![Covid Data Analytics : Case Study on India
Bankar Prasad Vilas Subhasis Panda Vaibhav Anand Vineet Kumar
Indian Statistical Institute, Kolkata
https://covid-isical.tech contact@covid-isical.tech
Introduction
We provide a macro-level India specific analysis on COVID
related data as obtained from [1]. We built a dashboard
available at [2] by giving multi-dimensional view and risk
summary for India.
Spread Assessment Metrics
Increasing trend of occurrence of new cases is likely to
indicate that the virus is spreading.
Figure 1: Average confirmed cases / day in India for each
week (last 7 weeks)
Increasing trend of active cases indicates the possibility
of increased stress on resources at present or in the near
future.
Figure 2: Active cases in India for the last 7 weeks
Risk Assessment Metrics
Avg. Confirmed Cases to Avg. Recoveries in Consecutive
Weeks Traffic Intensity (term from queuing theory) —
• Leading indicator of stress on resources
• Values > 1 implies faster arrival of new cases
compared to recovery
Figure 3: Traffic Intensity for India for the last 7 weeks
Points plot of Test in Each Week and % positive in each
week Higher proportion positive test results on a larger
test population (possibly less targeting) are anomalous
and need investigation.
There are at least three possibilities:
• Rapid increase of rate of infection
• Carrying out tests in new (hitherto untested) areas
with high but unknown rate of infection
• Improper testing leading to many incorrectly positive
results
Figure 4: Assessing adequacy of testing in India
Comparative Assessment
Heat maps showing the top 10 States in India w.r.t con-
firmed (blue), recovered (green), and deceased (red), re-
spectively.
Figure 5: Heat map of last 15 days
Bounds on Death Rate
Crude Fatality Rate Calculated on daily basis using
P
Death
P
Confirmed
.
Figure 6: Crude Fatality Rate
Kaplan Meier Analysis The estimator of the survival func-
tion S(t) (the probability that life is longer than t is given
by:
d
S(t) =
Y
i: ti≤t
1 −
di
ni
with ti a time when at least one event happened, di the
number of events (e.g., deaths) that happened at time ti,
and ni the individuals known to have survived (have not
yet had an event or been censored) up to time ti.
Figure 7: KM Curve for Survival Estimation
Spread-based Clustering
Clustered Districts based on number of active cases and
deaths using k means and agglomerate clustering tech-
niques.
MH
TN
DL
GJ
KA
UP
TG
AP
WB
RJ
HR
BR
MP
AS
OR
JK
PB
KL
-0.5
0.0
0.5
1.0
-5.0 -2.5 0.0
Dim1 (94.8%)
Dim2
(3.5%)
cluster
a
a
a
a
1
2
3
4
Cluster plot
MH
TN
DL
PB
JK
KL
OR
BR
AS
MP
RJ
HR
KA
GJ
UP
WB
TG
AP
0
2
4
6
8
10
Cluster Dendrogram
hclust (*, "ward.D2")
d
Height
Figure 8: Clustering states based on infection spread
References
[1] https://api.covid19india.org/
[2] https://covid-isical.tech/
Summer School of Machine Learning at Skoltech, SMILES – Aug’ 2020](https://image.slidesharecdn.com/covidposter-210516162615/85/Covid-Data-Analytics-Case-Study-on-India-1-320.jpg)
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- 1. Covid Data Analytics : Case Study on India Bankar Prasad Vilas Subhasis Panda Vaibhav Anand Vineet Kumar Indian Statistical Institute, Kolkata https://covid-isical.tech contact@covid-isical.tech Introduction We provide a macro-level India specific analysis on COVID related data as obtained from [1]. We built a dashboard available at [2] by giving multi-dimensional view and risk summary for India. Spread Assessment Metrics Increasing trend of occurrence of new cases is likely to indicate that the virus is spreading. Figure 1: Average confirmed cases / day in India for each week (last 7 weeks) Increasing trend of active cases indicates the possibility of increased stress on resources at present or in the near future. Figure 2: Active cases in India for the last 7 weeks Risk Assessment Metrics Avg. Confirmed Cases to Avg. Recoveries in Consecutive Weeks Traffic Intensity (term from queuing theory) — • Leading indicator of stress on resources • Values > 1 implies faster arrival of new cases compared to recovery Figure 3: Traffic Intensity for India for the last 7 weeks Points plot of Test in Each Week and % positive in each week Higher proportion positive test results on a larger test population (possibly less targeting) are anomalous and need investigation. There are at least three possibilities: • Rapid increase of rate of infection • Carrying out tests in new (hitherto untested) areas with high but unknown rate of infection • Improper testing leading to many incorrectly positive results Figure 4: Assessing adequacy of testing in India Comparative Assessment Heat maps showing the top 10 States in India w.r.t con- firmed (blue), recovered (green), and deceased (red), re- spectively. Figure 5: Heat map of last 15 days Bounds on Death Rate Crude Fatality Rate Calculated on daily basis using P Death P Confirmed . Figure 6: Crude Fatality Rate Kaplan Meier Analysis The estimator of the survival func- tion S(t) (the probability that life is longer than t is given by: d S(t) = Y i: ti≤t 1 − di ni with ti a time when at least one event happened, di the number of events (e.g., deaths) that happened at time ti, and ni the individuals known to have survived (have not yet had an event or been censored) up to time ti. Figure 7: KM Curve for Survival Estimation Spread-based Clustering Clustered Districts based on number of active cases and deaths using k means and agglomerate clustering tech- niques. MH TN DL GJ KA UP TG AP WB RJ HR BR MP AS OR JK PB KL -0.5 0.0 0.5 1.0 -5.0 -2.5 0.0 Dim1 (94.8%) Dim2 (3.5%) cluster a a a a 1 2 3 4 Cluster plot MH TN DL PB JK KL OR BR AS MP RJ HR KA GJ UP WB TG AP 0 2 4 6 8 10 Cluster Dendrogram hclust (*, "ward.D2") d Height Figure 8: Clustering states based on infection spread References [1] https://api.covid19india.org/ [2] https://covid-isical.tech/ Summer School of Machine Learning at Skoltech, SMILES – Aug’ 2020