Algorithm 1 Virtual individual generation. Takes as input posterior draws of $\alpha ,\beta ,{\sigma }_0,{\sigma }_1$ in Equation (2)

1:procedure VirtualIndividual($\alpha ,\beta ,{\sigma }_0,{\sigma }_1$) 2:    Sample a from a kernel density approximation of empirical distribution using inverse transform sampling 3:    Uniformly sample a set of random $(\alpha ,\beta ,{\sigma }_0,{\sigma }_1)$ values from posterior draw set 4:    Use Equation (2) to draw a value of $logb$ (and hence b) 5:    Use $ed\left(x\right)={10}^{a{(-1+1/(1-x))}^{1/b}}$ to calculate $E{D}_{25}$ ($x=0.25$) and $E{D}_{75}$ ($x=0.75$) for that individual 6:    If $E{D}_{25}$ is less than half the minimum $E{D}_{25}$ in estimates from [22], reject $(a,b)$ and call VirtualIndividual 7:    If $E{D}_{75}$ is greater than 1.5 times the maximum $E{D}_{75}$ in estimates from [22], reject $(a,b)$ and call VirtualIndividual 8:    return $(a,b)$ which characterise a dose-response curve 9:end procedure