WHAT IS IT? ----------- 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. HOW TO USE IT ------------- Each "tick" represents a week in the time scale of this model. 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. RUNNING THE MODEL ----------------- (1) THINGS TO NOTICE -------------------- 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. EXTENDING THE MODEL ------------------- This model is already an extension of the model "Virus." Look at that model and its information window for additional extension ideas. STARLOGO FEATURES ----------------- 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 individually.