<commit_before>#summary Output Database Population Examples

= Introduction =

On this page, we'll go over a couple of examples for how an output database could look under certain situations.

In all but the first case, the run table and the time_series table have been omitted.  The time_series table simply represents the counts of organisms which exist in a given population.  The run table merely describes the simulation software and identifies specific runs.


= Influenza SIER for two census blocks =

In this example, we'll create a sample output from a SIER model which models influenza for two census blocks.  This model does not discriminate sex or age, only SIER states and location.

So, we have 8 different simulated populations: 1 for each epidemic state (susceptible, infected, exposed, and recovered) in each census block (CB1, CB2).

|| *Population* || *Epidemic State* || *Census Block* ||
|| 1 || susceptible || CB1 ||
|| 2 || infected || CB1 ||
|| 3 || exposed || CB1 ||
|| 4 || recovered || CB1 ||
|| 5 || susceptible || CB2 ||
|| 6 || infected || CB2 ||
|| 7 || exposed || CB2 ||
|| 8 || recovered || CB2 ||

The tables for this set of populations would look like what's shown below. 

== simulated_population ==

|| *id* || *label* ||
|| 1 || susceptible in CB1 ||
|| 2 || infected in CB1 ||
|| 3 || exposed in CB1 ||
|| 4 || recovered in CB1 ||
|| 5 || susceptible in CB2 ||
|| 6 || infected in CB2 ||
|| 7 || exposed in CB2 ||
|| 8 || recovered in CB2 ||

== simulated_population_axis ==

|| *id* || *label* ||
|| 1 || epidemic state ||
|| 2 || location ||

== simulated_population_axis_value ==

|| *population_id* || *axis_id* || *value* ||
|| 1 || 1 || susceptible ||
|| 1 || 2 || CB1 ||
|| 2 || 1 || infected ||
|| 2 || 2 || CB1 ||
|| 3 || 1 || exposed ||
|| 3 || 2 || CB1 ||
|| 4 || 1 || recovered ||
|| 4 || 2 || CB1 ||
|| 5 || 1 || susceptible ||
|| 5 || 2 || CB2 ||
|| 6 || 1 || infected ||
|| 6 || 2 || CB2 ||
|| 7 || 1 || exposed ||
|| 7 || 2 || CB2 ||
|| 8 || 1 || recovered ||
|| 8 || 2 || CB2 ||

== run ==
|| *id* || *label* ||
|| 1 || ACME simulator ||

== time_series ==
|| *run_id* || *population_id* || *time_step* || *pop_count* ||
|| 1 || 1 || 1 || 5000 ||
|| 1 || 2 || 1 || 5000 ||
|| 1 || 3 || 1 || 5000 ||
|| 1 || 4 || 1 || 5000 ||
|| 1 || 5 || 1 || 5000 ||
|| 1 || 6 || 1 || 5000 ||
|| 1 || 7 || 1 || 5000 ||
|| 1 || 8 || 1 || 5000 ||
|| 1 || 1 || 2 || 3500 ||
|| 1 || 2 || 2 || 6500 ||
|| 1 || 3 || 2 || 3500 ||
|| 1 || 4 || 2 || 6500 ||
|| 1 || 5 || 2 || 3000 ||
|| 1 || 6 || 2 || 7000 ||
|| 1 || 7 || 2 || 4000 ||
|| 1 || 8 || 2 || 6000 ||
|| .. || .. || .. || .. ||
|| 1 || 8 || N || 10000<commit_msg>Edited wiki page OutputDatabaseExamples through web user interface.<commit_after>#summary Output Database Population Examples

= Introduction =

On this page, we'll go over a couple of examples for how an output database could look under certain situations.

In all but the first case, the run table and the time_series table have been omitted.  The time_series table simply represents the counts of organisms which exist in a given population.  The run table merely describes the simulation software and identifies specific runs.


= Influenza SIER for two census blocks =

In this example, we'll create a sample output from a SIER model which models influenza for two census blocks.  This model does not discriminate sex or age, only SIER states and location.

So, we have 8 different simulated populations: 1 for each epidemic state (susceptible, infected, exposed, and recovered) in each census block (CB1, CB2).

|| *Population* || *Epidemic State* || *Census Block* ||
|| 1 || susceptible || CB1 ||
|| 2 || infected || CB1 ||
|| 3 || exposed || CB1 ||
|| 4 || recovered || CB1 ||
|| 5 || susceptible || CB2 ||
|| 6 || infected || CB2 ||
|| 7 || exposed || CB2 ||
|| 8 || recovered || CB2 ||

The tables for this set of populations would look like what's shown below. 

== simulated_population ==

|| *id* || *label* ||
|| 1 || susceptible in CB1 ||
|| 2 || infected in CB1 ||
|| 3 || exposed in CB1 ||
|| 4 || recovered in CB1 ||
|| 5 || susceptible in CB2 ||
|| 6 || infected in CB2 ||
|| 7 || exposed in CB2 ||
|| 8 || recovered in CB2 ||

== simulated_population_axis ==

|| *id* || *label* ||
|| 1 || epidemic state ||
|| 2 || location ||

== simulated_population_axis_value ==

|| *population_id* || *axis_id* || *value* ||
|| 1 || 1 || susceptible ||
|| 1 || 2 || CB1 ||
|| 2 || 1 || infected ||
|| 2 || 2 || CB1 ||
|| 3 || 1 || exposed ||
|| 3 || 2 || CB1 ||
|| 4 || 1 || recovered ||
|| 4 || 2 || CB1 ||
|| 5 || 1 || susceptible ||
|| 5 || 2 || CB2 ||
|| 6 || 1 || infected ||
|| 6 || 2 || CB2 ||
|| 7 || 1 || exposed ||
|| 7 || 2 || CB2 ||
|| 8 || 1 || recovered ||
|| 8 || 2 || CB2 ||

== run ==
|| *id* || *label* ||
|| 1 || ACME simulator ||

== time_series ==
|| *run_id* || *population_id* || *time_step* || *pop_count* ||
|| 1 || 1 || 1 || 5000 ||
|| 1 || 2 || 1 || 5000 ||
|| 1 || 3 || 1 || 5000 ||
|| 1 || 4 || 1 || 5000 ||
|| 1 || 5 || 1 || 5000 ||
|| 1 || 6 || 1 || 5000 ||
|| 1 || 7 || 1 || 5000 ||
|| 1 || 8 || 1 || 5000 ||
|| 1 || 1 || 2 || 3500 ||
|| 1 || 2 || 2 || 6500 ||
|| 1 || 3 || 2 || 3500 ||
|| 1 || 4 || 2 || 6500 ||
|| 1 || 5 || 2 || 3000 ||
|| 1 || 6 || 2 || 7000 ||
|| 1 || 7 || 2 || 4000 ||
|| 1 || 8 || 2 || 6000 ||
|| .. || .. || .. || .. ||
|| 1 || 8 || N || 10000 ||
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