Agent-Based Modeling: A Tool for Simulating Impacts of Education on Public Health
Outcomes
Authors: Pawan Nihure1 and Robyn Mae Paul2
Affiliations: University of Calgary, 1 Biomedical Engineering and 2 Sustainable Systems Engineering
Introduction
Agent-based modeling (ABM) has emerged
as a powerful tool in biomedical engineering
for simulating complex, dynamic systems
where individual behaviors influence
population-level outcomes [1]. In the field
of public health, ABM allows researchers to
explore how variations in individual
behaviors, healthcare access, and education
can impact disease spread and control [2].
This study demonstrates the application of
ABM to simulate the spread of sexually
transmitted diseases (STDs) based on
differing
sex
education
strategies
(comprehensive, basic, and abstinence-only)
within
various
socio-environmental
contexts. By modeling urban and rural
settings, along with random and communal
interaction patterns, we illustrate how ABM
can inform public health interventions and
guide policy decisions in education and
healthcare.
Methods
A NetLogo-based ABM was developed to
simulate STDs spreading through a
population of 500 individuals over a 5-year
period, categorized by their sex education
background: comprehensive sex education,
basic sex education, or abstinence-only. The
model accounted for both urban (36-day
recovery) and rural (54-day recovery)
environments [3], and included two
interaction types: random and communal.
Each individual agent could become
infected with an STD based on interaction
dynamics, and their infection, recovery, or
re-infection was tracked over a 5-year
period. The 16 different simulation set-ups
varied the proportion of education types in
different settings to assess the impact of
each educational strategy on infection rates,
recovery rates, and re-infections.
Results
This early work-in-progress paper presents
preliminary results, which reviewed and
analyzed the 16 different simulation
parameters. Comprehensive sex education
consistently led to the lowest infection rates
across all settings, as low as 0.7%, whereas
basic sex education ranged between 4-5%,
and abstinence only education rose as high
as 5.5%. Recovery rates in comprehensive
sex education settings consistency were
around 70%, whereas basic sex education
and abstinence-only education showed
between 45-55% recovery rates. Most
notably,
re-infection
rates
for
comprehensive sex education remained
close to zero through all simulations,
whereas in rural abstinence-only settings it
climbed nearly 78%, demonstrating that
while initial infections may be controlled,
ongoing educational efforts are critical to
prevent recurring infections.
Conclusion
This study illustrates the versatility of
Agent-Based Modeling in biomedical
engineering, particularly in simulating how
behavioral, social, and educational factors
contribute to disease spread. ABM offers a
dynamic approach that traditional statistical
models might not capture for assessing the
effectiveness of health interventions and
educational policies. By incorporating
realistic social dynamics, ABM can guide
the development of targeted public health
strategies,
especially
in
vulnerable
communities. Future work aims to further
validate the model and perform additional
statistical analysis on the results from the
ABM.
References:
[1] Brennan RW, Nelson N, Paul R. Estimating the
effect of timetabling decisions on the spread of
SARS-CoV-2 in medium-to-large engineering
schools in Canada: an agent-based modelling study.
CMAJ Open, 2021.
[2] Badham, J., Chattoe-Brown, E., Gilbert, N.,
Chalabi, Z., Kee, F., & Hunter, R. F. (2018).
Developing agent-based models of complex health
behaviour. Health & Place, 2018.
[3] Jenkins, W. D., Williams, L. D., & Pearson, W. S.
(2021). Sexually Transmitted Infection Epidemiology
and Care in Rural Areas: A Narrative review.
Sexually Transmitted Diseases.