Fig. 6.

Schematic diagram illustrates the parametric bootstrap approach for estimating parameter uncertainty (See also Fig. 5). Each bootstrap realization is simulated by assuming a Poisson error structure (or a negative binomial error structure) where the number of new case counts for each simulated dataset is computed using the increment in the number of case counts from time $t_{j-1}$ to $t_j$ (i.e. $F(t_j,\Theta )-F(t_{j-1},\Theta )$) as the Poisson mean for the number of new cases observed in the $t_{j-1}$ to $t_j$ interval (i.e., $\text{Po}(F(t_j,\Theta )-F(t_{j-1},\Theta ))$). A) Cumulative number of case counts. The solid black line corresponds to the known model solution while the red dots correspond to one simulated realization using the bootstrap approach. B) The corresponding number of new case counts (i.e., incidence) for one simulated realization.