README

                PEDS
Place based, event driven simulation model
      1) relatively fine grained: people & places
      (offices, elevators, apartments, subways,
      buses, streets, parks, playgrounds, PPE, ...)
      2) graphical model
         Nodes: persons & places
         Paths: person visits places
      3) viral density field (VDF)
               diffusion and ventilation
      4) exposure (exponentially weighted sum over
      path of VDF) causes 
      5) step ~ 6 mins

Disease transmision:
 1) asymptomatic 
 2) symptomatic
 3) probability distribution:
        exposure(\int vdf): -> infection
        a) probability of infection after spending
           NSTEPS steps with an infected person
           (VDF = 1)  is 1.
 
Simple model:

1)Infected person produces  some (default=(1/NSTEPS)/step units)
with VDF 
2)Probability of infection ∝ exponential trailing average of VDF

A population is a composite with leaves of people
A business district is a composite with leaf nodes of various types

In  uses of composites, different node types may be handled differently.


Data Structures:
     1) population: network: nodes are composites or leaf.
                    ops set number of person nodes.
                            number of space nodes.
                    ops persons and space nodes
                        setInfectionState
                        showInfectionState
                        showPaths
                        simulate path traversal
                        iterative path traversal.

Approximate viral density in various, interconnected spaces. A graphical model (nodes & paths).
Nodes may be 'person' (or spatial nodes (subway, elevator,office..). Each path begins
and ends at one of the person node.  Spatial nodes have characteristics relevant(?) to
transmission.  Characteristics of the spatial nodes represent social distancing rules.

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