for the 2-level bar chart in update 1, using a legend for the bar colors for True and False would allow you to use the age group labels along the horizontal axis. Here is a simple hack of your BarChart that accomplishes this:

BarChart[First /@ visualizeThis, Frame -> True,
 FrameLabel -> {"age", 
   "percent of subgroup sick during given time period"},
 PlotTheme -> "Detailed",
 ChartLabels -> {Rotate[#, Pi/2] & /@ Last /@ visualizeThis, None},
 ChartLegends -> {False, True},
 PlotLabel -> "# of people sick in time range in a given age group"]

update 2 (notebook attached): So I have come across a math-y question about whether I should be calculating the average number of days someone is sick by looking at the average for each run individually, then averaging those... or just averaging the number of days for all individuals across all runs. I believe the correct way is the first approach as is evidenced by the last age group showing the proper mean with huge uncertainty. The question is shown in the attached notebook and the visualizations below shown there is a big consequence for the this decision.

here is averaging each run first, then averaging the averages...

and here is averaging all individuals across all runs in one go

Attachments:

covid-viz.nb

How about this?

data = Table[
   RandomVariate[BetaDistribution[2, RandomReal[]], 100], {4}, {2}];
DistributionChart[data, ChartLegends -> {"False", "True"}, 
 ChartLabels -> {{"0-9", "10-19", "20-29", "30+"}, None}]

I just have a quick question as I think visualizing states over time in each building could be very helpful. I see that your simulation data structure is big enough, but I cannot tell how the "states" vector is associated to person or date. Are consecutive records associated with people by date until all people are exhausted, and then move onto the next date? Or alternatively, are the records to be organized by person and run through all dates until exhausted, and then move to the next person?

I am thinking of line graphs showing spread within buildings and then another graphic showing the impact of certain buildings on wider community spread as a generalization of the "superspreader" concept. Demonstrating a threshold infection rate in a building above which mitigation is the only option, could be a good graphic.

Thank you, and good luck with the study.

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