Novel Application of Agent-Based Modeling in Evaluating Global Health
Programmes
Submitted to: International Journal of Epidemiology, December 2011
Sheree A. Pagsuyoin1, Gerard P. Learmonth2, James A. Smith2, Jeffrey B. Demarest3, Jonathan
E. Mellor2, Garrick E. Louis2, Jane R. Boissevain4, Nisha D. Botchwey5, Karen E. Firehock5,
Richard L. Guerrant4, Vhonani Netshandama6, Amidou Samie6, Pascal Bessong6, and Rebecca
A. Dillingham4.
1
Department of Civil and Environmental Engineering, University of Waterloo, ON, Canada
School of Engineering and Applied Sciences, University of Virginia, Charlottesville, VA 22903
3
Department of Systems Engineering, United States Military Academy, West Point, NY 10996
4
School of Medicine, University of Virginia, Charlottesville, VA 22903
5
School of Architecture, University of Virginia, Charlottesville, VA 22903
6
School of Mathematical and Natural Sciences, University of Venda, Thohoyandou, South Africa
2
Address correspondence to: Gerard P. Learmonth, Department of Systems and Information
Engineering, School of Engineering and Applied Sciences, University of Virginia,
Charlottesville, VA 22903, USA. Telephone 1-434-982-2100. E-mail: jl5c@virginia.edu
Short Title: Use of Agent-based Modeling in Health Programme Evaluation
Keywords: agent-based modeling, public health, child mortality, intervention programmes,
water quality, diarrhea
Acknowledgement / Funding Declaration We wish to acknowledge the support of the ARRA
Framework Programs for Global Health Signature Innovations Initiative (R24 TW008798-01).
We would also like to express our gratitude to the University of Virginia and University of
Venda students and the community members who assisted in the data collection which informed
the model described. Finally, we are grateful to all the community members who have welcomed
us and allowed us to develop the agent-based model described.
The authors declare they have no actual or potential competing financial interests.
Abstract
Background: Impact evaluation of environmental and health programmes in developing
economies enables identification of factors influencing programme efficiency and helps
formulate strategies that optimize the benefits of these programmes. Most assessment
methodologies employed in the past were based on statistical data analysis of various key health
indicators endorsed by the global health community. In this paper, we explore the potential
application of agent-based modeling (ABM) as a tool for the holistic evaluation of the collective
impact of development projects on public health.
Discussion: ABM is a widely accepted and utilized method for analyzing complex ecological,
environmental, and social systems. It has been applied extensively in studies across disciplines
including policy evaluation, disease transmission, and water balance analysis. In this research,
we underscore the novel use of ABM in simulating typical scenarios in local communities where
simultaneously on-going development projects affect the over-all public health status. An
illustrative ABM framework is also developed to describe the complex relationships among
water, sanitation and human health.
Conclusion: The use of ABM in evaluating the impacts of development and health projects can
be more resource-efficient than implementing field or observational trials. However, agentbased models designed for integrated impact evaluation are subject to calibration and complexity
limitations. Well-designed hybrid systems can circumvent these limitations and provide a crossplatform means for model validation. Over-all, there is a strong potential for the application of
agent-based models as a an inexpensive but effective tool for performing impact evaluations of
development and health projects in developing country settings.
Introduction
With only four years remaining before the 2015 deadline, many researchers have raised concerns
on whether some critical targets of the Millennium Development Goals (MDGs) will be met (13). While significant improvement has been achieved in the areas of literacy, water access, and
poverty reduction, current progress in child mortality reduction, sanitation access, and maternal
health indicates that the 2015 targets will not be attained (4-6). In low and mid-low income
regions, the gap in progress between rural and urban communities remains quite wide (1).
Likewise, for most key health indicators, while the global averages suggest an optimistic trend,
national averages in low-income countries are lower, particularly in South Asia and Sub-Saharan
Africa (1). Not surprisingly, the majority of international aid funds have been channeled to these
two regions (7-9). In 2006, the Sub-Saharan Africa and South Asia regions received 60% (US$
3.4B) and 13% (US$ 0.75B), respectively, of the total country-specific (i.e., traceable to recipient
countries) development assistance for health (DAH) funds (8). The Sub-Saharan region also
accounted for 37% of global official development assistance in 2008, up by 7% from its 1999
allocation (10).
Projects supported by international aid funds can be categorized into three general programme
areas: development (e.g., infrastructure, agriculture), health, and institutional strengthening (e.g.,
capacity building). In reality, however, many of the individual projects are interlinked and
precipitate some impact on the other (11). For example, providing education (capacity building)
to women can reduce the risk of their children dying before age five (health) (10). Likewise,
improving access to clean water and sanitation facilities (development) can reduce the burden of
diseases such as diarrhea (health). Typically, at the community level, on-going projects support
all three programme areas, and the over-all impact on the community can be a collective result of
these projects.
Despite significant investments in many development projects globally, impacts have been
minimal in some areas (1, 8). The global economic and financial crisis that started in 2008 has
exerted an even greater challenge to the progress made towards the MDGs. It is projected that
the adverse impacts of the crisis on human development, particularly in health and education,
will last beyond 2015 and cannot be overcome by a strong economic recovery (10). Moreover,
slower recovery can have greater long-term impacts on health goals that in turn affect other goals
(e.g., malnutrition on child and maternal mortality). Averting these grim scenarios requires a reevaluation of existing policies and establishing robust policies that can compensate for slower
economic and development growth.
At present, constraints on time and funding are most critical to efforts in achieving key
development targets of the MDGs. In view of the imminent 2015 target deadline and the
challenges posed by the current economic downturn, it is necessary to develop innovative
strategies for evaluating the impacts of development projects in order to identify appropriate
interventions that optimize benefits to communities. The need for robust project evaluation tools
is urgent.
Such tools can be an alternative or complementary to existing evaluation
methodologies.
In this paper, we explore an innovative application of agent-based modeling (ABM) as a tool for
evaluating the cumulative impact on health of concurrent development and health programs. We
also demonstrate the development of an ABM framework for describing the complex
relationship of water and sanitation to human health.
Global Health Perspectives
The global burden of inadequate hygiene, access to clean water, and sanitation remains
persistently high and is evident in alarming statistics on morbidity and mortality (12-14).
Despite an over-all global declining trend in the last two decades, child mortality remains high. It
is also expected to fail the 2015 benchmark of the United Nations MDG. It is also becoming
increasingly concentrated in a few developing countries (e.g., India and Nigeria) even as
improvements are steadily observed in more developed nations (6).
Perennial water-related diseases continue to be at the core of health issues among children. In
latest estimates, diseases such as diarrhea and malaria are still among the most prevalent, with a
combined under-five mortality of over 5,000 per day (12). Naturally, interventions designed to
combat these health problems have focused on expanding access to clean water and sanitation
(15). In addition, point-of-use water treatment, hygiene education, and medication (vaccine, oral
rehydration therapies) have increasingly been made available to developing countries, leading to
marked decline in child mortality and morbidity (16-20).
Globally, international aid agencies and governments disburse billions of dollars to fund various
projects that seek to improve public health (7-8, 21). These projects range from those that
directly impact health such as vaccine research and health care delivery, to those that indirectly
result in some health improvements such as education and capacity building (22-24). Despite
these substantial investments, reported efficacies of health intervention projects have been highly
variable (19, 25-26), and a more systematic and integrated analysis of available data has been
recommended (27).
Thus far, the progress made towards achieving the health targets of the MDGs is encouraging;
but more effort is required to inch closer to the missed targets. During the current economic
crisis, a more holistic approach is necessary to address these health challenges. This entails
integration of global efforts ranging from financial regulation, governance, and managing health
delivery systems (10).
Analytical Methods for Impact Evaluation
Project evaluations are carried out for a variety of reasons: improve an existing project, expand
an initiative, assess applicability in other settings, or evaluate alternatives (28). Based on the
objective, the assessment can occur prior to, midway through, or post project implementation. In
turn, the preferred method of assessment is influenced by these timelines. Ex-ante evaluations,
often referred to as impact assessments, have a semi-qualitative structure, with the perceived
project impacts often compiled in matrix form. The matrix generation can be very subjective and
rely heavily on the experience of the evaluator. However, the perceived impacts can also be
supported by simulation, for example, dispersion modeling of stack emissions from a planned
manufacturing plant.
Mid and post completion evaluations rely on actual data collected during or after project
implementation. For health and development projects, these types of evaluations usually involve
statistical data analysis of factors affecting the key health indicators endorsed by the global
health community. Among the more commonly used indicators are mortality and morbidity
rates, reduction in diseases, and malnutrition (16, 18-19, 29).
Impact evaluation provides both quantitative and qualitative measures of the effect of human
development programmes on the quality of life. It is an important component of the project life
cycle because it highlights both the weaknesses and strengths of the implemented projects and
allows for identification of those factors that influence efficacy. It also enables development of
benchmarks for similar programs or environments in other regions. For example, systemic
analyses of factors influencing health, regional flows of DAH funds, and child mortality have
provided insights on how to realign existing strategies for more effective management of global
health programs (2, 8, 10).
There are few impact studies of development projects available in literature. The current culture
in implementing development projects does not offer sufficient incentives for carrying out indepth impact evaluations, resulting in missed opportunities for both data collection and learning
new insights (21). Most impact evaluation studies done in the past have been based on rigorous
statistical analyses or qualitative assessments that have enabled identification of trends and
outcomes resulting from the intervention programs (2, 29-32).
A potential disadvantage of statistics-based impact evaluation, however, is the selection of
control and intervention groups when ethical considerations make it impractical to select such
groups (33). For example, in evaluating the impact of rotavirus vaccine on diarrheal cases, it is
unethical to intentionally deprive vulnerable individuals of the vaccine solely for the purpose of
designating a control group. Likewise, when multiple programs that are simultaneously in place
have similar effects, it can be difficult to filter possible interactions between and among the
individual contribution of each program, for example, in child mortality studies when both
education and malnutrition can have some direct and indirect health impacts. Lastly, stakeholder
response can be difficult to capture in statistical analysis, and human behavior can have
unpredictable but detrimental effects on program implementation (34).
Agent-Based Modeling and Simulation
Modeling environments as complex systems is becoming increasingly common in many studies
in the natural and social sciences (35-38). Complex systems, as they are frequently used in
biology, physics, economics, and many other disciplines, refer to multi-scale collections of
related objects (a system) that can be identified by their structures and behaviors at different
scales of observation. Typically in a complex system, a central controlling or coordinating
mechanism is absent. As a result, system-level behaviors cannot be predicted based on a precise
understanding of the behavior of lower-level constituents. Such unpredictable system behaviors
are said to be emergent. The autonomous but interdependent interactions of the constituents in a
system may be modeled and simulated in an in silico experimental laboratory to develop and
refine an understanding of the potential behavior of the system. These experiments may suggest
circumstances where unusual or counter-intuitive outcomes might emerge from these
interactions.
Agent-based modeling and simulation (ABMS) provides a framework for understanding the
structure and behavior of a complex system. It enables identification of key components,
factors and processes that give rise to possibly emergent macro-system behaviors. ABMS, also
called Individual-based Modeling Simulation, or IBMS, in ecological studies, refers to using
computer simulation as a tool that allows the decision-making elements of a complex system,
generally referred to as agents, to be represented in software and be equipped with simple
decision rules that capture, at a sufficient level of detail, the actions that the agents might take in
the modeled environment. The simulated interaction of a collection of simulated agents over
time provides an experimental outcome. With the power and availability of computing
resources, many independent experiments may be conducted, providing a researcher with a
deeper understanding of potential experimental outcomes.
Rather than testing a single
hypothesis, ABMS may be considered a tool for hypotheses generation. These hypotheses may
be subsequently tested in field or observational trials.
The development of an agent-based model comprises five stages: 1) conceptualization 2)
construction, 3) validation, 4) model implementation, and 5) verification. The objectives,
expected output, and intended end-users of the model are identified during concept development.
The desired level of abstraction (i.e., scale of observation) of the ABM is also specified during
this stage. The elements of the agent-based model are identified during model construction.
Here, agents are given rules of behavior within the defined boundaries of their environment. The
interactions among the agents are also specified at this stage. The review for model
completeness and accuracy of representation of all the model elements occurs during validation.
The model is also screened for any algorithm or mathematical errors (e.g., unit conversions) that
result in unreasonable simulation outcomes. The validated model is implemented in software
and run on a computer to generate possible outcomes arising from different stimuli to the agent
behaviors. The results of these simulation experiments are then verified to assess whether the
observed results conform to plausible outcomes or observed field data. These observed outcomes
inform decision-making and possibly point out unusual or unexpected outcomes.
Applications of ABM in Environmental and Health Studies
Agent-based modeling and simulation has found extensive application in the evaluation of
policies and strategies affecting environmental systems and civil infrastructures (Table 1).
Agent-based models have been developed and calibrated to study water systems ranging from
small-scale water distribution networks to large-scale watersheds. A calibrated model of water
use and allocation in Beijing City identified several economic implications of existing water
management policies in the city (39). Simulation results from the model have also quantified the
economic benefits of instituting water recycling at the households.
Catchment-scale agent-based models of water systems have also generated new insights on
regulating water quality and quantity. An ABM simulation of water supply and demand in the
Upper Danube catchment revealed a potential risk for water scarcity under extreme climatic
conditions despite current supply surplus (40). Similarly, preliminary simulation results from a
highly integrated ecosystem agent-based model of the Chesapeake Bay watershed provided
unforeseen aspects of the individual and collective impacts on nutrient loading in the bay of
human and development activities across six states in eastern United States (41).
Agent-based models have also become increasingly popular in the health sciences for several
reasons. Firstly, a growing appreciation of the need to account for the complexity of health
problems at the molecular, organism, and community levels exists. In doing so, traditional
vertical translational barriers are successfully crossed. Secondly, High Performance Computing
platforms are increasingly available, for example, the IBM-supported World Community Grid
consisting of 1.8M personal computers (42). Because ABM simulations are essentially parallel in
nature and they allow for the exploration of outcomes over an extremely large number of
assumptions, the availability of relatively inexpensive computing replaces time-consuming and
resource-intensive observational field experimentation. Lastly, the language used to define agent
rules within the model is more accessible to researchers in different fields because only relatively
simple rules are required to guide each agent rather than the differential equations or esoteric
stochastic representations used in traditional simulation modeling approaches (43).
Recent publications have demonstrated the potential of ABM to assist global public health
practitioners as they face challenges ranging from appropriate implementation of insecticidetreated bednets (lTNs) for malaria control with respect to different vectors to development of
recommendations about vitamin D supplementation to predicting the course and preventing
transmission of pandemic influenza (44-46). These in silico experiments were accomplished
more rapidly than observational or interventional trials and at less cost. They also revealed
unexpected findings such as the need to assess individual vector susceptibility to ITNs and the
differential efficacy of school closure in mitigating influenza spread.
ABM has also been used effectively in disease transmission and epidemic modeling. Bomblies
et al. (35) developed an agent-based model to predict the risk of malaria transmission in two
villages in the Sahel Region with distinct hydrological characteristics. The model also enabled
the researchers to explain the ten-fold difference in mosquito density in the villages despite the
villages’ proximity to each other. Mei et al. (47) used ABM, combined with complex networks,
to study the spread of human immunodeficiency virus (HIV) in a small population in Amsterdam
through sexual relations among men. Results from the model simulations were consistent with
historical data of HIV incidences from 1986 to 2006, prompting the researchers to suggest using
the validated model in predicting future incidences of HIV. Borkowski et al. (48) also used
ABM to perform epidemic modeling in a small city with a population of 650,000. The study was
conducted to identify causative agents and factors that turn a disease into an epidemic.
Potential Use of ABM in Impact Evaluation of Health and Development Programs
Providing health services and access to clean water and sanitation is generally regarded as an
effective means to improve over-all public health (15). However, such interventions are difficult
to implement in resource-limited settings, leading to the high variability in their reported efficacy
rates and in some cases, to general failure of the intervention program (16, 18-19, 34). Factors
such as dependence on household-level response and likelihood of sampling bias have been
suggested as possible causes of variability in reported efficacy rates (27), while socio-political
conflict, economic concerns, and general stakeholder disinterest have been cited as contributors
to program failure (22-23, 34).
Although the link between environmental and health challenges is universally acknowledged as a
key element in formulating unified health and environmental policies (49), integration of control
strategies remains poor (27, 50). Independent institutional analyses of global health and
development programs have resulted in concepts such as economic regulation for water services
and formulation of guidelines for DAH fund allocations among countries (8, 51). However,
enhanced awareness on the mutual benefits of health and development is needed to forge an
integrated approach in policy-making (11).
Indeed, policy integration is necessary in developing more effective strategies that result in
greater health and development benefits to communities. However, this endeavor is a complex
process. Formulation of intervention programs requires a thorough understanding of the link
between health and development strategies. This entails an accurate description of field
conditions that can vary from site to site. Additionally, stakeholder response to intervention
programs can be unpredictable at various levels and may even pose the greatest barrier to
successful implementation of isolated or integrated programs. Thus, methodologies for
evaluating strategies and their expected impacts to communities need to account for the
complexity of the environmental, ecological, as well as the social interactions within affected
communities. Statistics-based assessments can be difficult to design and may lead to isolated
analyses of intervention (27). More importantly, representing human behavior in an aggregated
statistical analysis misses the richness of the dynamic interaction of individual behaviors.
Qualitative assessments such as those performed during health and environmental impact
assessments only provide a rudimentary picture of the perceived effects of intervention
programs.
Previous impact evaluations of environmental systems and health intervention strategies have
demonstrated the ability of agent-based models to create accurate descriptions of existing field
conditions and simulate scenarios resulting from perturbations of the described system. In each
of these cases, simulation outputs from the ABM generated new insights that can direct the
course of action for problems such as village-scale control for malaria transmission, overcoming
water recycling obstacles, and regulating nutrient loading in large watersheds (35, 39, 41). The
use of ABM in evaluating integrated health and environmental programs follows the same
fundamental principle as in these applications of agent-based models – communities can be
described as dynamic systems comprising elements (e.g., humans) that exhibit complex
relationships among themselves and with their environment. The scale of the ABM, however, is
expanded so that the link between development and health is properly represented.
Limitations of Agent-based Modeling
Although many agent-based models have been developed for ecological simulations in the past
decades, model verification has been a challenge. Agents in ABM respond to local information
by application of the simple set of behavioral rules and to initial global model parameters making
macro-scale behavior sensitive to the initial conditions of the simulations. Because of this
sensitivity, it is difficult to perform a formal mathematical analysis of the relation between agent
rules and macro-scale system behaviors (52-54). To address this limitation, Grimm et al. (55)
proposed pattern recognition as a method to verify the model architecture. In this approach, the
patterns of observed behavior from the model are compared with those of their real-world
reference. The process involves performing multiple simulations with different sets of parameter
initializations to generate data sets for pattern comparison. However, a vast parameter space
requires a significant increase in available computing power (52).
Another issue with ABMs is the level of detail required to capture the essential elements and
interactions within the modeled system (54-55). The ease of use and functionality of agentbased models depends on the level of detail – scale – necessary to capture the system-level
behavior. To untrained researchers, determining the proper scale of representation throughout a
model can be difficult. Additionally, with human agents, the often irrational and unpredictable
nature of human behavior may be difficult to define with a set of simple rules (54). Ultimately,
in designing agent-based models, it becomes necessary for the modeler to strike a balance
between data requirements and desired level of abstraction.
Over the past decade, techniques to address concerns over ABM calibration and complexity have
evolved towards utilizing systems of hybrid models, especially in large-scale simulations (5660). Hybrid systems (e.g., with a mixture of equation-based, systems dynamics, and agent-based
models) can optimize the functionality of different approaches by handling parallel but
complementary simulations of discrete and continuous processes (61).
Consequently,
computing requirements are reduced considerably while preserving the integrity of the modeled
system (62).
Development of an ABM Framework: Understanding the Impact of Water and Sanitation
on Public Health
Development of an accurate agent-based model begins with the proper representation of the
model framework that describes all elements of the complex system: the characteristics of the
agents, the rules of behavior of the agents, the interactions among the agents, and the
characteristics of the environment in which the agents move about. A sound agent-based model
should be able to capture, to a level of detail, the complex interactions occurring within the
dynamic system it aims to represent. The level of detail required will depend on the expected
output from the model; for example, modeling a community of 30,000 persons may only require
household representation rather than individual representation. Likewise, extraneous interactions
among agents that do not contribute to the end goal of the ABM nor impact behaviors that affect
the model output need not be included.
Modeling health systems requires an understanding of the key elements that define over-all
health. Public health is influenced by policies on health and development, and other inputs from
environmental, social, and health factors. These variables can synergistically or independently
cause an improvement or decline in health status. In the developing country setting, a typical
rural village can have existing health and development policies and programs that govern
livelihood (e.g. farming, fishing), health care (e.g., child and maternal care), education, and
infrastructure (e.g., water and sanitation access) (63-65). A model ABM framework described in
the following sections is set in this backdrop.
Description of Model Community
The model ABM environment encompasses two adjacent rural communities in Limpopo
Province, South Africa (Figure 1). It has a total of 457 households, with an average household
size of 4. The households rely largely on agriculture for livelihood, with both crop and animal
farms randomly distributed within the 8-square km region bounded by the modeling
environment. A small river separates the communities, and a significant number of dwellings
can be found very close to this river.
Water for agricultural needs is sourced from nearby rivers. During the dry season when grass for
grazing is limited farther from the rivers, some farmers bring their animals to graze in proximity
at various locations along the rivers. Water for domestic use is obtained from three sources:
communal tap stands, a chlorinated water distribution system in one community, and surface
water. The municipality provides water to the communal taps. Water for the community-level
chlorinated water distribution system is drawn from the river separating the two communities.
The river water passes through chlorine dosing tanks and is eventually distributed to some of the
community’s households.
A number of groups work with the communities to implement water and health programs. These
programs include: water quality monitoring, manufacture of ceramic filters, and health education
drives. A small health center provides primary medical services and implements a number of
health programs including child vaccination, health education, and maternal and neonatal care.
Defining the Objectives and Scope of the ABM
Diarrhea, the second leading cause of child mortality, accounts for 15 % of global under-5 child
mortality (1.34 million deaths in 2008) (7,12). Repeated episodes of early childhood diarrhea
(ECD) have been reported to affect growth and cognitive development (66-67). An agent-based
model describing the communities is to be designed to understand the relationship between water
and child development as affected by diarrhea. The three objectives of the study are: 1) evaluate
how water quality affects diarrheal rates among children, and how child growth and development
is affected by the frequency of diarrheal episodes; 2) understand how and to what extent the
levels of microbial contamination are introduced in the water chain from source to household;
and 3) identify the most effective intervention strategies that can combat ECD.
Although the ABM is designed to study child health, the model focuses on the collective
behavior and activities of the household rather than that of the individual child. Thus, the
behavioral rules of the decision-making element (agent) will be defined at the household level.
The ABM simulation tracks child health from birth to five years.
The major elements and activities within our prototype community are depicted in Figure 2. The
quality of water sources within the community is affected by agricultural and domestic activities.
Water reaching the households could either be pretreated or untreated, with households having
options for point-of-use treatment for the latter.
Identification of Model Elements and Defining Rules of Agent Behavior and Interaction
This agent-based model has eight principal elements, one of which is a decision-making agent –
the household. A general model of this environment is shown in Figure 3.
Various groups in the two communities are involved in health or water programs. One group
works with a local potter in manufacturing ceramic filters that can be used as point-of use
treatment for water at the households. It has determined pricing for the filters based on the cost
of material and distribution, profitability for the potter, and capacity of the households to pay for
pots. Another group provides health education on topics such as hand washing, disease
prevention, and sanitation. Yet another group monitors water quality at different points in the
rivers, in the households and at communal tap stands. It shares water quality data with the water
service provider and the census bureau.
Households may obtain water from the river bisecting the communities, the communal taps, the
community-level water distribution system, or from any combination of these water sources.
This choice is influenced by the proximity of the household to the water sources, affordability of
the treated water, and reliability of the water service operation. The households can also opt to
perform point-of-use treatment such as filtration (using ceramic filters), boiling of water, and
chlorination.
Children, while they do not make decisions, retain the attributes and decisions made by the
household to which they belong. They are susceptible to diarrheal episodes depending on the
quality of the water they consume and their resistance to infection. The public health indicator
for this study is child growth in height measured over five years when growth is affected by the
number of diarrheal episodes experienced by the child over those five years.
Transforming the ABM Framework to a Computer Platform
The ABM framework in Figure 3 provides the programming template for: 1) identifying the
required input data to the agent-based model; 2) formulating the algorithms that define the
attributes and rules of behavior of the agents as well as the structure of the environment in which
they interact; and 3) delineating the types of relationships and boundaries of interactions among
different agents. The algorithms can then be translated into a source code written using simple
ABMS computer packages such as Netlogo, RePast, and MASON or other high-level
programming languages (e.g. MatLab, Java, C, Fortran, or C++). The model described here was
developed in NetLogo. In general, an ABM can be designed to enable independent or collective
evaluation of the health and development programs by generating algorithms that enable or
disable these programs within the programming logic structure. The accuracy of the resulting
agent-based model is validated by running several simulations and observing whether the outputs
generate plausible outcomes (e.g., water quality is within typical range) or additional elements
and data need to be embedded into the model. Finally, the validated model is verified by
comparing simulation outcomes with observed field data.
Conclusion
Agent-based modeling is a widely accepted and utilized method for analyzing complex
ecological, environmental, and social systems. In this paper, we explored the potential
application of ABM as a tool for the holistic evaluation of the collective health impact of
development and health projects. This approach can be more resource-efficient than
implementing field or observational trials. Through an illustrative framework, we underscored
the novel use of ABM in simulating community-level interactions among human and
environmental agents within an environment where concurrent health and development programs
are in place.
As with other models created for ecological and behavioral studies, agent-based models designed
for integrated impact evaluation are also subject to calibration and complexity limitations. Welldesigned hybrid systems can circumvent these limitations and provide a cross-platform means
for model validation. Over-all, there is a strong potential for the application of agent-based
models as a an inexpensive but effective tool for performing impact evaluations of development
and health projects in developing country settings.
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Table 1 Applications of Agent-based Modeling in Various Research Studies
Scope
Topic
Level of Abstraction
Reference
Water allocation
Village (individuals)
68
Urban water use and
City (households)
39
distribution
Water supply and demand Watershed (1,700 km2)
69
Environment
2
Water supply and demand Watershed (77,000 km ,
40
11.5M inhabitants)
Nutrient loading
Watershed (166,000 km2,
41
17M inhabitants)
Land use for forestry
Island (4,600 km2)
38
2
Sewer network planning
Service area (40 km )
70
Infrastructure
Hydrogen transport fleets Metropolitan (100K
71
inhabitants)
Vaccine rationing
Metropolitan (7.4M
72
inhabitants)
HIV transmission
Community (2.3K
47
individuals)
Malaria transmission
Village (4 km2)
35
Pathogen transmission
Metropolitan (10,400
62
Health
2
km )
Epidemic
City (460 km2, 635K
48
inhabitants)
Lung cancer
Cellular
73
Epidermal Tissue
Cellular
74
Homeostasis
Figure Legend
Figure 1 Base Map of the Two Adjacent Villages in South Africa.
Figure 2 Factors and Activities that Affect Water Quality and Health Status in the Modeled
Villages.
Figure 3 Framework for the Agent-based Model in the Modeled Villages. The modeled
system has eight principal elements, one of which (the Household) is a decision-making agent.
Arrows represent interactions among agents. Solid lines represent programs (health and
development) and decisions affecting other elements in the direction of the arrows. Dashed lines
represent the flow of data from one element to another.