This is a very good write up, where he goes through how he develops the estimates for real infections, deaths, etc. with the available data. He's had the highest quality model in this whole thing, so it is very interesting to read through his approach.
https://covid19-projections.com/estimating-true-infections/
Some sections that people will find interesting here.
https://covid19-projections.com/estimating-true-infections/
Some sections that people will find interesting here.
Quote:
Lower IIFR Over Time
The IIFR in the US decreased from over 1% in March to 0.25% in July. Below, we present a few explanations to why the IIFR in the US has decreased significantly since March/April.The above are explanations that would explain a true decrease in IFR. We believe the lower median age of infection and better protection of high-risk populations are the primary drivers behind the decrease in IIFR. Below are some reasons that could skew the IIFR lower, but not change the true IFR:
- Lower median age of infection (see section above)
- Better protection of vulnerable populations (nearly half of COVID-19 deaths in March/April were in care homes)
- Improved treatment (new drugs, better allocation of resources, more experience among staff, etc)
- Earlier detection
- More comprehensive reporting of confirmed cases
- Changes in the distribution of age groups tested (e.g. more younger people getting tested would skew IIFR down)
- Inflation of the test positivity rate (e.g. double-counting positives, not reporting negatives, etc)
- Longer lag in death reporting
- Underreporting of deaths
Quote:
Herd Immunity Threshold
Looking at the data, we see that transmissions in many severely-impacted states began to slow down in July, despite no clear policy interventions. This is especially notable in states like Arizona, Florida, and Texas. While we believe that changes in human behavior and changes in policy (such as mask mandates and closing of bars/nightclubs) certainly contributed to the decrease in transmission, it seems unlikely that these were the primary drivers behind the decrease. We believe that many regions obtained a certain degree of herd immunity after reaching 10-30% prevalence.
A widely-accepted method to calculate the herd immunity threshold (HIT) is to use the basic reproduction number, R0:
HIT = 1 - 1/R0
. Back in March/April, we estimate R0 in the US to be around 2.3. This corresponds to a HIT of
1-1/2.3 = ~0.6
, or 60%. But the effective reproduction number, Rt, has decreased dramatically since then due to a variety of reasons such as greater population awareness, mask-wearing, reduced larger gatherings, and implementation of social distancing guidelines. The Rt in most regions around the US where there are outbreaks is now between 1.1-1.6. This corresponds to an effective HIT of 10-35%. As a result, it makes intuitive sense that we are seeing a decline in transmission after those regions reach a 10-35% prevalence.
One thing to note is that original definition of the herd immunity threshold is derived from the basic reproduction number, R0, and assumes no intervention/social distancing. Hence, by definition, the HIT of the SARS-CoV-2 virus remains unchanged over time, between 50-80%. But the effective HIT does change over time as the effective reproduction number, Rt, decreases due to society adjusting to the virus. That's why we are seeing an effective HIT of 10-35%.
Also note that reaching the herd immunity threshold does not stop transmission - it simply slows down further transmission.