shananalla Profile picture
12 Sep, 37 tweets, 17 min read
Karnataka provides very detailed information on all its discharges and deaths in its daily bulletin

I scraped that data (from Jul-14 to Sep-10), and this thread contains a detailed analysis #CoronavirusInIndia

This follows the pattern of Odisha's analysis, but is more detailed
To avoid clutter, the following conventions are used
KAR: Karnataka (all districts)
BLR: Bengaluru
ROK: Rest of Karnataka
From the Patient no.,we can calculate the date of confirmation. This allows us to estimate the interval between Confirmation-Discharge
- Distribution is bimodal (2 peaks)
- Peak at ~7 days (due to mild/asymp.)
- Peak at 14-16 days (due to severe/symp. )
- 2nd peak very pronounced for BLR compared to ROK
=> Proportion of severe patients is significantly higher in BLR
How has this changed over time:
mean values:
Sep: 11.1 days
Aug: 10.8 days

- Lower interval implies (proportionately) fewer severe patients
- Early positive sign in slight down-slope of Linear fit
- Need more data from Sep to conclusively prove improvement
Mean value
KAR: 60.7 yrs
BLR: 60.1
RoK: 70
Over time:
sep: 61.6 yrs
aug: 61.1
jul: 59.2

-Slow increase in mean age of death over time is similar to OD

Gender Profile:
- Fatalities are overwhelmingly Male
KAR: 68.3% M , 31.7% F
BLR: 67.7% M , 32.3% F
ROK: 68.6% M , 31.4% F

Again, similar to OD
- The age-profile has'nt changed much over time (best fit is nearly flat)
Fraction of Males among a certain date's fatalities is plotted (best fit is nearly flat)
- Mean age of fatalities is practically identical across genders
M: 60.8
F: 60.4
Again similar to OD
27.5% had NO Comorbidities
72.5% had Comorbidities

-Co-morbidities(especially DM and HTN) very often occur together in the same patients
Common Comorbidities
Diabetes: 39.2%
Hypertension: 38.1%
Heart Disease: 8.9%
Kidney Disease:5.6%
COPD: 1.8% (??)

(Doctors,what is COPD?)
Number of co-morbidities among those who ]have them ( eg. Diabetes+Hypertension )
1 : 48%
2 : 39% (often DM and HTN)
3+: 13%
Proportion of those with no comorbidities
- v. slightly higher among women
M: 27.3%, F: 27.8%

- higher in ROK vs BLR
BLR: 21.9%
ROK: 30.2%

- In ROK larger share patients dying despite no co-morbidity
Percent of those with NO comorbidites by age
60+ : 21.8%
40-60: 30.2%
20-40: 50.9%
0-20: 82.1% (only 28 individuals)

As expected, young have fewer comorbidities
Origin (SARI/ILI/Contact)

- Fraction of SARI/ILI deaths among total(plotted) is a measure of community transmission
KAR: 86.3% deaths are SARI/ILI
BLR: 96.4%
ROK: 81.4%
- V. different picture in BLR vs ROK
- 100%(!!) of BLR's deaths since mid-August have been SARI/ILI
- A v. high fraction indicates that very few contacts(at least among fatalities) were traced
- Lower fraction in ROK indicates containment may not be a lost cause there
- Early positive sign in slight down-slope of linear fit
admission-death interval
- A measure of quality/effectiveness of hospital care

Distribution is an exponential decay
Mean/Median (in days)
KAR: 4.4, 3
BLR: 4.7, 3
ROK: 4.3, 3
- higher value in BLR might indicate better availability of hospital care in BLR
KAR Admission-Death Interval
Home deaths: 7.2% ( or brought dead to hospital)
0-2 days : 44.5%
3-7 days : 29.8%
8+ days : 18.3%
over time:

KAR: (Mean/Median values)
Sep: 5.2,4.0 median
Aug: 4.6,3.0 median
Jul: 2.7,1.0 median
- Indicates things were v. bad in July, but patients are spending longer in hospitals in August-Sep
v.different in BLR vs ROK
Mean,Median values
Sep: 6.2,4.0
Aug: 6.1,4.0
Jul: 2.0,1.0
- A lengthening interval in BLR indicates things in v. bad shape in July, but are improving since
Mean,Median values
Sep: 4.7,3.0
Aug: 4.1,3.0
Jul: 3.2,2.0
- Indicates The virus hasn't hit ROK hospitals as hard as BLR in July
Confirmation-admission interval:
- Time between confirmation (in bulletin) and admission to hospital
Mean value
BLR: : -2.9 days (yes, its negative!)
ROK : -3 days
- v. sharp peak at -3 days
- i.e. most deaths were confirmed(in the bulletin) 3 days *after* admission
- Example:

P.51825 48-yr old Female from Mysuru died on 18 July
-she was detected on 17 Jul (1st is from the Jul-17 bulletin)
-but was admitted on 14 Jul, i.e. 3 days prior (2nd image is from July-19 bulletin)
over time:
Mean,median values (all Karnataka)
Sep: -2.2,-2 days
Jul: -2.8,-3 days
- Indicates patients are confirmed (tested +ve) sooner in Sep vs Jul. This is good.
Death-Reporting interval:
- Lots of states(MH,DL etc) have reported deaths from "backlogs"
- Karnataka's data lets us evaluate its reporting backlog quantitatively

KAR: 5.1, 3 days
-Sharp peak around 2-3 days
-Small fraction reported on same days(interval=0)
over time
Mean,Median values
sep: 2.0,2
aug: 4.7,3
jul: 6.8,4
- Indicates the state's reporting systems was overwhelmed around July (v. long interval), but things have improved v. significantly since
SUMMARY (Conclusions from the data)
- BLR discharge distribution(strong peak around 14-16 days) indicates BLR is mostly detecting severe/symp. cases

- ROK is detecting mostly mild/asymp. cases (single peak around 7 days)
- Fatalities are overwhelming old and male. This hasn't changed over time
- Most fatalities have co-morbidites (similar to OD)
- Fewer fatalities in BLR have co-morbidities (vs ROK)
- Younger have *far* fewer co-morbidities
- High fraction of SARI/ILI deaths indicates community transmission is well-established since July(especially in BLR)
- In July patients(especially in BLR) died very soon after admission (~1 day). This has improved to >6 days in Sep.
- Most patients are confirmed *after* being admitted (typically 2-3 days later)
- Deaths are typically reported(in bulletin) 2-3 days after they occur
Kudos to Karnataka for providing such detailed data in its bulletin. If other states provided even half as much information, we'd know a lot more about India's epidemic @epigiri
-Source code for parsing and analysis is at…
- Raw data on KAR's fatalities (csv format) is at…
- For convenience an archive of all the raw PDF bulletins is at…

Apologies for the (very) long thread length, but I thought it was better to analyze all of Karnataka's data in one place , in a single thread

• • •

Missing some Tweet in this thread? You can try to force a refresh

Keep Current with shananalla

shananalla Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!


Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @shananalla

13 Sep
Tamil Nadu provides detailed information on its fatalities in its daily bulletin
I scraped that data (Jul-1 to Sep-10).
This thread contains a preliminary analysis, and a comparison with Karnataka/Odisha (at the end) @epigiri ImageImage
mean age: 63.1 yrs

Very clear clustering in the 60-80 yrs range
The Mean age has increased significantly from July-Sep ImageImage

Male : 71.9%
Female: 27.6%

Fraction of Males in daily deaths has increased slowly over time Image
Read 17 tweets

Did Thread Reader help you today?

Support us! We are indie developers!

This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Too expensive? Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal Become our Patreon

Thank you for your support!

Follow Us on Twitter!