Thread by @CPHO_Canada

1/6 Surveillance Data Models use actual data to plot how a disease behaves over a certain period of time, in a specific context & set of circumstances to PREDICT what could happen in the short term IF NO CHANGES in context/circumstances. #COVID19

2/6 These models can be inaccurate if the context & circumstances DO change over time. Every change in context & our actions (#PhysicalDistancing, new disease introduction or cluster, etc.) makes the real outcome diverge from the forecast. #COVID19

3/6 To correct for this, surveillance data models are limited to modest, short-term predictions (days) of the trajectory & are regularly updated & re-run as new case data come in, to account for changes & correct the predicted path. #COVID19

4/6 PHAC uses this modelling to track progress using the latest surveillance data. Even if predictions shift rapidly & only look days ahead, we can observe how the actions of Canadians #TogetherApart is working to control #COVID19 spread canada.ca/content/dam/ph…

5/6 Caution & caveats apply, but models show us that our #publichealth practices matter to prevent a worst case scenario [done] & we need to #KeepItUp #TogetherApart to maintain #COVID19 epidemic control to avoid a possible rebound wave.

6/6 If I haven’t lost you yet, read the next thread on how the second type of model, transmission models, are used to answer BIG questions like comparing a worst case scenario vs. intervention options to modify a dire trajectory. #COVID19