Global Dynamics Emerging from Local Interactions: Agent-Based Modeling for the Life Sciences - Mathematical Concepts and Methods in Modern Biology - page 120

The patch colors showing contamination can be observed during a simulation or in
the online color version of the text, and a grayscale version can be seen in Figure 4.7 .
The model assumes that humans contract the disease through contact with a patch
that has been contaminated with bacteria. The likelihood of contracting disease is
found through the product of the contamination level of the patch and the slider-set
parameter prob-inf . Due to the difference in infectivity of the freshly-shed bacteria,
the probability of infection from contact with a patch that has only the low-infectious
bacteria is found by dividing the product above by 700. The current value for that
slider has been chosen with other initial slider values so that some, but not all, humans
become infected in our baseline experiments. Humans are pictured as red in the
simulation once they are infected. Recovered humans are immune, and are pictured
as white. Since the disease is transmitted through shared contact with patches, we
hypothesize that the amount of time the residents spend in the common space versus in
their individual homes might contribute to the spread of disease, and this is regulated
with the slider time-in-common . We additionally hypothesize that human density
could be a significant factor. Since shrinking the NetLogo grid requires significant
re-coding, to increase density in our experiments we must change the population size.
The number of humans in each household is set by the slider num-in-house . Finally,
we suspect that the amount of individual movement would also change the likelihood
of cross-contamination of patches and thus the number of infections. This parameter
is adjusted using the slider prob-mvmt .
There are several unknown parameters that may have great impact on the dynamics
of a cholera outbreak. The parameters are often not known because the regions of the
world affected by cholera are those with little infrastructure and few resources for
the careful study and recording of outbreak data, but additionally the bacteria mutate
and do not behave in the same way in all outbreaks [ 37 ]. Hence, a key motivation for
an agent-based study of cholera is to gain insight into the significance of the choice
of various ranges of parameter values, and to better understand how daily activities
might contribute to the spread of cholera.
A parameter of debate is the proportion of people who may be infected with
cholera and show no symptoms [ 37 , 41 , 42 ]. Those with asymptomatic infections are
most likely ill for less time than those with symptoms; however, because they are
not suffering, they are moving around more within their population and may have a
greater opportunity to spread the bacteria. Our agent-based simulations will give us an
opportunity to explore under what conditions having a high number of asymptomatic
illnesses might increase or decrease the spread of disease. The model assumes that
that humans who have symptomatic cholera are spending all of their time in the
household, and most of that time near the latrine, as pictured in Figure 4.7 , while those
who have asymptomatic illnesses leave the house along with other villagers during
common times. We will vary the parameter asymptomatic-prob to discern the effect
of asymptomatic disease sufferers. Additionally, while there is varying information
about the length of time a human will be sick with cholera — anywhere from 1-
2 days to 1-2 weeks and set by our slider days-sick , most certainly, asymptomatic