@nathan yes the data structure is a bit strange to avoid data redundancy since the files will get very large eventually. I am attaching a much simpler starter data set with some code to slice it in one way. I had not even thought about the states in a given building over time, that is bloody brilliant! I really appreciate the input! Each row in the "states" matrix is one person in the "people" vector. For a given row in the "states" matrix we are looking at the states over time of a single individual. Each entry in an individual's states over time corresponds to the "dates" vector.

Attachments:

simple-starter.nb

Hi all, we made some press on simplifying the data format that should make things simpler, the notebook is attached with a very clean output from one sort of real simulation run.

Attachments:

simple-starter.nb

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.

update 3 (major revision): I improved the data structure to make it more closely tied to the result database. I also generalized pipeline a lot. I am still working on recreating the earlier visualizations in this new workflow.

Attachments:

covid-apis.nb

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 1 (notebook attached): I have my first visualization, although it needs some better generalization into a reusable function for numerical demographics. I also need some better styling guidance.But a good night's progress on a very sizable (and imo important) task. Hope to see some others able to engage here, I know my prompt might be too abstract or vague, ask questions if you want to.

Attachments:

covid-viz.nb