Why does R_t start so high for COVID in these models?
The site rt.live is a website for tracking the Rt value for COVID-19 for states in the U.S. The Rt value is the average number of people infected by someone who is infected with COVID-19. In almost all cases, the Rt for each state starts high, generally around ~2-3, and then quickly declines to a number closer to 1.
The image below shows the described trend for a handful of states, but the same trend holds for almost every state.
My question is why does the Rt value start so high?
My a priori guess would be that the initial value of 2-3 is the "normal" transmission rate and it went down when states enacted quarantines/restrictions. However that does not seem to hold water as the drop off, in almost every case, occurs very quickly, and often before states first enacted quarantines. My other guess is that the model, early on is just noisy and uncertain due to the limited data, but it shows error bars which aren't that large, and doesn't explain why it's consistently high (and not more evenly distributed between, say, 0-3). I was hoping someone with more knowledge about how these models work or the data fed into them could provide better insight.
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People don't just act in response to official orders. People react to the perception of danger, and by the time various official responses were announced, they were already taking measures to prevent the spread of COVID-19 (some more effective than others).
For example, look at the mobility data for New York: By the time the lockdown started on March 22, cell-phone mobility (a proxy for person-to-person contact) was already down 56%.
This holds just about everywhere. California, one of the earliest states to lock down, was already down 36%. South Dakota, which never had a lockdown order, saw mobility go down 32% at the lowest. Heading over to Europe, by the time the UK announced a total lockdown, mobility was already down 46%. Italy was down 36%. And so on.
Rt starts high because that's the natural infection rate, and drops rapidly as people take measures to avoid being infected.
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