FEATURED GOLDSIM APPLICATION
Modeling Infectious Disease Transmission Using GoldSim
Greg Paoli,
and Todd Ruthman, Decisionalysis
Risk Consultants, and Barry Michaels, The Michaels Group.
Introduction
The need to better understand the transmission of infectious
agents has been underscored by recent events including the
international SARS crisis and reports of the considerable
burden of disease associated with infections acquired in hospitals.
Many questions remain regarding the best way to reduce transmission
of infectious agents. The answers are of great interest to
public health as well as to the health care, food service
and consumer products industries.
The transfer of pathogens between patients and health care
workers in a hospital is an important consideration in developing
hospital policies and procedures. Hospitals may implement
one or more mitigation strategies (e.g. gloves and other barriers,
isolation of patients, hand hygiene products or procedures)
to reduce the incidences of infection due to pathogen transfer.
These mitigation strategies are implemented in a very complex
and dynamic environment. Computer simulation of the transmission
and mitigation processes can improve our understanding of
exactly how the system generates and propagates risk and how
to most effectively manage it.
This article discusses the use of GoldSim to model the transfer
of pathogens between patients and health care workers in a
hospital. The model generates simulated outbreaks in hospital
wards, where the onset, extent of spread, and control of the
outbreak is a manifestation of natural variability and the
impact of simulated control strategies.
The Model
The following is a simplified description, focusing on the
use of GoldSim to represent model elements, for some exploratory
models. In modeling microbial agents, bacterial counts are
commonly expressed as colony forming units (CFU). A new unit,
CFU (colony forming unit) was created for the model as an
Amount (1/Avogradro's Number mol). The same type of unit can
be used for viral particles. (Editors note: In Version 8,
there is now a built-in unit called Item, and the new CFU
unit could have been created by simply defining it as being
equal to 1 Item).
Each patient and health care worker was represented by a
reservoir with units of CFU. During a simulation run, each
patient element might actually represent several individuals
as patients were discharged and admitted. Each health care
worker element might represent several individuals due to
shift changes. Patient turnover was modeled using random Timed
Events. Shift changes where modeled using regular Timed Events.
Discharging patients and shift changes would reset the reservoir
counts to zero. In one version of the model, new patients
were assumed to be the initial source of the pathogens. Each
new patient may have an initial pathogen count of zero or
greater. A stochastic element was used to determine the initial
pathogen count for each new patient (Figure 1). Each new health
care worker was assumed to have an initial pathogen count
of zero.
Figure 1: New Patient Pathogen Count
As the simulation run progressed, contact events between
individual patients and health care workers could result in
the transfer of pathogen from one to the other.
Contact between individual patients and health care workers
were modeled using Timed Event elements with defined rates
but random occurrences (Figure 2). Each contact event would
trigger two Discrete Change Events: one from the patient to
the health care worker and one from the health care worker
to the patient. Each transfer was expressed in units of CFU,
the amount of which depended on the current number of CFU
in the respective reservoir (patient or health care worker).
Figure 2: Contact Model
Each patient reservoir was created with multiple discrete
additions and withdrawals - one for each possible contact
event. The number of CFU transferred from the health care
worker was modeled as an addition, while the number of CFU
transferred to the health care worker was modeled as a withdrawal.
Health care worker reservoirs were also created with discrete
events (in reverse, with patient withdrawals representing
health care worker additions). Patient reservoirs also included
a withdrawal rate to simulate the potential for a decline
in the number of pathogens over time.
A sample result for a single patient is shown in Figure 3.
Figure 3: Patient Result
In this example, the new patient reservoir was assumed to
have a starting population of 50 CFU. For simplicity in this
example, no shift change occurred. The corresponding results
for a single health care worker are shown in Figure 4:
Figure 4: Health care worker result
Results and Discussion
The model, using the dashboard elements to provide a user
interface, provides the ability to simulate the behavior of
patients and health care workers by controlling the statistical
properties of their interaction using GoldSim model elements.
The model generates simulated outbreaks in hospital wards,
where the onset, extent of spread, and control of the outbreak
is a manifestation of natural variability and the impact of
control strategies.
The impact of various policies (including variability in
compliance and efficacy) can be studied over many realizations
to discover the underlying relationship between policy elements
and risk reduction. Initial results suggest that variability
in individual behaviors (e.g., individuals with a very low
frequency of handwashing) can dominate the fate of outbreaks,
suggesting that less importance should be assigned to the
notion of average or typical behavior. This has implications
for the measurement of performance and compliance in designing
and evaluating infection control policies.
In applying GoldSim to this application, the user interface,
stochastic elements, timed random events and the use of reservoir
elements were found to be important tools to represent and
study this infectious disease transmission system. Extensions
to this model and related models for food service and other
environments are also being developed and explored.
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