In case of the coronavirus COVID-19 (or 2019-nCoV), it seems that there are a lot of mild cases which do not require medical intervention, see here:

For every person who is sick enough to come to the attention of public health authorities, there are even more people who have only mild symptoms and never seek medical attention.

I guess that epidemiologists try to establish a good measure the total number of the infected people. The next sentence in the article of the two researchers from Johns Hopkins Bloomberg School of Public Health, however, suggests this is not the case.

Because we do not have a handle on how large that number is, we are not able to include those people in our estimates of mortality.

What are established methods in epidemiology to take into account undercount in the total number of people infected to due missing diagnosis of mild infections? In econometrics, the Tobit model is, for example, widely used. Censoring is an established concept in statistics. How comes that these measures are at least not mentioned in the public media?


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