366Total Recovered
1.23SEIR Model Reproduction Rate (R0)
As of 26th July, the total active cases of COVID-19 inGujrat is 13,130, 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
Gujarat
17,39110^2
10^3
10^4
10^5
10^1
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 2020
17
KARNATAKA’S FIGHT AGAINST COVID-19
Oct 20End of Infection
14.10 DaysDoubling Rate
0.05Death to Recovery Ratio
1,176,827# of tests done
96,141Total Cases
1,887Total Deaths
35,839Total Recovered
1.60SEIR Model Reproduction Rate (R0)
0 - 1000
1000 - 2000
5000+
2000 - 3000
3000 - 4000
4000 - 5000
Confirmed Cases
As of 26th July, the total active cases of COVID-19 in Karnataka is 58,415, 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
Karnataka
10^5
10^1
147,974
10^6
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 2020
18
KERALA’S FIGHT AGAINST COVID-19
Sep 30End of Infection
10.07 DaysDoubling Rate
0.01Death to Recovery Ratio
672,748# of tests done
19,026Total Cases
62Total Deaths
9,299Total Recovered
1.31SEIR Model Reproduction Rate (R0)
As of 26th July, the total active cases of COVID-19 inKerala is 9,665, of which the maximum cases are inKasaragod, Kannur Kozhikode districts. %age ofdeaths is at 0.6% in comparison to the total numberof cases.
0 -1000
1000 - 2000
2000+
Confirmed Cases
Projections
20/03 20/04 20/05 20/06 20/07 20/08 20/09
10^2
10^3
10^1
Kerala
4,835
10^4
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 2020
19
RAJASTHAN’S FIGHT AGAINST COVID-19
Oct 20End of Infection
19.47 DaysDoubling Rate
0.02Death to Recovery Ratio
1,403,124# of tests done
36,430Total Cases
624Total Deaths
25,954Total Recovered
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.
1.22SEIR Model Reproduction Rate (R0)
0 - 1500
1500 - 3000
7500+
3000 - 4500
4500 - 6000
6000 - 7500
Confirmed Cases
Projections
20/03 20/04 20/05 20/06 20/07 20/08
10^3
10^4
10^1
Rajasthan
20,150
10^2
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 2020
20
TAMIL NADU’S FIGHT AGAINST COVID-19
Oct 10End of Infection
24.03 DaysDoubling Rate
0.02Death to Recovery Ratio
2,351,463# of tests done
213,723Total Cases
3,494Total Deaths
156,526Total Recovered
Tamil Nadu has the second highest number of COVID-19patients in India with 53,703 active cases on 26th July. 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.
1.59SEIR Model Reproduction Rate (R0)
0 - 8000
8000 - 16000
40000+
16000 - 24000
24000 - 32000
32000 - 40000
Confirmed Cases
Projections
Tamil Nadu
76,14410^2
10^3
10^4
10^5
10^1
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 2020
21
TELANGANA’S FIGHT AGAINST COVID-19
Oct 01End of Infection
20.45 DaysDoubling Rate
0.01Death to Recovery Ratio
353,425# of tests done
54,059Total Cases
463Total Deaths
41,332Total Recovered
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.
1.39SEIR Model Reproduction Rate (R0)
0 - 200
200 - 400
1000+
400 - 600
600 - 800
800 - 1000
Confirmed Cases
Projections
10^2
10^3
10^4
10^1
Telangana
22,530
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 2020
22
WEST BENGAL’S FIGHT AGAINST COVID-19
Oct 03End of Infection
14.72 DaysDoubling Rate
0.04Death to Recovery Ratio
805,185# of tests done
58,718Total Cases
1,372Total Deaths
37,750Total Recovered
1.30SEIR Model Reproduction Rate (R0)
As of 26th July, the total active cases of COVID-19 inWest Bengal is 19,596, 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
10^2
10^3
10^4
10^5
10^1
West Bengal
27,193
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 2020
23
UTTAR PRADESH’S FIGHT AGAINST COVID-19
Oct 05End of Infection
17.18 DaysDoubling Rate
0.03Death to Recovery Ratio
1,834,297# of tests done
66,988Total Cases
1,426Total Deaths
41,641Total Recovered
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.
1.25SEIR Model Reproduction Rate (R0)
0 - 2000
2000 - 4000
4000 - 6000
6000+
Confirmed Cases
Projections
20/03 20/04 20/05 20/06 20/07 20/08 20/09
10^3
10^4
10^1
Uttar Pradesh
51,659
10^2
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 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.
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 2020
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.
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 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.
OVERVIEW OF COVID-19AN INDIA PERSPECTIVE
27th JULY, 2020
The underline publication/report/presentation contains information obtained from the public domain/external sources which is provided in good faith, however the same has not been verified for authenticity, accuracy, reliability or completeness.
Our publication may make reference to “Protiviti Member Firm-India”, this merely indicates that we have (where specified) undertaken certain analytical activities/adopted certain model of the underlying data to arrive at the information presented, we do not accept responsibility of the veracity, quality of source of the data or any action or inaction regarding transmission, communication or usage of its content. By using this publication/report/presentation the user shall be deemed to have accepted the terms of the publisher/presenter.
“Protiviti Member Firm-India” is not liable for any damages arising from the use or inability to use this publication/report/presentation or any material contained in it, or from any action or decision taken as a result of using the publication/report/presentation. This Presentation is been made available on hold harmless basis only. No representation or warranties whatsoever are given or to be implied. No commitment is made and presumed action should be assumed, including with regard to correcting or updating or following-up. It is entirely for the professional recipients of this presentation to make their own determination about what may or may not be accurate or appropriate.
Save as specifically agreed in writing by “Protiviti”, the publication/report/presentation and the information provided therein must not be copied, reproduced, distributed or passed in whole or in part to any other person/organization. “Protiviti” had prepared this publication/report/presentation on the basis of publicly available information and it does not constitute or serves as a substitute for an audit, due diligence or a similar review. This publication/report/presentation is based on prevailing economic, regulatory, market and other conditions as well as the information provided to “Protiviti” as of the date of preparation of this publication/report/presentation, all of which are subject to changes which may impair the information and statements given in this publication/report/presentation.