To investigate the influence of the negative binomial cusum settings on outbreak detection performance, we also evaluated the performance of the negative binomial cusum when the out of control state was defined as 3 standard deviations greater than the mean.
Infectious disease outbreak detection performance varies with incubation period, baseline counts and outbreak magnitude.
This approach allows sensitivity analyses to be conducted and linked to outbreak detection performance.
Nevertheless, further research into the outbreak detection performance of these syndromes would be worthwhile.
Investigating the actual outbreak detection performance of the syndromes in this study would also be worthwhile.
These issues prompted us that epidemic features of infectious disease may affect outbreak detection performance.
Thus, there are no constraints on the outbreak detection performance measures that can be applied to the model outputs.
Thus, in this outbreak, the performance of WGS sequence was equivalent to conventional SBT in differentiating the outbreak from the non-outbreak strains.
Several studies have described the determinants of outbreak detection performance, including: system factors (representativeness, outbreak detection algorithms and algorithm-specifics), outbreak characteristics (outbreak size, shape of the outbreak signal and time of the outbreak) [ 5- 8].
To compare the outbreak detection performance of the expectation-variance model with the traditional models, receiver-operator (ROC) curves were constructed for all models.
