Today I realized that almost every epidemiologist has made a great error regarding R and herd immunity.
Let’s say R0=2.5. Then it’s a simple thought error to assume that 60% needs to be infected for herd immunity. However here is the error, populations are not uniform. For example R in NYC is gonna be higher than in Alaska. Let’s assume R=4 in NYC and 2 in Alaska, then herd immunity should be at 75% in NYC and 50% in Alaska. Wrong, populations are not uniform. Some people will interact with more people than other. For example young people vs retired people, bus drivers vs truck drivers, kindergarten teachers vs online English teachers etc. If in one city 10% of the people do 50% of the interacting then these people will get infected earlier in average and once they have immunity the city will get their R cut in half.
So the conclusion of this is that R will be higher early in the spread when the high interaction individuals get infected. Then it will naturally fall even with pretty low level of immunity.
Let’s say R0=2.5. Then it’s a simple thought error to assume that 60% needs to be infected for herd immunity. However here is the error, populations are not uniform. For example R in NYC is gonna be higher than in Alaska. Let’s assume R=4 in NYC and 2 in Alaska, then herd immunity should be at 75% in NYC and 50% in Alaska. Wrong, populations are not uniform. Some people will interact with more people than other. For example young people vs retired people, bus drivers vs truck drivers, kindergarten teachers vs online English teachers etc. If in one city 10% of the people do 50% of the interacting then these people will get infected earlier in average and once they have immunity the city will get their R cut in half.
So the conclusion of this is that R will be higher early in the spread when the high interaction individuals get infected. Then it will naturally fall even with pretty low level of immunity.