This thread summarizes key findings of our report….

What is the UP Model? At its heart is the decision not to shut down the economy during the second wave. This helps weaker sections of society as they are the worst sufferers of a lockdown.
An additional benefit is that the returning migrant workers can find employment more easily. The downside is also substantial. The pandemic may get totally out of control since people are moving about and interacting. This would lead to a collapse of health infrastructure.
This makes controlling the pandemic so it does not go out of control of utmost importance. This has to be supplemented with a rapid expansion of health infrastructure to take care of increased patient load.
Our report took data from multiple sources & analyzed them to conclude how successful was the model. At several places, SUTRA model has been used for analysis purposes. For those interested, a significantly updated version of our paper on the model is at
First the economy. More than 2.6 lakh NOCs were issued to new enterprises during the pandemic period in the state. Large scale skill mapping was undertaken for migrant workers and shared with potential employers. These and other steps significantly impacted unemployment rates.
As the plot shows, unemployment rate in the state came down from ~10% in March 2020 to ~4% in June 2021! A sharp contrast with pan-India and states of Bihar and Delhi plotted here (source: CMIE Prowess Unemployment Database).
To support weaker sections, about 39 lakh metric tonnes of free ration was distributed to them during the second wave. This translates to more than 100 kg per family. Besides, a cash transfer of ~3K per family was done.
Healthcare manpower was significantly augmented by training ASHA and Anganwadi workers. The workers were provided special medical insurance and incentives to keep their motivation high. Innovative methods were used to expand the availability of Covid-beds to almost 80,000.
This met the requirements except for a brief period around the peak.

Oxygen became a very precious resource during the second wave. In about 12 days in second half of April, the demand nearly tripled, going way past the generation capacity of the state.
Plenty of Oxygen was available in plants near east or west coast, the challenge was how to transport it quickly to the state because Oxygen tankers move very slowly on road.
Again, some out-of-box thinking helped. Empty tankers were airlifted by IAF transport planes to generation plants and filled tankers were loaded on special trains to bring them back quickly (they were too heavy to be brought by planes).
A centralized dashboard live tracked all the tankers. In addition, to rationalize the consumption, @IITKanpur was roped in to create a real-time Oxygen audit system for hospitals in the state. It resulted in savings of 30+ MT per day.
These steps resulted in supply outstripping demand within 2 weeks as shown in the plot below.
Finally, for the most critical aspect of controlling the pandemic, a number of strategies were deployed. Every village had monitoring committee that was given a basic medication kit to be given to anyone suspected of being ill.
Besides, house-to-house visits were conducted to identify possible cases. A clear home isolation protocol was put in place for mild cases. In cities, micro-containment zones were dynamically created to limit the outbreak without effecting economic activity much.
Restrictions were put during weekends and nights on movement. These measures brought down contact rate from 0.6 to 0.3. The reduced contact rate was roughly the same as achieved by most states with a strict lockdown!
The reduction in contact rate caused the peak of infections to also reduce by a factor of two as shown in the plot below. The red and orange curves have been computed using SUTRA model.
The timing of the restriction steps is also crucial. Could an earlier imposition have led to further reduction? Our analysis shows no. In addition, it shows that even a slight delay would have resulted in large increase!
We compared the timing of measures in UP with many other states. In the plot below, states within green and red circles got the timing wrong: green were too early and red were too late.
Note that some states occur twice with postfix M and S. They first imposed Mild restrictions and later Strict Lockdowns. Only five states got the timing about right: UP, Assam (M), Telangana (M), Gujarat, and Maharashtra (M). Among these, UP was spot on!
Another critical component of control was testing. There are two broad strategies for testing. First is targeted testing focusing on symptomatic cases. Second is chasing the pandemic by testing extensively.
To identify the strategy followed by a state, test positivity rate (TPR) is used often. However, there is a better method: to use normalized test positivity rate (NTPR). It is defined to be the ratio of TPR and percentage of active cases.
For targeted testing, NTPR will be significantly more than one (finding many more cases than a random test would do) and when chasing the pandemic, NTPR will be significantly less than one (finding much fewer cases that a random test would do).
SUTRA model allows one to estimate percentage of active cases at any time, thus NTPR can be computed. Plot below shows the progression of NTPR for UP (size of circles denotes NTPR value at the time). As is clearly evident, UP has consistently chased the pandemic!
In contrast, Maharashtra has consistently followed targeted testing as evident from the plot below.
Kerala has an interesting mix. It chased the pandemic last year, but has shifted to targeted testing during second wave.
The chasing the pandemic strategy contributed towards not allowing uncontrolled growth in the number of cases.
Above were salient points of the UP Model and our analysis of it. Despite all these steps, there was a period of about two weeks in April-May when cases were high, there was Oxygen shortage, and there were many deaths. Could this have been avoided?
There is always possibility of improvement, especially in hindsight. At the same time, it needs to be borne in mind that delta is an extremely virulent mutant. It has caused chaos wherever it has gone.
Some countries got away with less damage due to high vaccination (e.g. UK), while some others suffered majorly (e.g. US). This is despite the fact that they had time to prepare for delta and had significantly more resources too.
There have been reports about significantly more deaths in UP (and elsewhere) than recorded. Nearly all of them are either anecdotal or use partial data. One needs CRS data for 2021, compare growth from 2020 against the trend of past decade to estimate excess deaths.
This exercise needs to wait until early next year when 2021 data will become available. Growth from 2019 to 2020 in UP was ~3% which is about same as trend since 2011. This implies that at least in the first wave, number of Covid-deaths were not significantly more than reported.

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More from @agrawalmanindra

27 Aug
@stellensatz A thread on testing strategies. They can be classified in three broad types: 1) random testing, 2) targeted testing, and 3) infection chasing. Targeted testing is biased towards those more likely to be infected. For example, testing only symptomatic people.
Infection chasing is biased towards those less likely to be infected. For example, testing everyone in a locality if one positive case is found. This is the strategy recommended by WHO. So how does one find out which strategy is a state following?
The answer is already above: divide test positivity ratio (TPR) by the percentage of active cases. TPR will be close to percentage of active cases in case of random testing, significantly above in case of targeted testing, and significantly below in case of infection chasing.
Read 15 tweets
11 Jul
@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!
Read 27 tweets
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

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