Reason for fall in Jan-Feb is likely combination of acquired immunity from surge in Nov-Dec plus subsequent restrictions. Now restrictions are being lifted and surges are occurring in some places (MI) but not others (CA).pic.twitter.com/vZSKG37Tym
Complex systems, wicked problems. Society, technology, science and more. @UNC professor. @NYTimes columnist. My newsletter is @insight: http://www.theinsight.org
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Reason for fall in Jan-Feb is likely combination of acquired immunity from surge in Nov-Dec plus subsequent restrictions. Now restrictions are being lifted and surges are occurring in some places (MI) but not others (CA).pic.twitter.com/vZSKG37Tym
Whether B.1.1.7 will cause new surge in cases as it rises in frequency will depend on differential advantage over local variants, current transmission rates Rt (which reflect immunity & social interactions). If Rt=0.66 w/out B.1.1.7 & advantage is <50%, Rt<1 & no surge, (cont)
assuming behavioral changes don't increase Rt. But if Rt=0.66 & B.1.1.7 advantage is 75%, then then Rt=1.16 & cases will increase 16% every 6d w/ B.1.1.7 dominant. Is this why MI is increasing & CA decreasing?
Thus, determining reason for differential growth of B.1.1.7 will inform whether additional restrictions will be needed to keep cases in check as it increases in freq or not. This is very important as people are planning for next 3 months.
A big ace up our sleeve is vaccination! If we can quickly vaccinate population before B.1.1.7 increases in frequency, we can push Rt well below 1. Vaccines are very effective against B.1.1.7 so far (i.e. w/out E484K mutation, so I hope we can pull this off!
Obvious lurking concern is whether immune escape variants (e.g. B.1.351) will spread in partly (& later more fully) vaccinated populations. I hope the answer is poorly, but no hard data to know yet.
Clarification: All plots here show the FREQUENCY of B.1.1.7, so they show changes RELATIVE to other variants. Factors that increase Rt (e.g. less restrictions, social behavior) wouldn't affect rate of frequency increase unless they affect the variants differently.
Based on comparing real-world trajectories, one (speculative, yes) question may be whether B.1.1.7 is less susceptible to NPIs than B.1.3.5. So it's probably possible for various factors to differentially affect variant trajectories (and also makes B.1.1.7 such a challenge).
Interesting idea! How were you thinking this would work (other than age-dependent Rt hyp above)? Are you hypothesizing that B.1.351 has a lower R0 than B.1.1.7 (but could have a higher Rt due to more immune evasion) so easier to control with NPIs? Or am I misunderstanding?
Different infectious window: length and/or timing? Earlier/later infectiousness in disease course (later easier to notice/control with NPIs) or narrower/wider duration (latter harder with NPIs). South Africa vs UK/Israel/Europe trajectory is interesting. B.1.1.7 seems terrible.
So, speculatively, that combo could account for re-infection (spiking again in the highly-connected population) and the antibody drop findings of b.1.135 and the South Africa surge/fall trajectory. Meanwhile, note the persistent havoc B.1.1.7 (no immune evasion) seems to wreak.
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