FIGURE 1.

Illustration of data structure at generation g with the incorporation of selection and misclassification. For a person i with the characteristic information(or risk factors) $Z_i=z$ at the beginning of generation g, the true infection status $D_i^{(g)}$ is unobserved, with 1 indicating being infected and 0 being uninfected. The probability for an infected person of developing severe(s)/mild(m)/no(a) symptoms is $t_j(z)$, where $j\in \{s,m,a\}$, which is based on the characteristics. An uninfected person may also develop similar symptoms due to other diseases, for example, influenza, and the probability is $f_j(z)$. Often, people are tested/selected based on symptoms and some other risk factors. $\beta$ and $\alpha$ are the false negative and positive rate for the test