@stellensatz Starting a new thread on states where infections are not coming down as expected, leading to apprehensions about a third wave. Let us see Kerala first. As one can see, the numbers were coming down nicely until mid-June, but then plateaued and now are rising. Image
Current phase plot for Kerala shows continuous drifting. The points are turning, which indicates that stability is still far off. What is causing this? Parameter estimates, admittedly imprecise due to drift, show that contact rate is not high, but reach has increased by 25%. Image
It could be due to either pandemic expanding to newer regions, or a new mutant that is bypassing existing immunity to a significant extent. Genome sequencing done in Kerala has not thrown any such new mutant so far. So it is likely to be former, which is good news!
Now Maharashtra. It also underwent a phase change in mid-June and since then has plateaued. Image
Current phase plot shows points traversing on a line, but this line does not pass through origin. This indicates a slow drift. However, it is nowhere close to sharp drift in Kerala and so one hopes it will stabilize soon. Image
Parameters estimated for the current phase show no change in contact rate and about 20% increase in reach. Genome sequencing has not found any new variant here as well.
Finally Manipur. A sharp rise is observed from 21st June and the model is unable to capture it fully yet. Phase plot is showing some signs of stabilizing. Image
Estimates done for parameters of current phase show something interesting. While reach has increased by 15%, contact rate has reduced by 10%! Then what is causing the sharp rise in cases? It is epsilon! It has gone up by 20%!
These estimates need to be taken with a pinch of salt due to phase not stabilizing yet. If they are close to actual values, it means that there is no real rise. Just that fewer cases are undetected. It is backed by the fact that in June, large scale testing was carried out.
Kerala continues to drift. I had suggested two weeks ago that the rise is Kerala is being fueled by expansion of reach. With the ICMR serosurvey showing ~48% seropositivity for Kerala in June, the model can be calibrated and more precise projections done.
Reach of pandemic by mid-June was ~65%. It means that ~35% population was untouched by pandemic. Since then reach has been increasing and is above 90% now. As phase plot has not stabilized, this may change. However, it is clear that reach has gone up significantly in past month.
At present, ~51% population is immune and there is much room to grow. Pandemic is unlikely to subside anytime soon there. So why is Kerala different from most other states? It is because the state has been trying to control the reach of the pandemic, and succeeding to an extent.
Reach until March-end was ~26% in Kerala. In contrast, UP was 50+%. So were several other states. Strategy of aggressively controlling reach works well when one has resources and people cooperate. Otherwise one gets a large number of cases for extended period as is happening now.
Now that reach appears close to maximum, control on contact rate will determine how fast the pandemic spreads. At present, contact rate is controlled due to lockdown, leading to slow rise in cases. The moment lockdown is lifted, cases will rise sharply.
Is Kerala strategy better? This depends on which one prefers: extended lockdown leading to economic misery or sharp rise leading to stress in health infra for a short period. My view is that Kerala health infra is decent so it should go for a sharper rise leading to quicker end.
Kerala is testing well though -- it is missing only 4.5 cases for every detected case. Overall for India, about 32 cases are undetected for every detected one.
Much discussion around Kerala model these days. Two sides of the debate are using same data, low seropositivity and high TPR, to argue opposite points! The supporters of government say that low seropositivity demonstrate success of containment strategy.
And high TPR show that smart testing is being done. Opponents say that low seropositivity mean there is much larger susceptible population that other states which means pandemic has still a long way to run there, and high TPR indicates that infection is spreading significantly.
Both are true, but, only partially. Let me explain why. The plot below is the entire timeline for Kerala showing how the values of reach and contact rate parameters have changed to influence the trajectory. The values are reasonably precise thanks to calibration via serosurvey. Image
Until August-end, reach in Kerala was a mere 3%. This demonstrates success of containment strategy adopted by the state -- the pandemic could not even reach 97% of population! In September, reach jumped to 11% due to onset of festival season.
It jumped again to 21% in October-end, but the cases did not rise due to reduction in contact rate. Both the parameters increased slowly over next four months resulting in almost flat trajectory that eventually came down in February.
And then delta-variant struck resulting in sharp increases in both reach and contact rate. Reach is now nearly 94% thanks to another sharp rise during mid-June to mid-July.
The containment strategy adopted by the state was a success until February. It managed to keep most of the population away from the pandemic. However, with onset of delta-variant, the strategy has not worked well resulting in spread of pandemic everywhere.
With seropositivity around 52% at present and pandemic spread over nearly entire population, there is a long way to go before herd immunity is reached. There are now two options available: keep contact rate low through lockdowns, or allow it to increase further by opening up.
If contact rate is brought down from its current value of 0.33, the numbers will start reducing, however, herd immunity will not be reached and lockdown will have to be kept until a large fraction is vaccinated.
If strict lockdown is lifted and basic rules are enforced like wearing masks, avoiding crowding etc, the contact rate should settle down around 0.35. The resulting trajectory is shown below by dashed line. It shows a peak of around 25K infections by mid August. Image
By September, seropositivity would be 75% and then even without any precautions cases will not rise. To me, this appears a better strategy. Kerala already has had a very long lockdown leading to significant economic and emotional stress. It is time to open up.

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More from @agrawalmanindra

2 Jul
<SUTRA's analysis of third wave> @stellensatz @Ashutos61 @Sandeep_1966 @shekhar_mande It took us a while to do the analysis for three reasons. First, loss of immunity in recovered population. Second, vaccination induced immunity. Each of these two need to be estimated for future.
And third, how to incorporate the two in the model. Fortunately, it turned out that both can be incorporated by suitably changing contact rate and reach parameters. So that takes care of third one. First two required detailed analysis.
We went through the studies done in the past on loss of immunity and used conservative numbers for them. Similarly, we looked at the projected vaccination rate over next few months, included the effects of vaccine-hesitancy, and arrived at month-wise estimates for vaccination.
Read 9 tweets
17 May
Starting a new thread on analysis of lockdown in various states. First, let us examine UP. The plot for UP and SUTRA projections for it from 1st March are below.
UP went through two phase changes in this period. First started on 15th March with 10 days of drift. In this phase, the contact rate went up to 0.53 (95% CI: +- 0.03) from 0.4. And reach roughly doubled. This double whammy caused sharp rise in infections as is evident.
Next phase change started on 21st April with 12 days of drift. In this phase, contact rate went down to 0.28 (95% CI: +- 0.01) and reach further increased by more than 50%.
Read 13 tweets
6 May
<Update on 6/5> @stellensatz I am finding it increasingly difficult to post updates. Hence, getting a website prepared that will do the job. Hope it will be ready by tomorrow. That will allow me to focus more on discussions.
Many posters have pointed out erroneous predictions done for second wave in early April. I already explained the reasons in India thread. If they do not sound convincing, please pay no attention to our predictions. I am sure there are better things to do!😊
For those, who find some value in our predictions, here are updates. Maharashtra continues its downward journey. Notice that orange curve is fitting better now! It is because I updated the simulation with data up to 5th May. Earlier one was with data up to 24th April.
Read 22 tweets
1 May
@thattai I am glad to see decent language now unlike your earlier posts. I hope you can make this a habit. Your argument, as I understand it, is not that the model went wrong in March, rather that policy makers were misled by it. If yes, your argument is based on flawed premise.
Policy makers do not make decisions based on one input. They collect them from multiple sources. While we did give our feedback to them last month, and it was received graciously, they were skeptical about our predictions. Seemingly they had better inputs. 😊
As for our model, it adopts a very different approach to parameter estimation. An approach that is becoming ubiquitous: use data. If one argues that it needs to be improved, I will readily agree. However, to retire? That is truly bizarre!
Read 6 tweets
23 Apr
I have been asked by many people about details of the SUTRA model. We have a preprint uploaded at arxiv.org/abs/2101.09158. It describes how we compute parameter values and phase changes.
India projections can be found in this thread:
Read 4 tweets
18 Apr
@stellensatz @Ashutos61 @Sandeep_1966 @shekhar_mande Starting a new thread for India. I updated India curve last on 14th with suggested peak at ~190K. Past few days have breached this value massively. This led to a discussion amongst us (me, Prof Sagar, and Gen Kanitkar).
The problem is that parameters of our model for current phase are continuously drifting, and so it is hard to get their value right. We decided to switch to predicting "active" instead of "new" infections. Former is about 10x of latter and hence less prone to fluctuations.
Indeed, it turned out that the trajectories are better matched. See plot below for the entire timeline. Image
Read 29 tweets

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