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Aug 9, 2021 13 tweets 5 min read
Understanding reinfections is critical as a large share of our population has already been infected

A summary of the (limited) data, some extrapolation, and what it means for the 3rd wave 🧵

For conciseness, NI(X) will denote "protection due to infection against outcome X" The are 4 heads under which I'll examine the data

a) (protection against) asymptomatic infection
b) "" symptomatic infection
c) severe disease/hospitalization(/death)
d) Transmission
Jul 4, 2021 4 tweets 2 min read
ICMR-NIV neutralization study vs B.1/Delta
, among Covishield vaccinees, who were

A) uninfected, 1-dose (n=31)
B) uninfected, 2-dose (n=31)
C) (previously) infected, 1-dose (n=15)
D) infected, 2-dose (n=19)
E) Vaccine breakthrough (n=20) Previously infected had much higher titres (even 1-dose) vs previously uninfected (even after 2-doses)

18/31 (58%) uninfected showed no Nab after 1-dose

5/31 (16%) uninfected showed no Nab after 2-dose @singhak_endo

All previously infected seroconverted after 1-dose
Jul 3, 2021 15 tweets 7 min read
The long-awaited Covaxin Phase-3 data came out this morning (… )

Headline results are good efficacy against symp. infection, along with v. good efficacy against severe disease

Some other things I found interesting, and some links with other Indian data: First, a note on the CIs

The study was designed to evaluate efficacy against symp. infection. The design/sample size was based on that

This means that any subgroup analysis (by age/variant/comorbidities etc) will "automatically" have wider CIs

This is v. much expected
Jul 2, 2021 6 tweets 3 min read
This thread describes a SIR model for Delhi that uses google mobility data and genomic surveillance to refine its output

Using known parameters, it reproduces the epi-curve, variant frequencies etc fairly well

A general introduction to SIR is below
It has been calculated elsewhere that
Alpha has a ~50-60% Tx advantage over B.1
Delta has a ~60-70% advantage over alpha

So, R0b~2.5, R0a~4,R0d~7
A common generation time (5 days) is used throughout

For dates of introduction, I use data from CSIR paper (…)
Jul 2, 2021 8 tweets 4 min read
A brief description of SIR epidemic models for later reference (with extensions to variants, reinfections, mobility etc):

One of the simplest way to mathematically model the evolution of an epidemic is to use the SIR model

In this model, there are 3 classes of individuals: a) "Susceptible": Fraction of pop. thats never been infected
b) "Infected": Fraction of pop. currenly infected
c) "Recovered": Fraction of pop. that has recovered as is now immune

G is the generation time (how long someone is infectious)
R0,M etc as below
Jul 1, 2021 5 tweets 3 min read
While Delhi has been reporting few daily cases (~100), the R value (measured by 5-day change here) has been rising

On June 22/23, the city added ~740 backlog cases. I've removed those for a more accurate (recent) picture

There are 3 major contributors here: Image a) As more people get infected, the population immunity increases. This reduces R

b) As vaccination coverage increases, R decreases (it falls more slowly if vaccine effectiveness is lower)

c) As (decline in) mobility/average contacts per person decreases, R increases Image
May 6, 2021 6 tweets 5 min read
Karnataka has also been seeing a (mostly) steady and sustained decline in mean age of daily deaths since early March (beginning of vaccination for 60+) unlike TN,KA provides age/district wise vaccination data in its bulletins

I've plotted the inverse of that (fraction of *unvaccinated * 60+ over time) vs percent of 60+ in KA's daily deaths

as more and more elderly get vaccinated, the fraction of elderly deaths falls sharply
Apr 30, 2021 5 tweets 3 min read
Fraction of elderly cases(60+) in TN has been falling for the most part since eqarly march Image Percent of deaths among 60+ has also (mostly) been falling since early march Image
Oct 23, 2020 15 tweets 12 min read
Tamil Nadu's epidemic seems to have peaked, with TPR/deaths/case-counts falling and steady levels of testing

I updated the previous thread by parsing TN's fatality data (from daily bulletins) from Sep 10-Oct 22.

Age Profile:

Deaths continue to be clustered among the elderly.

Mean age: 63.5 yrs

The trend of increasing mean age over time has continued into Sep-Oct

Oct 21, 2020 10 tweets 8 min read
While India's mortality rate has reduced faily steadily over time, there are wide variations among states.

This thread looks at possible explanations, and what TN's specific case can tell us about reducing it further


TN(and other states') data clearly shows that the elderly make up a very high fraction of fatalities

As the graph shows, as mean age (2011 census) increases, mortality rate increases

Sep 13, 2020 19 tweets 13 min read
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 Age-Profile
mean age: 63.1 yrs

Very clear clustering in the 60-80 yrs range
The Mean age has increased significantly from July-Sep
Sep 12, 2020 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