Abstract：
:Computer models have proven to be useful tools in studying epidemic disease in human populations. Such models are being used by a broader base of researchers, and it has become more important to ensure that descriptions of model construction and data analyses are clear and communicate important features of model structure. Papers describing computer models of infectious disease often lack a clear description of how the data are aggregated and whether or not non-epidemic runs are excluded from analyses. Given that there is no concrete quantitative definition of what constitutes an epidemic within the public health literature, each modeler must decide on a strategy for identifying epidemics during simulation runs. Here, an SEIR model was used to test the effects of how varying the cutoff for considering a run an epidemic changes potential interpretations of simulation outcomes. Varying the cutoff from 0% to 15% of the model population ever infected with the illness generated significant differences in numbers of dead and timing variables. These results are important for those who use models to form public health policy, in which questions of timing or implementation of interventions might be answered using findings from computer simulation models.
journal_name
Epidemicsjournal_title
Epidemicsauthors
Orbann C,Sattenspiel L,Miller E,Dimka Jdoi
10.1016/j.epidem.2016.12.001subject
Has Abstractpub_date
2017-06-01 00:00:00pages
24-32eissn
1755-4365issn
1878-0067pii
S1755-4365(16)30062-7journal_volume
19pub_type
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