The median epidemic size by the end of the simulated period and percentage die-out for each scenario, were the key outcomes captured.
Intervention methods that restrict interaction between medical doctors and their visits to different wards shrink the final epidemic size more than intervention methods that directly protect patients, such as isolating patients in single rooms.
We therefore explore a large volume of high-dimensional parameter space, but seek to relate final epidemic size, risk of spread to Danish islands and potential success of control measures, to early indicators of epidemic dynamics.
Reducing this density to 0.1% largely isolated the trade network, so that inspections reduced the final epidemic size by over 90%, and most epidemics ended without escape into nature.
Therefore the GSCC and GWCC provide lower and upper bounds, respectively, to maximum epidemic size.
This figure represents the empirical distribution of the final epidemic size calculated on the 300 stochastic simulations for each scenario.
We enlarge the framework by focusing on other aspects of an epidemic (such as epidemic size and duration, etc) in addition to the probability of early extinction.
In order to explicitly illustrate our sensitivity analysis approach, let us describe, as an example, the statistical analysis performed on the epidemic size.
One of the important features of R is its potential to provide early and crude predictions of the expected final epidemic size [10].
Additionally, if 10% of the population visiting one of the most popular locations is randomly vaccinated, the epidemic size is reduced by 19%.
Concerning mitigation strategies evaluation, Table 4 shows that the random immunization of 10% of the population (first strategy) reduces the epidemic size by 11.40%, with no substantial gain.
