| Data sources and challenges |
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As the pandemic evolved, the model was refined using data from the epidemic in Italy [34], and as local evidence emerged, Canadian-specific data [35].
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Because of different populations, health care systems, and medical practices, we acknowledge that the use of other jurisdictions' data to inform the model can be misleading but is the only practical solution at the earliest phases of an outbreak.
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Model assumptions and data sources need to be clearly explained, and the model was refined as local data became available.
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For pandemic trajectories, that is, predicted cases over time, initial projections had to be based on observed data from other countries (Italy and South Korea) [36] but were updated to Ontario's expected cases using the integrated Public Health Information System (iPHIS) database as the epidemic in Ontario continued.
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Data availability and quality is a major challenge during an outbreak (e.g., changes in case definitions, testing criteria, reporting, right censoring, especially for clinical data).
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However, modelling for pandemic planning needs to find a balance between perfect data and timeliness of generated evidence from the model.
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| Refining the model |
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As more data becomes available, calibration of uncertain parameters can be conducted (e.g., probability of ward and intensive care unit (ICU) admissions in Ontario).
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Calibration is a technique comparing model outputs with an independent data set (e.g., observed data), also known as calibration targets, to explore variations of an uncertain model parameter to provide a better fit of the model [37].
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Another method to improve model credibility and accuracy is through model validation.
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Some forms of validation include face validity, where experts confirm whether the model reflects their understanding of the disease and patient pathways, and external validity, where model results are compared with actual event data [38].
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