This model simulates the transmission and perpetuation of a 
virus in a rodent population.  It is an extension to the
Star-Logo model "Virus" which demonstrates the same
phenomenon in a human population.  Ecological biologists have
suggested a number of factors which may influence the
survival of a directly transmitted virus within a population.
    (Yorke, et al. "Seasonality and the requirements for
     perpetuation and eradication of viruses in populations."
     Journal of Epidemiology, volume 109, pages 103-123)

The model is initialized with 1500 rodents, of which 10 are
infected.  Rodents move randomly about the screen in one of
three states: healthy and susceptible to infection (green),
sick and infectious (red), and healthy and immune (white). 
People may die of infection or old age.  When the population
dips below the environment's "carrying capacity" (set at 1500
in this model) healthy people may reproduce healthy and
susceptible offspring. 

Some of these factors are summarized below with
an explanation of how each one is treated in this model.    

	The density of the population
Population density affects how often infected and
susceptible individuals come into contact with each other. 
In this model, a maximum population is preset at 1500.  This
is twice as high as the population capacity for the human
population of "Virus."

	Population turnover
As rodents die, some who die will be infected, some
 will be susceptible and some will be immune.  All the new
rodents who are born, replacing those who die, will be 
susceptible.  Rodents die from the virus, the chances of
which are determined by the slider CHANCE-RECOVER.  Rodents
also die of old age.  In this model, rodents die of old age
at approximately 1 year.  Reproduction rate is constant in
this model.  Each turn, every healthy rodent has a
chance to reproduce one offspring.  

	Degree of immunity
If a rodent has been infected and recovered, how 
immune are they to the virus?  We often assume that immunity
lasts a lifetime and is assured, but in some cases immunity 
wears off in time and immunity might not be absolutely
secure.  Nonetheless, in this model, immunity does last
forever and is secure.

	Infectiousness (or transmissibility)
How easily does the virus spread?  Some viruses with 
which we are familiar spread very easily.  Some viruses
spread from the smallest contact every time.  Others
require significant contact, perhaps many times, before the 
virus is transmitted.  In this model, infectiousness is 
determined by the slider INFECTIOUSNESS.

	Duration of infectiousness
How long is a person infected before they either 
recover or die?  This length of time is essentially the
virus's window of opportunity for transmission to new hosts.
In this model, duration of infectiousness is determined by the
slider DURATION.

Each "tick" represents a week in the time scale of this

The INFECTIOUSNESS slider determines how great the chance is
that virus transmission will occur when an infected person
and susceptible person occupy the same patch.  For instance,
when the slider is set to 50, the virus will spread roughly
once every two chance encounters.

The DURATION slider determines the percent of the average
life-span (which is 1500 weeks, or approximately 27 years, 
in this model) that an infected person goes through before
the infection ends in either death or recovery.  Note that
although zero is a slider possibility, it produces an
infection of very short duration (approximately 2 weeks) not
an infection with no duration at all.

The CHANCE-RECOVERY slider controls the likelihood that an 
infection will end in recovery/immunity.  When this slider
is set at zero, for instance, the infection is always deadly.

The SETUP button resets the graphics and plot windows and
randomly distributes 1490 green susceptible rodents and 10 red
infected rodents (of randomly distributed ages).  The GO
button starts the simulation and the plotting function.

Three output monitors show the percent of the population
that is infected, the percent that is immune, and the number
of years that have passed.  The plot window produces a
graph showing (in their respective colors) the number of
susceptible, infected, and immune people.  It also shows the
number of individuals in the total population in blue.


The factors controlled by the three sliders interact to
influence how likely the virus is to thrive in this
population.  Notice that in all cases, these factors must
create a balance in which an adequate number of potential
hosts remain available to the virus and in which the virus
can adequately access those hosts.

As previously stated, this model is very similar to "Virus"
which simulates the same phenomenon in a human population. 
Compare the two models if possible.  Notice that viruses
with very different infectiousness rates can thrive in a
population that has such high turnover.  There is a much
higher supply of susceptible hosts in a population with such
tremendous reproductive capabilities.

This model is already an extension of the model "Virus." 
Look at that model and its information window for additional
extension ideas.

Notice that in order to potentially infect every turtle
sharing its patch, infection is first passed on to the patch
from a sick turtle with the command TSETINFECT? TRUE.  Then,
in the same subroutine, every susceptible turtle occupying
that patch has a random chance to become infected with the
command PSETSICK? TRUE.  

The code was written with this double variable exchange so
that each turtle has a separate chance to become infected by
a sick turtle.  The other option was to write the code so
that on a random chance sick turtles would execute
SETSICK?-AT 0 0 TRUE, but this would change every neighbor
turtle at once instead of evaluating each neighbor's chances