Biology Reference
In-Depth Information
Exercise 4.40.
Save the NetLogo cholera file with a new name, and modify the
code so that individuals with symptomatic cholera are only confined to the home, and
not constantly heading for the latrine. Rerun the baseline experiment with the new
behavior. Does the in-home behavior of symptomatic individuals affect the outcome
measures?
Exercise 4.41.
Save the NetLogo cholera file with a new name, and modify the
code so that the Purple household always begins with one turtle who is infected
and symptomatic. Run the baseline experiment twice: once with the environmental-
reservoir at 50 and once with the reservoir at 0. Compare to the original baseline, and
discuss the results.
Exercise 4.42.
Modify the assumptions for cleaning so that individuals clean where
ever they roam, and not just inside the home. Does this change make any difference
to the conclusions in Exercises
4.38
and
4.39
?
There are a number of complications you could add to the model for a much
more in-depth project, such as increasing the number of households or including
genders and ages within the households, with appropriate activities such as markets
and schools. These would be appropriate to consider as part of a long-time-frame
project that compares results with those already discovered within the exercises.
4.4
USE AND DESCRIPTION OF ABM IN RESEARCH:
TICK-BORNE DISEASE AGENT-BASED MODELS
ABMs provide a powerful tool for research at all levels including undergraduates. As
part of a research project focused on understanding the dynamics of ehrlichiosis, a
human tick-borne diseases, an ABMwas created and published [
29
]. This model was
used as the basis for building a more complex ABM to explore another tick-borne
disease, Rocky Mountain spotted fever, by an undergraduate student as part of a
summer research program working with Gaff [
43
]. Here we provide a basic overview
of these two models as an example of the use of ABM in a research-focused project
as well as how ABM can be reported for publication.
Vector-borne diseases are spread through ticks, mosquitoes, and other arthropods.
In the US, tick-borne diseases are the most common arthropod-borne disease and have
been for many years [
44
,
45
]. Risk of tick-borne disease, e.g., Lyme disease (
Borrelia
burgdorferi
), Rocky Mountain spotted fever (
Rickettsia rickettsii
), Tidewater spotted
fever (
Rickettsia parkeri
), and ehrlichiosis (
Ehrlichia chaffeensis
), is a combination
of expected frequency of encounter with a competent vector and the prevalence of
disease within that vector population. Predicting either of these factors is a task well
suited for mathematical modeling and simulation.
In order to understand many of these diseases, we must come to understand the
underlying dynamics of the tick populations themselves [
46
]. Unlike other arthropod
vectors, ticks have a unique life history that significantly impacts the prediction and
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