Forecasting airline travel with data pre-covid, during-covid, and 'post'-covid

Hi,

I am trying to forecast airline travel and I have 3 years worth of data (2019-2021), spanning from pre-covid until now. I have read #1416, #1726, and #1595 and I've tried the following, with respect to observing more stable confidence intervals and forecast estimates:
**1) Extending my changepoint_range:** I extended it to 0.9, but my forecasts seem more unstable than the baseline model where I let Prophet auto determine everything.
**2. Specifying changepoint dates:** After taking a look at the auto detected changepoint dates, I included the ones that Prophet detected in addition to more changepoint dates I decided to experiment with after the 80% mark. This reduced my confidence intervals for the forecasts slightly, but I wouldn't say it is satisfactory.
**3. Removal of outliers:** about 1-2% of the data, out of 1000 samples. This slightly reduces the variance.

Let's say I consider the main effects of COVID to be from March 2020 - Sept 2020. My understanding from the previous issues I have read is that we can experiment with adding these dates as a holiday, but my forecasts continue to interpret these dates as a seasonal occurence, for the year 2022, 2023. I do see a more stable pattern in airline travel towards the end of 2020, and I would like the model to favor those trends, rather than the airline travelling that occured pre-Covid.

Is there any advice on how I may produce stable forecast estimates, atleast for the second half of 2021 or 2022? If i do remove a huge chunk of data pre-covid, would it even make sense to provide covid -affected airline data and then post-covid data only? I thought perhaps the model would be over optimistic on the growing trend in airline travel.

I am new to using Prophet, my apologies if I have misunderstood something.

Thank you!