Download BIOWAR: Simulating Disease Outbreaks using Social Networks

BioWar: Simulating Disease Outbreaks using Social
Networks
Abstract
The reality of life is embedded in social networks. At present, most epidemiological
models do not consider the heterogeneity of social networks when predicting disease
outbreaks. One of the challenges in modeling natural and man-made epidemics is
understanding how social networks affect disease propagation and how the consequences
of disease changes social networks. In particular, early detection of disease outbreaks or
detection of bioterrorism events requires early detection, planning, and a keen
understanding of how diseases propagate through social networks and alter agents
behavior. It is critical to be able to systematically reason about the rate and location of
spread of diseases, early presentation of diseases, potential of media and inoculation
campaigns as counter measures, and the relative value of various possible early warning
diseases (such as access to web-MDs). There is simply not enough actual data on
weaponized attacks to begin to train personnel or to learn from best practices how to
detect such attacks. What is needed is a cost effective, ethical system for reasoning about
such events, for planning responses, and for gaining intelligence on what such attacks are
likely to look like in early stages. Estimating the impacts of large scale biological attacks
and the efficacy of containment policies is necessary from an intelligence and planning
perspective.
In this paper, we describe a simulation system called BioWar which uses cognitively
realistic agents embedded in social, knowledge and work networks to describe how
people interacting in these networks acquire disease, manifest symptoms, seek
information and treatment, and recover from illness. Using a model of diseases and
symptoms, agents who come in contact with infectious agents through their social and
work networks become ill. These illnesses alter their behavior, changing both the
propagation of the disease, and the manifestation of the disease on the population.
BioWar is a city level multi-agent network model of weaponized biological attacks. In
building BioWar advanced artificial intelligence techniques, such as autonomous agents
and machine learning, are combined with state of the art tools from the social sciences,
i.e., social network methods, current spatial epidemiology models, and empirical data
drawn from diverse sources. This combination enables greater accuracy and flexibility in
modeling the behavior of people and organizations.
Presently, we have completed a number of simulations which examine the effect of
contagious and non-contagious illnesses in high-alert (agents have knowledge of a
potential disease outbreak) or low alert states. Agents who believe they may be ill and
have knowledge of a potential outbreak are more likely to seek care than those who do
not. We have compared results of low alert states to known influenza epidemics and to
data containing emergency room visits, pharmacy purchases and absenteeism. Although
the peak incidence of the simulated outbreak is larger than the peak incidence seen in the
population data, the simulation results are temporally similar to those seen in the
population data. Further work to increase the fidelity of both the simulation and the
population data is on-going. It is hoped that this simulation framework will allow us to
ask "what-if" questions regarding appropriate response and early detection strategies for
both natural and man-made disease events. This simulation framework will also allow
the more precise, timely, and localized modeling of naturally occurring disease “noise”
versus the bioattack “signal”. This is especially crucial to enable early detection within
the first critical 48 hours. Secondary data sources such as the sales indicators of drugs,
orange juice, absenteeism would help in this respect. As the simulation framework gets
refined in greater detail, it would become feasible to use it as a bioattack detector – in
addition to as a simulator -- customized to a particular city/area.
Introduction
Throughout history, the spread of disease has been linked to social and environmental
factors. When people live in close proximity to each other, the risk of disease spread—
both infectious and non-infectious—increases. Public health measure and aggressive
vaccination policies have reduced the risk of disease spread, and in some cases, have
eradicated diseases altogether. Smallpox for example, has been eradicated from the
natural population through an aggressive and systematic vaccination program.
However, public health officials remain vigilant to control and prevent disease outbreaks.
Recent attention has turned to bio-terrorism and the risk that terrorists might use weapons
of mass destruction to infect vulnerable populations. In these scenarios, the social
networks in which we live can become the vehicle through which disease is spread. How
best to detection and respond to a man-made disease outbreak is not always clear. The
goal of our research is to develop tools to systematically think about, model and analyze
how diseases would spread through socially connected groups.
In trying to prepare for attacks, policy makers need to be able to think through the
consequences of their decisions in various situations. Consider, for example, trying to
decide if all US citizens should be vaccinated for smallpox. Speculations abound as to
the potential devastation that smallpox could wreak. Medical experts, scientists, and
policy makers need a way of thinking through the morass of complex interconnections to
understand whether different inoculation or containment strategies will be effective.
Unfortunately many existing models are quite limited in that they only apply to a single
disease, discount factors such as the urban geography which can influence disease spread,
or discount how people use their social networks (who is a friend with whom) to pass
information such as when to go to the doctor to be treated. In general, being able to
estimating the impacts of large scale biological attacks and the efficacy of containment
policies is necessary from an intelligence and planning perspective and requires
reasoning about social response and disease processes as a complex social system.
While conventional epidemiology has achieved significant successes in managing
diseases and epidemics, the approach is inadequate in dealing with the high noise to
signal ratio in case of bioattacks where the focus is early detection. Part of the reason for
this is that conventional epidemiology does not inherently account for spatial,
geographical, and social dimensions in its mathematical modeling of diseases. There is a
recent progress in spatial epidemiology which takes into account the spatial and
geographical dimensions directly [Lawson 2001][Gimblett 2002], however it does not
consider the social dimension nor the intricate interplay between different dimensions.
The interplay would affect the shape of signal fed into detection systems. Moreover, the
organizational, media, government/institutional action dimensions also play significant
roles in the event of bioattacks.
To aid the analyst and decision maker we are developing BioWar. In BioWar we are
combining state-of-the-art computational models of social networks, communication
media, disease models, demographically accurate agent models, wind dispersion models,
and a diagnostic error model into a single integrated model of the impact of an attack on a
city. Unlike traditional models that look at hypothetical cities, in BioWar the analyst can
examine real cities using census, school track, and other publicly available information.
Moreover, rather than just providing information on the number of infections, BioWar
models the agents as they go about their lives – both the healthy and the infected. This
enables the analyst to observe the repercussions of various attacks and containment
policies on factors such as absenteeism, medical web hits, medical phone calls, insurance
claims, death rate, and over the counter pharmacy purchases. Analysts could use BioWar
to ask and answer “what if questions” of the form “what would happen to this city if three
people returned from vacation with smallpox?” Moreover, these questions could be
answered from the person’s perspective, the perspective of the organization where he or
she works, and the city’s perspective.
Social Networks
The world we live in is a complex socio-technical system. Although, social,
organizational and policy analysts have long recognized that groups, organizations,
institutions and the societies in which they are embedded are complex systems; it is only
recently that we have had the tools for systematically thinking about, representing,
modeling and analyzing these systems. These tools include multi-agent computer models
and the body of statistical tools and measures that have arisen in social networks. Using
these tools we can begin to think about these complex socio-technical systems in which
people live, and in which diseases can propagate. Such an illustration is particularly
salient in lieu of the tragic events of October 2001 when anthrax was distributed through
the US postal system.
Over the past few years, major advances have been made in the ability to model and
analyze social networks and graphs of agent interactions. Techniques including the P*
family of tools, and the graph level metric tools (such as that for clustering networks) can
be used to locate patterns. Among the data that can be represented as graphs are
interaction or communication networks, monetary networks, inter-organizational
alliances, mental models, texts, web pages, who was present at what event, story lines.
The combination of these techniques with machine learning is likely to be especially
powerful for locating anomalies, assessing coherence, and locating underlying
fundamental patterns.
Multi-agent network models, if based on known information about general or specific
characteristics of groups, can suggest general or specific guidance about how to affect or
protect the underlying group, organization or society. Exactly what these models can
address depends on the model. For example, food poisoning is often discovered through
analysis of social links and shared experiences. More worrisome, infectious bioagents
spread through the social networks of people who are infected. While non-contagious
bioagents such as anthrax do not spread through social networks, it affects the behavior
of social networks and more importantly social networks would facilitate piecing up
together the puzzle of non-contagious attack (e.g., if anthrax is released at a stadium,
sport-fan networks would show a malignant pattern). Since BioWar has imbedded within
it agents that are linked through social, work, and knowledge networks, it allows us to
recognize patterns with the network that may suggest a disease outbreak or bioterrorism
event has occurred.
BioWar is an effort to develop a scaleable and precise simulation tool to examine disease
propagation and agent behavior in response to disease and illness. We believe it will
serve to help researchers understand, predict, and analyze weaponized biological attacks
at the city level and engage in “what-if” analyses to help inform decision-making in this
complex socio-technical policy domain. For example, it can be used in a “what-if” mode
to examine the impact of and response to various weaponized attacks for contagious and
non-contagious diseases under high-alert and no-alert conditions.
BioWar
The following sections describe the components of BioWar and the underlying rationale
of the disease, network, and agent models. Graphically, these components are illustrated
in Figure 1.
Figure 1. The design of BioWar. Input characteristics of the agents, diseases,
geography, and communication technology are used by the agent model to change
behavior. The raw output can be used to test early detection algorithms, and
hypothetical what-if scenarios used to analyze policy impact.
Disease Model
In constructing our disease model, we used historical accounts of known anthrax releases,
documents from the October, 2001 bioterrorism attacks, and known disease knowledge
bases. The disease model considers risk factors (to identify subpopulations at more risk of
developing a disease), symptoms, diagnostic tests, and disease progression to describe
how a bioagent release would affect an individual, and focuses on how these individual
characteristics affect behavior and the data that might be collected from non-routine
sources. We have drawn on the experience of other medical expert systems developed to
assist in diagnosis to ground our disease model in well-founded medical knowledge
representations [Miller 1982].
age: 34
gender: F
occupation:
mailroom worker
Initial Population
infection propagation
ongoing infection
and health seeking
behaviors
population state at the end
of the simulation
FIgure X. Example population changes for a simulated contagious disease. In part A, the population is initialized to match
the demographics and social characteristics of the population of study. Once the simulation begins, individuals become
infected, and can propogate the infection to people nearby in their social network. As patients manifest symptoms (black
color), they change their behavoir, and may seek medical care, or stay home from work (reducing the risk of spreading
the infection). The final population state may include patients who have died, who currently have symptoms or who have
changed their behaviors in response to their disease symptoms.
Representation of Disease course
We divide the disease symptoms into three groups, divided by time course: those
symptoms and risk factors that predispose a person to development of a disease, those
symptoms that might prompt a visit to the doctor or pharmacist, and those symptoms or
tests that would help with diagnosis or may affect mortality and morbidity. Pre-existing
risk factors affect who has a higher probability of getting a disease, and will use
information about agent demographics during simulation to afflict agents with disease.
For example, an elderly or immunosupressed patient may be at a higher risk of
contracting anthrax if exposed. Once infected, the patient then begins to acquire
symptoms as their disease progresses. This affects patient behavior in the simulation. For
example, a patient with multiple, non-specific symptoms might be more likely to go to a
physician or visit a pharmacy. When a patient has developed enough symptoms to
warrant further investigation, a portion of the patients who are ill will seek medical care.
It is at that point a patient may have additional tests performed, use more healthcare
resources, and likely have their information recorded in databases that serve as a source
for early detection. This progress is shown in Figure 2.
Risk factors
As we discovered in the anthrax releases in October 2001, certain demographic groups
are more likely to be susceptible to particular diseases than other. These risk factors
increase a person’s susceptibility to diseases either through host factors, or environmental
factors to which that person is exposed. For example, individuals who have contact with
animals (sheep shearers, for example), are more likely to contract cutaneous anthrax than
other occupations. Risk factors are distributed to individuals in the population, according
to demographic characteristics, and we can determine these risks based on age, sex, and
disease prevalence.
Symptoms
Symptoms are important in our model in that they motivate behavior changes. People
with symptoms will stay home from work, will visit their doctor or pharmacist, and will
change their patterns of interacting with others. This change in interaction patterns leaves
a “footprint” on the data streams used to detect bioterrorism events. It is this manifest
pattern of symptoms that enables early detection of bioattacks.
The symptoms have two different measures that influence which symptoms a person gets
and how that changes their behavior. The frequency measure is a qualitative measure of
how frequently people with a particular disease will manifest this symptom. The evoking
strength is a qualitative measure of how frequently someone with a particular symptom
will think they have a particular disease. For example, the symptom of cough may have a
very high frequency among patients with anthrax—however cough would have a low
evoking strength because people with a cough do not immediately think they have
anthrax.
Frequency
Frequency is a number between 1 and 5 which answers the question: “In patients with
disease x, how often do you see symptom y?”. For example, patients with the diagnosis
of anthrax will have a fever frequency of 5 – nearly all patients with anthrax will have
fevers at some point in the course of their disease.
Evoking Strength
Evoking strength is a number between 0 and 5 which answers the question: “When you
see symptom y, how often do you think the cause is disease x?” For example, fever
symptoms are not specific to any one disease – in our disease profile of anthrax, fever is
given a ES of 1. However, widened mediastinum is a more specific manifestation of
anthrax – in patients who have a widened mediastinum, the diagnosis of anthrax should
be considered. Evoking strength is similar to specificity.
How symptoms relate to agent behavior
The frequency and evoking strength associated with individual symptoms are used in
different ways within BioWar. Although individuals will get symptoms based on the
symptom frequency for the disease that they have, they will actually alter their behavior
based on the evoking strength of that symptom for a serious illness. For example, if a
person contracts an anthrax infection and displays fevers and chills, in the absence of a
known attack, may consider their symptoms to be an influenza infection or simple cold
and not significantly alter their behavior. However, if they began heaving shortness of
breath, chest pains, or other symptoms more suggestive of a serious infection, they would
likely stay home from work, seek medical attention or go to an emergency room.
Diagnostic tests
Diagnosis is simulated based on the apparent symptoms. It produces certain accuracy in
the form of ROC curve similar to the real diagnosis. The result of diagnosis would
determine whether a person is treated properly and recovered. To determine which
disease a person has, the evoking strengths among potential diseases are compared and
the highest one is chosen as the diagnosed disease.
Current Simulation Experiments and Preliminary Results
Figure 3 shows a potential view of the results of a BioWar simulation. Within BioWar, it
will be possible to examine the impact of weaponized attacks for the city as a whole,
particular geographic sub-locations, the entire population, or specific sentinel populations
such as school children, military personnel or police for all diseases or for a specific
disease.
Figure 3. An interface into BioWar. This illustrates the GIS and graphical capabilities
that BioWar will have to display simulation information.
In addition, a comprehensive set of information will be tracked by location and time that
will include over-the-counter drug purchases, web access, symptoms, accurate and
inaccurate diagnoses, physician and emergency room visits, deaths and recoveries. Each
type of information will be capable of being displayed separately or in aggregate for the
population as a whole or by sentinel group or physical sector. (It is also possible to track
a simulated health & life history of a particular kind of person.) For example, over the
counter purchases are displayed in Figure 4.
Ov e r-the-Counte r Drug Purchase s, based on appare nt symptoms
40
35
30
#Purchase
25
cough
cold-co ugh
cold
as pirin
imm odium
20
15
10
5
0
1
18
35
52
69
86 103 120 137 154 171 188 205 222 239 256 273 290 307 324 341 358 375 392
Day
Figure 4. Illustrative BioWar Output for Over the Counter Drug Purchases
Virtual Experiment
High Alert vs. No-alert conditions
In the following section, we discuss how knowledge of the alert conditions—high alert
versus no-alert conditions affect the behavior of agents and ultimately the outcomes and
mortality of the populations. This is an example of how policy decisions and response
strategies can have a very significant effect on the outcomes of a bioterrorism attack.
For our initial experiments, we assume that in the states in which there was not a high
state of alert, patients would not immediately seek medical care, and physicians and
health care providers would not consider the illness to be a bioterrorism attack. In these
settings, treatment is delayed and is less effective, and patients would be more likely to
self-treat their symptoms.
In conditions in which there is a state of high alert, both patients and healthcare provides
are more likely to assume their illnesses represent a potential bioterrorism attack, and
seek early intervention at a clinic or emergency room. Healthcare providers have a higher
index of suspicion that patients may have been exposed, and treatment is more effective
and more efficient.
Figure 5 and Figure 6 show non-alert versus high-alert conditions for a non-contagious
disease like anthrax. In both cases, the number of exposed and infected patients is similar.
However, in the non-alert conditions, more patients seek over-the-counter medications
and fewer are seen by healthcare providers. The result is a higher death rate with nonalert conditions than with alert conditions.
Bioattack Effects (Noncontagious, Non High-Alert)
250
200
Infected
#Persons
Go home, calling sick
150
Go to pharmacy
Go to doctor
Go to ER
Total death
100
Total immune
Total cured
50
120
113
106
99
92
85
78
71
64
57
50
43
36
29
22
15
8
1
0
Time (days)
Figure 5. No-alert condition: people self-treat themselves by going to pharmacies,
resulting in more deaths.
Bioattack Effects (Noncontagious, High-Alert)
250
200
Infected
#Persons
Go to home, calling sick
150
Go to pharmacy
Go to doctor
Go to ER
Total death
100
Total immune
Total cured
50
120
113
106
99
92
85
78
71
64
57
50
43
36
29
22
15
8
1
0
Time (days)
Figure 6. High-alert condition: people go directly to ER, resulting in more people treated
and recovered and less deaths compared to that of no-alert condition (though the number
of deaths is still high due to the severity of the anthrax attack).
All agents within the simulation are socially situated and tend to associate with similar or
like-minded individuals. Agents that share characteristics will tend to associate with other
agents who are like them. For infectious diseases, the social network takes on more
significance—those people infected will tend to associate and infect other agents within
their social network.
We run virtual experiments for infectious/contagious bioattacks in addition to noncontagious bioattacks. In one of these experiments, we simulate the occurrences of
bioattacks on the background of influenza and staph pneumonia. The population of
Pittsburgh is approximated by a representative sample of 2000 people, with the
corresponding demographics ratios (age, gender, and race). The length of the simulation
is 90 days.
The background diseases will have varying degree of base rate (the susceptible
proportion), transmissivity, life span, and time of occurrence. There are a total of 10
strains of influenza and 10 strains of staph pneumonia. The staph pneumonia is assumed
to be community acquired and mild; it has the duration of 7-10 days.
The bioattacks will occur at day 15, during which bubonic plague bioattack – which is
contagious -- is released, and days 20, 30, 40 during which cutaneous anthrax is released.
All background diseases and bioattacks have associated symptoms, evoking strengths,
and frequencies data as contained in the QMR database. QMR is a diagnostic decisionsupport system with a knowledge base of diseases, diagnoses, findings, disease
associations and lab information. QMR contains information from the primary medical
literature on almost 700 diseases and more than 5,000 symptoms, signs, and labs.
More concisely, the following table shows the parameters of the simulation.
Table 1. Simulation Variable Values
Disease
Type
Influenza
Strain 1
Strain 2
Strain 3
Strain 4
Strain 5
Strain 6
Strain 7
Strain 8
Strain 9
Strain 10
Staph
Pneumonia
Strain 1
Strain 2
Strain 3
Strain 4
Strain 5
Strain 6
Strain 7
Strain 8
Strain 9
Strain 10
Bubonic
Plague
Attack 1
Cutaneous
Anthrax
Attack 1
Attack 2
Attack 3
Background
Base Rate
Transmissivity Life Span
Time
of
Occurrence
0.13
0.07
0.16
0.14
0.09
0.05
0.08
0.10
0.14
0.06
0.36
0.42
0.43
0.44
0.43
0.38
0.42
0.50
0.36
0.37
7.06
8.39
7.42
8.65
8.61
7.76
8.94
7.19
8.77
7.88
0
52
30
39
41
55
66
55
43
30
0.05
0.19
0.15
0.12
0.19
0.05
0.05
0.11
0.05
0.15
0.09
0.09
0.13
0.13
0.16
0.15
0.06
0.05
0.10
0.19
8.94
8.92
7.73
8.91
8.73
8.13
7.27
7.41
7.75
7.85
76
42
60
62
3
67
80
37
37
33
0.40
0.60
7.00
15
0.40
0.40
0.40
0
0
0
21.00
21.00
21.00
20
30
40
Background
Bioattack
Bioattack
Results
Figure 7 below shows the result denoting the general statistic of health, counting the
number of people infected, died, cured, and immune. Additionally, it gives supplemental
statistics useful for the early detection of bioattack: the number of people who got sick &
go home, go to pharmacy, go to doctor, and go to ER.
One Bubonic Plague (Day 15) + Three Anthrax Attacks (Day 20, 30, 40)
2500
2000
#infected
go home, calling sick
go to pharmacy
1500
#Persons
go to doctor
go to ER
cumulative death
cumulative immune
1000
cumulative cured
#healthy
#dead
500
86
81
76
71
66
61
56
51
46
41
36
31
26
21
16
11
6
1
0
Day
Figure 7. General Statistics of Health over 1 Contagious Bioattack and 3 Non-Contagious
Bioattacks.
As Figure 7 shows, after the Bubonic Plague happens on Day 15, the number of people
who died rises. Then on Day 20, an Anthrax attack occurs, which further increases the
number of people who died afterwards of bioattacks. However, when the second and the
third Anthrax attacks occur, the number of people who died does not increase as much
afterwards. This is due to an increased people’s awareness of bioattacks and stockpiling
of antibiotics like Cipro. This could be seen in the graph by “cumulative death” line.
Figure 8 below shows the contribution of Bubonic Plague.
Bubonic Plague Factor
1200
1000
#Persons
800
#infected
#cured
600
#immune
#death
400
200
86
81
76
71
66
61
56
51
46
41
36
31
26
21
16
11
6
1
0
Day
Figure 8. Bubonic Plague Contribution
As Figure 8 shows, there is an increase of the number of death because of the Bubonic
Plague.
Figure 9 below shows the contribution of Cutaneous Anthrax.
Anthrax Factor
1000
900
800
700
#Persons
600
#infected
#cured
500
#immune
#death
400
300
200
100
86
81
76
71
66
61
56
51
46
41
36
31
26
21
16
11
6
1
0
Day
Figure 9. Anthrax Contribution
As Figure 9 shows, while after the first anthrax attack there is an increase in number of
death, during the second and third anthrax attacks there is fewer number of death.
Figure 10 below shows the manifested symptoms during the time.
Symptoms
40
35
30
#Persons
25
cough
sneeze
20
fever
diarrhea
15
10
5
89
85
81
77
73
69
65
61
57
53
49
45
41
37
33
29
25
21
17
9
13
5
1
0
Day
Figure 10. Symptom Manifestations
The above symptoms are combined symptom manifestations of background disease
strains of influenza and staph pneumonia, and of bubonic plague and anthrax bioattacks.
Data Sources
There are several categories of data needed for generating BioWar simulation. As
described earlier, BioWar simulation is complex; it involves sociological, geographical,
spatio-epidemiological, technological, demographical, financial, and psychological data.
It also involves issues of government, authority, and the media.
Categories of data:
 Sociological: demographics, social network, social contact, diffusion of
information
 Psychological: perception of disease, perception of doctors, perception of
bioattacks
 Geographical: the nature of the terrain, city layout, local weather, vegetation
 Economic: banks, confidence, government funding, general health spending,
cost/benefit of treatment procedure and of response strategies to bioattacks




Epidemiological: spatial epidemiological models and data, disease behaviors,
vector behaviors
Technological: detection devices and drugs
Media: newspaper, TV, radio, the Internet
Institutional: governmental authority, government agencies, city mayors,
congressional decisions, non-governmental organizations.
All the above factors interplay with each other resulting in a cumulative contribution to
the fidelity of BioWar simulations. Not all of the above have been implemented, but work
is progressing to include more and better data in BioWar.
General Social Survey (GSS)
This GSS data set describes the general and comprehensive social characteristics such as
work status (working full time, working part time, unemployed, in school), occupation
(transportation worker, farmers, military people), work hours, medication taking habits,
who to talk to in time of need, family networks (sibling, brothers/sisters, single mothers),
working in a small or big firm, Internet use, commuting time, etc. GSS in the Year 1985
describes the social network. Thus GSS data set describes the general characteristics of a
heterogeneous population.
The source of GSS we use is GSS 1972-2000 Cumulative Datafile, published by the
National Opinion Research Center (NORC), with Inter-University Consortium for
Political and Social Research (ICPSR).
GSS is used to parameterize agents in biowar simulations to reflect the people in the real
world. For example, the working time in the simulation is modeled after the real world
data from GSS, resulting in a much fewer people work at weekends and during the night.
If a bioattack occurs at night, it has a different effect than if it occurs during the working
hours of most people. Other GSS data, such as the Internet use, provides estimates to
what people would be able to effectively use during/after a bioattack.
Census 2000
The Census 2000 data describes the demographic characteristics of a population, income
level, area profiles, birth & death rates, migration rate, estimate of population growth,
among others.
The source of this census data is Census 2000 from US Census Bureau.
This census data set is used to parameterize agents with appropriate demographics for a
certain area as agents have the spatial dimension. It will also be used to provide the
natural birth, death, and migration rates of agents.
ArcGIS
ArcGIS data set describes the characteristics of geographical area. It provides maps,
charts, views, and graphical user interfaces for geographical information systems.
The source of ArcGIS data set is ESRI, in addition to publicly available mapping data
from USGS. Additionaly, census tract boundary files, school district boundary files, and
ZCTA (ZIP Code Tabulation Area) from Census Bureau are used.
ArcGIS data set and tool are used to provide graphical user interface for biowar
simulations. In the future, ArcGIS data set may be used to constraint the behavior of
agents in a local geographical area, depending on it characteristics. The geographical
constraints also affect the spread of diseases. USGS data set is particularly interesting as
it provides maps of water resources. Water resources may be used as a means to spread
biological agents.
Biosurveillance Database version 1 (Biosurv1)
Biosurv1 data set describes the patient visits to a hospital, the symptoms they have, and
their work place and home location.
Biosurv1 data set comes from the Laboratory for Computational Epidemiology, Center
for Biomedical Informatics, University of Pittsburgh Medical Center (UPMC).
This Biosurv1 data set is used to parameterize agent behaviors when it comes to visiting
doctors and hospitals with respect to time. It is also used in as the ground truth for
detection of a bioattack.
BigFred Datasets
BigFred datasets describe the drug purchase behaviors of a population in a particular
area. These datasets come from UPMC. We use the datasets to validate BioWar
simulation.
Spatial epidemiological data
Spatial epidemiology (Lawson 2001, Gimblett 2002) is the study of epidemiology in time
and space. There is an increasing quantity of spatial epidemiological data. Spatial
epidemiological data sets are used to parameterize, compare, and validate BioWar
simulations. For background influenza disease, spatial epidemiological data for influenza
will be used to parameterize agents having influenza.
Quick Medical Reference (QMR) data
QMR data set describes the interrelationship between diseases, symptoms, diagnosis, and
treatments, among others. QMR itself is diagnostic decision-support system with a
knowledge base of diseases, diagnoses, findings, disease associations and lab
information. With information from the primary medical literature on almost 700 diseases
and more than 5,000 symptons, signs and labs.
The source of this data set is FirstDataBank, Inc.
The QMR data set is used to parameterize the disease models on the relationship between
diseases and symptoms.
Hospital data
Hospital data describes the capacity of a hospital, the patient admission rate, and the
number of nurses and physicians. This hospital data will be used to parameterize hospital
abstract agents in BioWar simulations. The hospital data is useful for determining the
capacity of care in case of a bioattack and for researching an improvement to the health
system with respect to bioterrorism.
Emergency room data
Emergency room data describes the critical care capacity of health systems. This
emergency room data will be used to parameterize the critical care abstract agent in
BioWar simulations.
Looking up Health Information Online (LHIO) data
LHIO data describes the percentage of people looking up health information on the
Internet based on their demographics (gender, race, age). This data set is useful to
parameterize how agents behave online after enduring certain symptoms or after knowing
a bioattack occurred.
Emergency Room Statistics (ERS)
ERS data describes emergency room visits based on demographics. This data set is used
to parameterize how demographics affect agent’s decisions to go to Emergency Room.
Doctor Visits (DV)
DV data describes doctor visits based on demographics. This data set is used to
parameterize how demographics affect agent’s decisions to go to a doctor.
Purchase of Over-the-Counter Drugs (OTC)
OTC data describes the over the counter drug purchasing rate based on demographics.
This data set is used to parameterize how demographics affect agent’s decisions to
purchase over the counter medicine.
Limitations
The current implementation is a work in progress. It has the following limitations:
 There is no model of the media (TV, cable, Internet) in the current
implementation, other than a simple alert status variable and simple Internet use
statistics.
 Lack of schooling and sentinel population data.
 While the current implementation has a simple model of location in terms of
longitude & latitude and zipcodes, it lacks the sophistication of ArcGIS
geographical depth.
 The disease model currently implemented is a simple one. More complex and
higher fidelity disease models are being worked on.
 The empirical data is somewhat limited in quantity and quality. We need to gather
more and better data.
Extensions
In addition to fixing the limitations mentioned in the previous section, we are extending
the model to include:
 Gathering locations outside work, school, stadium, and home: movie theaters,
bars, restaurants, universities, parks, and malls
 Interface to ArcView geographical information system
 A better user interface written in Java, which would be capable of controlling the
simulation (possibly residing on a server) over the Internet and getting the result
transmitted securely over the Internet.
 Complete alert system based upon the new color-coded alert system announced
by the Office of Homeland Security. This would also allow users to enter and/or
alter the alert level.
 Appropriate response scenarios to the alerts.
 Cost benefit analysis of medical procedures and response policies, modeled on top
of the multi-agent network model.
 Automated what-if theorizing of detection, responses, and organizational
effectiveness to a bioattack and/or a pattern of bioattacks.
 Validation procedure of bioattack simulations against empirical data.
 Parallel code to run on a supercomputer, which would enable us to run millions of
agents.
REFERENCES
[Gimblett 2002]
Gimblett, H. Randy, “Integrating Geographic Information Systems
and Agent-based Modeling Techniques”, Santa Fe Institute, Oxford University Press,
2002.
[Lawson 2001]
Lawson, Andrew B., “Statistical Methods in Spatial
Epidemiology”, John Wiley & Sons Publisher, 2001.
[Miller 1982]
Miller RA, Pople HE, Myers JD, “Interist-I, An Experimental
Computer-based Diagnostic Consultant for General Internal Medicine”, N Engl J
Med 1982, 307:468-76.
APPENDIX: PICTURES
Bioattack Effects (Contagious, Non High-Alert)
400
350
300
Infected
Go home, calling sick
Go to pharmacy
Go to doctor
200
Go to ER
Total death
150
Total immune
Total cured
100
50
Time (days)
120
113
106
99
92
85
78
71
64
57
50
43
36
29
22
15
8
0
1
#Persons
250
Bioattack Effects (Contagious, High-Alert)
250
200
Infected
#Persons
Go home, calling sick
Go to pharmacy
150
Go to doctor
Go to ER
Total death
100
Total immune
Total cured
Healthy
50
120
113
106
99
92
85
78
71
64
57
50
43
36
29
22
15
8
1
0
Time (days)
Bioattack Effects (Noncontagious, Non High-Alert)
250
200
Infected
Go to pharmacy
Go to doctor
Go to ER
Total death
100
Total immune
Total cured
50
Time (days)
120
113
106
99
92
85
78
71
64
57
50
43
36
29
22
15
8
0
1
#Persons
Go home, calling sick
150
Bioattack Effects (Noncontagious, High-Alert)
250
200
Infected
Go to pharmacy
Go to doctor
Go to ER
Total death
100
Total immune
Total cured
50
Time (days)
120
113
106
99
92
85
78
71
64
57
50
43
36
29
22
15
8
0
1
#Persons
Go to home, calling sick
150