@stellensatz@shekhar_mande@Ashutos61@Sandeep_1966 Covid infections are picking up in India again. Interestingly, only a few states are contributing to this spike. Why is this happening? Our model SUTRA provides some clues. 1/n
First, let us look at Maharashtra. In the picture, blue curve is recorded daily new infections (averaged over a window of seven days) and orange curve is prediction of the model. According to the model, the latest spike is due to significant increase in contact rate. 2/n
Contact rate represents average number of people infected by an infected person in one day. For Maharashtra, it went from 0.3 to 0.5. The peak should arrive within two weeks. 3/n
Second state we analyze is Punjab. It has a peak due in March-end at around 2K infections. There are two causes for this: one, contact rate increased from 0.3 to 0.4, and two, reach increased by about 20%. 4/n
Reach parameter measures fraction of population under coverage of pandemic. This parameter is monotonically increasing. Once it stabilizes for a long period, we can assume the reach to be close to 100%. 5/n
Major states where reach has stabilized are UP and Bihar. UP in fact has all parameters stable since November. That is why there is no spike in UP. 6/n
Bihar has a similar story. Reach is stable since October. Contact rate is fluctuating a bit, but not enough to create a spike. 7/n
Looking at India, we see a gentle increase until early April followed by equally gentle decline. Reach for India is almost stable since September - it has increased by less than 7% since. The current increase is caused by increase in contact rate to 0.3 from 0.22. 8/n
Above shows there will be no "second wave" in India. The states should be able to tide over the spikes without much problems. 9/9
• • •
Missing some Tweet in this thread? You can try to
force a refresh
@stellensatz I am back with Covid-19 predictions, when it already seems to be peaking! Reason for being so late is that the model was unable to capture the trajectory due the numbers being very small (5-6K per new for India is nothing).
Once the numbers crossed 10K per day, the model did capture the trajectory somewhat. By 15th April, this is what it predicted:
The predicted peak was around 50K new cases per day somewhere in mid-May. I made a couple of media comments also based on this. However, before I could post the predictions on twitter, the trajectory changed again.
@stellensatz@GyanCMehta The rapid spread of Omicron in China over the past one month has raised several questions: 1) Why is it happening in China now after such a long time despite vaccination? [1/18]
2) Numbers in some countries are also rising. Is it likely to spread to other countries too? 3) Should we be concerned in India?
I recently did a simulation of spread in China and a few other countries using SUTRA. Here are the conclusions. [2/18]
1) The new infections plot for China since March this year when a major outbreak happened. [3/18]
Indian trajectory is rising faster than projected earlier. We now have enough data to start doing preliminary predictions (as opposed to projections).
Maximum data is from Mumbai. Phase plot still showing a drift indicating that parameter values are likely to change.
Current parameter values suggest a peak around 15th Jan. Need to wait for a few more days for parameters to stabilize (of course, if lockdowns are imposed, it will change the parameters again).
. With data up to 16th Dec, SUTRA prediction of trajectory matches well so far.
There seems to be broad agreement that Omicron arrived in October in SA. If true, our earlier assumption that Omicron arrived in August causing a jump in contact rate is incorrect. So how did Omicron change parameter values?
A new phase started in SA from November 1 and stabilized after three weeks. Value of contact rate (beta) increased from 1 to 1.35 and reach (rho) increased from 0.86 to 1.04.
@stellensatz More information on omicron variant. First, a more careful SUTRA simulation shows that contact rate (beta) jumped to 1 by Aug-end. It strongly suggests presence of a new mutant that was active by Aug.
Note that the initially numbers would be very small and so genome sequencing may not throw up any case, especially if (as has been reported) most of the cases are mild and thus may go unreported. So it is not a surprise that first case was reported in November.
Why was the initial spread slow given that it is a highly infectious mutant?
Reach of the pandemic from July until now is around 85% (this is computed using the serosurvey that showed ~47% seropositivity in May '21). Calibrated model shows that natural immunity in Sep was ~77%.
). It makes many important points regarding epidemiological modeling. In this thread, I am going to develop his analysis further and show what is the purpose of SUTRA model.
A key point made is that epidemiological models stratify population on age basis since interactions vary widely with age. Further stratification is done between mild and seriously ill cases. Former is useful to make policy decisions about controlling the spread.
Latter is useful in predicting hospital beds requirements. These advantages, however, come with some complications. More compartments mean more interactions, which mean more parameters to estimate. Epidemiologists use mobility, social structures etc to estimate their values.