Download Ontology Based Modeling of Pandemic Simulation Scenarios

PROSPER
Smittsamma sjukdomars inverkan på det svenska
samhället: mot evidensbaserade responsstrategier
Toomas Timpka
Henrik Eriksson
Anders Grimvall
Joakim Ekberg
Magnus Strömgren Einar Holm
James M Nyce
Elin A Gursky
Olle Eriksson
Lars Valter
Ny kunskap för samhällsskydd och beredskap
MSB 8 december 2009
The CriSim group
 Department of Computer and Information Science
Department of Medicine and Health Sciences
Linköping University, Linköping, Sweden
 ANSER/Analytic Services Inc.
Arlington, VA., USA
 Department of Social and Economic Geography
Umeå University, Umeå, Sweden
 Department of Anthropology
Ball State University, Muncie, IN., USA
Introduction
 Over the last decades, several serious infectious diseases have
emerged rapidly to become global threats, including the severe
acute respiratory syndrome (SARS) and avian influenza.
 Each of these diseases has required a fast and specific
response from policy-makers and public health authorities.
 Such a situation occurred in 2009 with the emergence of the of
a novel A/H1N1 influenza virus (the ‘swine flu’) in Mexico.
Introduction – the ’swine flu’
 On 28 April, Mexico had 26 confirmed human cases with seven
confirmed deaths. Elsewhere, there were confirmed cases and deaths
in the USA, Canada, UK, Spain, New Zealand, and Israel (1).
 On 11 May, researchers analyzing data from the Mexican outbreak
reported that the transmissibility of the new influenza strain was
substantially higher than for seasonal flu, and comparable with lower
estimates of the basic reproduction rate obtained from previous
influenza pandemics (2).
 One month later, transmission in several countries could no longer be
traced to clearly-defined chains of human-to-human contacts, and
further spread was considered inevitable. Accordingly, with 30.000
confirmed cases in 74 counties, the WHO raised the influenza
pandemic alert to the highest level, phase 6 (WHO 2009)
1. Swine influenza: how much of a global threat? Lancet 2009;373(9674):1495.
2. Fraser C, Donnelly CA, Cauchemez S, et al. Pandemic Potential of a Strain of Influenza A (H1N1) : Early
Findings. Science 2009. DOI: 10.1126/science.1176062
Pandemic influenza
 Global epidemic with high mortality
 ’Spanish influenza (1918)
 ’Asian ’flu’ (1957)
 ’Hongkong ’flu’ (1968)
 Modified (1918) or reassorted (1957,
1968) avian virus
 Destructive in non-traditional risk
groups
Taubenberger et al, Nature 2005;437:889-892.
Edward Munch: After the Spanish
disease (self portrait), 1919.
Introduction – public health response
Public health officials in nations affected by the ‘swine flu’
decided on response actions
 In the absence of a vaccine, closure of schools with infected
pupils was used by some countries, but not others
 In the USA, the CDC initially supported school closures
 The Public Health Agency of Canada did not recommend
closing schools
 The UK Health Protection Agency took the position that
“consideration should be given to temporarily closing the
school” (1)
1. Editorial. Putting influenza A H1N1 in its place. Lancet Infectious Diseases. 2009;DOI:10.1016/S14733099(09)70134-3 1.
Introduction – infrastructural issues
 The principles for forming the response to the ‘swine flu’ seem to have
remained unsystematically linked to the particular forms of disease
validation and prediction locally on hand.
 If poorly validated and coordinated methods are allowed to inform
policy-making on emerging infectious diseases, these methods may
dangerously mislead critical response implementation (1,2)
 The social distancing example from the ‘swine flu’ outbreak is
particularly disquieting in light of that the effectiveness of this set of
measures recently had been questioned because of the major
impediments to compliance (3).
1. ECDC. Now-casting and short-term forecasting during influenza pandemics - a focused developmental
ECDC workshop. Stockholm: ECDC, 2007.
2. Timpka T, Eriksson H, Gursky E, et al. Population-based simulations of influenza pandemics: validity and
significance for public health policy. Bull World Health Organ 2009;87:305-311.
3. Rothstein MA, Talbott MK. Encouraging compliance with quarantine: a proposal to provide job security and
income replacement. Am J Public Health 2007;97 Suppl 1:S49-56.
Negotiated
theories
Empirical
observation
Empirical
evidence
Descriptive
analysis
Hypothesis
testing
Intervention
experiments
Forecasts
Policy
decisions
(Re) evaluation
Multi-level
simulations
Research
questions
Empirical data
collection
Overview of workflow in establishment of evidence on
rapidly emerging infectious disease outbreaks
Research problems
 There is no common infrastructure in place for analyses of
pandemic data and sharing of information
 The co-ordination of the response within nations and across
national borders remains an issue
An information infrastructure is required that differs from traditional
health information systems in that it is to be used in situations when
infectious diseases overwhelm the first-order resources on hand
designed to detect and control the outbreak.
Research aims
 to draft a protocol that can be used to realize a
standardized information infrastructure for rapid
production of pandemic response program evidence
The hypothesis is that it is possible to standardize the establishment of
a distributed global infrastructure for rapidly translating evidence from
analyses of available data into coordinated response when addressing
worldwide emerging infectious diseases.
Methods
A. Requirements data collection
Coordinator
Expert panel
Program analysis
scope
assignments
Test bed
requirements
Expert panel
Test bed
functions
B. Design pattern specification and test bed realization
Coordinator
Expert panel
Overview of the methods used for
A. Data collection
B. Data analysis
Design patterns
Test bed
realization
Methods
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Data collection
A nominal group method (1) was used to collect requirements data.
Two expert panels examined and outlined requirements on the protocol with
regard to the scope of data sources and analytic functions to be covered,
respectively.
Individual experts reviewed a working requirements document followed by
telephone discussions (n=18).
Requirements on the data were defined by a panel consisting of scientists and
practitioners (n=8) with backgrounds in medicine, epidemiology, medical
anthropology, computer science, health informatics, cognitive science, and
socio-economic geography.
The panel examining requirements on analytic functions consisted of scientists
and practitioners (n=5) with backgrounds in medicine, statistics, computer
science, health informatics, and cognitive science.
When subsequent turns did not return significant changes in the documents,
the requirement specifications were considered to be established.
1, Jones J, Hunter D. Consensus methods for medical and health services research. Bmj 1995;311(7001):37680.
Methods
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Data analysis
A method for rational solution of multi-facetted design problems (1,2) was used
for data analysis
The members of the two panels were merged into one protocol specification
group
The task communicated to the group was to formulate a protocol design using
the requirements, their subject matter expertise, and the published literature.
The experts first provided their individual comments, which were collected by a
design process coordinator
Formulation of functional design solutions was performed independently by
experts who reviewed a document describing the model described as design
patterns
The design patterns were represented in the form Title, Problem-Requirements,
Functional design, and Realization
In the final step, the design patterns were summarized in the PROSPER
protocol (PROtocol for Standardized Pandemic and Emerging infectious
disease Response)
1. Rittel H, Webber M. Dilemmas in a general theory of planning. Policy Sciences 1973;4(2):55-169.
2. Simon H. Design of the artificial. 2nd ed. Cambridge, Mass.: The MIT Press, 1981.
Results - requirements
 Status overview Major problems during the early stages of pandemic
planning include
- analytic methods and technologies are uncoordinated with the
organization of response programs
- shortage of reliable microbiological and epidemiological data, and
- lack of universally applicable detection and forecasting methods
 Data requirements Specifications are needed of population and
community data, the quality and timeliness of outbreak data, and on
how data on population behavior are represented, especially over time
 Analytic functions requirements Comparative assessments of
response program effectiveness, rather than efficacy, are needed
Results – PROSPER*
 The PROSPER protocol outlines an information infrastructure
for pandemic response that is defined with reference to
response program implementation.
 The infrastructure can be realized using conventional system
implementation methods by regional, national and international
public health agencies or other organizations with an interest in
rapidly responding to pandemic threats.
* PROtocol for Standardized Pandemic and Emerging infectious disease Response
Results
Infectious
disease
response
Context of
response
Program context
Stages of
implementation
Process design
Response
outcomes
- for individuals
- for organization
Effectiveness
Implementation process evaluation
Information
infrastructure
defined by
PROSPER
Capacity and
needs
assessment
Analysis scenario
management
Access to
epidemiological
and population
data
Iterative response
process analyses
Maintenance of
knowledge base on
intervention
program design
Response
program
process
program
design
process
Outcome and
impact
evaluation
Comparative
analyses of
outbreak detection
and intervention
effectiveness
design
Display of the PROSPER protocol in relation to intervention program implementation (1)
1. Rogers E. Diffusion of Innovations. Fifth ed. New York: Free Press, 2003.
Results
 The PROSPER infrastructure covers analyses of the response
context, the response processes, and outcomes and impacts.
For each of these aspects, it outlines the basic infrastructure at
levels of evidence, function, and technical systems.
 Evidence on the program context is organized with reference to
the STROBE, guidelines on what should be included in reports
of observational studies
 Reports on the process design and program effectiveness are
organized according to the SQUIRE guidelines for reporting
studies of quality improvement in health services
Response program evidence
STROBE guidelines
SQUIRE guidelines
Table 1
Evidence level
Functional
level
Technical
system level
Table 2
Table 3
Program context
Process design
Effectiveness
Capacity and needs
assessment
Implementation process
evaluation
Outcome and impact evaluation
A1
A2
Analysis scenario
management
Access to
epidemiological
and population
data
Ontology
management
system
Social
geographic
population
database
Population
based
administrative
healthcare
databases
Laboratory
and
syndromic
databases
A3,I2
I2
Iterative response
process analyses
Disease
model
repositories
Maintenance of
knowledge base
on intervention
program design
Ontology
management
system
Program
databases
D,I2
D1,I
Comparative
analyses of
outbreak detection
alorithms
Statistical
analysis
packages
Comparative
analyses of
intervention
effectiveness
Social
geographic
population
database
Population
based
administrative
healthcare
databases
Laboratory
and
syndromic
databases
Simulation
software
Results
 The structure of the functional level reflects the methods used
to produce pandemic evidence and the organization of
infectious disease response.
 The Capacity and needs assessment function is informed at the
technical systems level be systems for laboratory and
syndromic data access and visualization, and analysis scenario
management (Table 1).
Response program evidence
SQUIRE guidelines
STROBE guidelines
Evidence level
Functional
level
Technical
system level
Table 3
Table 2
Table 1
Program context
Process design
Effectiveness
Capacity and needs
assessment
Implementation process
evaluation
Outcome and impact evaluation
A1
A2
Analysis scenario
management
Access to
epidemiological
and population
data
Ontology
management
system
Social
geographic
population
database
Population
based
administrative
healthcare
databases
Laboratory
and
syndromic
databases
I2
A3,I2
Iterative response
process analyses
Disease
model
repositories
Maintenance of
knowledge base
on intervention
program design
Ontology
management
system
Program
databases
D1,I
D,I2
Comparative
analyses of
outbreak detection
alorithms
Statistical
analysis
packages
Comparative
analyses of
intervention
effectiveness
Social
geographic
population
database
Population
based
administrative
healthcare
databases
Laboratory
and
syndromic
databases
Simulation
software
Results
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Table 1. PROSPER design pattern Capacity and needs assessment.
Capacity and needs assessment
Analysis scenario management
Problem-Requirements: A1 Sociogeographical representation of communities
Functional design A spatially explicit model for population representation is used to allow for experiments in full scale with factual and synthetic populations.
This design solution supports that different sociogeographical scenarios can be defined by changing the starting conditions of the model. Other basic model
categories included in the pandemic outbreak scenario define transportation systems and other geographic conditions, e.g. location of workplaces, schools,
and facilities for sports and entertainment events. The representations also include relational variables, such as individual-mother, -partner, -child, and coworker at workplace. These relational variables are directly relevant for representation of the social networks transmitting infectious agents. In other words,
sociogeographical preprocessing of spatially explicit population data are used to in advance identify specific groups and populations that may require more
careful and intensified surveillance. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations (1) on what
should be included in accurate reports of observational studies are used to organize the communication of evidence from the analyses.
Realization The scenario management is based on the ontology handling system Protégé (2). In addition to the SVERIGE (System for Visualizing Economic
and Regional Influences Governing the Environment) model for sociogeographical representation of the Swedish population (3), preliminary settings for
additional scenario models have been developed, representing, e.g. local social interaction and commuting patterns (4).
Access to epidemiological and population data
Problem-Requirements: A2 Control and visualization of data quality and timeliness
Design solution Epidemiological data from factual outbreaks are complemented with artificially generated data and collected into databases for use in detailed
analyses of historical outbreaks and experiments on hypothetical outbreaks in populations. The factual outbreak data range from highly specific genomic and
microbiological laboratory data to non-specific syndromic data, e.g. from telephone health advice centers and Internet website logs (5, 6). All data sources are
controlled by methods for systematic statistical follow-up of the data used. In particular short-term trends in the pandemic progress can easily be masked by
errors in sampling or laboratory practices. Statistical tools for trend analysis, such as semiparametric regression models (7), are therefore used to identify
causes to flaws in the data collection routines that can lead to erroneous interpretations.
Realization Population-based administrative healthcare databases (8) are used to assemble geographically explicit data from infections disease outbreaks at
local levels. In Sweden, the regional telephone health advice services have been synchronized into a national call center supported by a telehealth Electronic
Patient Record (EPR), where the reason for contact and residence for each caller is documented using a controlled terminology. We use the national database
collecting data from all regional telehealth EPRs as a source for syndromic surveillance data. Regarding visualization services, these have not been included
in the present realization. Instead, interactive graphs (www.ggobi.org) and motion chart (www.gapminder.org) services available at the Internet are used for
getting overviews of large data sets.
References
1. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative. The Strengthening the Reporting of Observational
Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008 Apr;61(4):344-9.
2. Gennari JH, Musen MA, Fergerson RW, Grosso WE, Crubézy M, Eriksson H, et al. The evolution of Protégé: An environment for knowledge-based systems
development. Int J Hum Comp Stud 2003;58(1):89-123.
3. Holm E, Holme K, Mäkilä K, Mattsson-Kauppi M, Mörtvik G. The SVERIGE Spatial Microsimulation Model: Content, Validation, and Example Applications.
GERUM 2002;4.
4. Holm E, Timpka T. A discrete time-space geography for epidemiology: from mixing groups to pockets of local order in pandemic simulations. Stud Health
Technol Inform 2007;129:464-8.
5. Sintchenko V, Gallego B. Laboratory-guided detection of disease outbreaks:three generations of surveillance systems. Arch Pathol Lab Med. 2009
Jun;133(6):916-25.
6. Smith GJ, Vijaykrishna D, Bahl J, Lycett SJ, Worobey M, Pybus OG, Ma SK, Cheung CL, Raghwani J, Bhatt S, Peiris JS, Guan Y, Rambaut A. Origins and
evolutionary genomics of the 2009 swine-origin H1N1 influenza A epidemic. Nature. 2009 Jun 11. [Epub ahead of print] PubMed PMID: 19516283.
6. Wahlin K, Grimvall A. Uncertainty in water quality data and its implications for trend detection: lessons from Swedish environmental data. Environmental
Science and Policy 2008;11:115-124.
7. Wirehn AB, Karlsson HM, Carstensen JM. Estimating disease prevalence using a population-based administrative healthcare database. Scand J Public
Health 2007;35(4):424-31.
Results
 The functions for Implementation process evaluation are
supported by technical systems for response process analysis
and knowledge-base maintenance (Table 2).
Response program evidence
SQUIRE guidelines
STROBE guidelines
Evidence level
Functional
level
Technical
system level
Table 3
Table 2
Table 1
Program context
Process design
Effectiveness
Capacity and needs
assessment
Implementation process
evaluation
Outcome and impact evaluation
A1
A2
Analysis scenario
management
Access to
epidemiological
and population
data
Ontology
management
system
Social
geographic
population
database
Population
based
administrative
healthcare
databases
Laboratory
and
syndromic
databases
I2
A3,I2
Iterative response
process analyses
Disease
model
repositories
Maintenance of
knowledge base
on intervention
program design
Ontology
management
system
Program
databases
D1,I
D,I2
Comparative
analyses of
outbreak detection
alorithms
Statistical
analysis
packages
Comparative
analyses of
intervention
effectiveness
Social
geographic
population
database
Population
based
administrative
healthcare
databases
Laboratory
and
syndromic
databases
Simulation
software
Results
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Table 2. PROSPER design pattern Implementation process evaluation (section)
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Implementation process evaluation
Iterative response program implementation
Problem-Requirements: A3 Explicit representation of populations over time, D1 Adjustments to missing data, I2 Explicit fact and
hypothesis management
Functional design An iterative procedure for response program design was envisioned, where analyses of virtual outbreak
detection and simulated interventions are used until real-time surveillance data and evaluations of factual interventions become
available. The disease models used in the virtual analyses are preliminarily instantiated from the literature, e.g. with regard to
incubation period and serial interval. Thereafter, program components are specified in intervention models. Response program
developers can prepare process analyses by configuring program components and specifying intervention model parameters,
e.g. the prophylactic performance of specific antiviral drugs or combinations. The SQUIRE guidelines for reporting studies of
quality improvement in health services (1) are used for communication of evidence from the analyses.
Realization The engineering principle underlying the example system is separation between the software for management of the
outbreak models and the software for execution of the analyses (2). This separation allows flexible modeling to represent
unexpected events and circumstances, while maintaining the run-time performance of outbreak detection and simulation
programs. Basic disease and intervention characteristics are available from profiles reported in the literature (3) and at the
Internet (https://www.epimodels.org/midas/modelProfilesFull.do). The basic models are combined and for a typology of explicit
models and baseline parameter settings.
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References
1. Davidoff F, Batalden P, Stevens D, Ogrinc G, Mooney S; SQUIRE development group. Publication guidelines for quality
improvement in health care: evolution of the SQUIRE project. Qual Saf Health Care. 2008 Oct;17 Suppl 1:i3-9.
2. Eriksson H, Morin M, Jenvald J, Gursky E, Holm E, Timpka T. Ontology based modeling of pandemic simulation scenarios.
Stud Health Technol Inform 2007;129:755-9.
3. Carrat F, Vergu E, Ferguson NM, Lemaitre M, Cauchemez S, Leach S, et al. Time lines of infection and disease in human
influenza: a review of volunteer challenge studies. Am J Epidemiol 2008;167(7):775-85.
Results
 The last section of the protocol, Outcome and impact evaluation
outlines the details for the comparative analyses of outbreak
algorithms and the assessment of intervention effectiveness
(Table 3).
Response program evidence
SQUIRE guidelines
STROBE guidelines
Evidence level
Functional
level
Technical
system level
Table 3
Table 2
Table 1
Program context
Process design
Effectiveness
Capacity and needs
assessment
Implementation process
evaluation
Outcome and impact evaluation
A1
A2
Analysis scenario
management
Access to
epidemiological
and population
data
Ontology
management
system
Social
geographic
population
database
Population
based
administrative
healthcare
databases
Laboratory
and
syndromic
databases
I2
A3,I2
Iterative response
process analyses
Disease
model
repositories
Maintenance of
knowledge base
on intervention
program design
Ontology
management
system
Program
databases
D1,I
D,I2
Comparative
analyses of
outbreak detection
alorithms
Statistical
analysis
packages
Comparative
analyses of
intervention
effectiveness
Social
geographic
population
database
Population
based
administrative
healthcare
databases
Laboratory
and
syndromic
databases
Simulation
software
Results
 The functions for comparative analyses in the outcome and
impact evaluation section are based on technical systems for
simulations and statistical analyses that can be acquired
without major financial investments, by utilizing knowledge
based systems techniques and networked cloud computing
PROSPER examples
A two-tier model of epidemic progression:
the biological tier
Sick,
staying
home
Susceptible
infection
Incubation
Sick, not
staying
home
Recovered
No
symptom
Incubation 1-3 days
(average 1.9 days)
Contagious 3-6 days
(average 4.1 days)
PROSPER examples
A two-tier model of epidemic progression:
the sociogeographical tier
 Geographical
 Logistical
Community
 Social
Child care
Workplace
 Cultural
Neighborhood
Household
School
 Mixing network approach to represent meeting places as social pockets
 Households are central social pockets
Day rest with
symptoms
Decision modifiers
Personal care resources
Healthcare access
Health beliefs
Withdrawal
from normal
social
interaction
Individual person
Decision to perform ordinary
daily activities
Biological data and assumptions
regarding virus performance
Symptoms
development
Biological simulation tier
Non-intervenable conditions
modifying pandemic spread
Intervenable mechanisms
mediating pandemic spread
Individual person
Sex
Age
Genetic constitution
Ethnicity
Formal education
Employment
Physical environment
Climate
Urbanisation level
Transportation network
Social environment
Community
Market and economy
Social capital
Family
Financial resources
Family structure and roles
Social network
Individual person
Prevention knowledge
Compliance to policies
Self-protective behaviour
Immunization
Nutritional status
Physical environment
Healthcare facilities
Schools
Workplaces
Sanitary standard
Information infrastructure
Social environment
Community
Laws and regulations
Pandemic plans
Mass media
Family
Family behaviour
Infected
Agent-vector
Contagiousness
Virulence
Individual person
Regular social interaction
in personal social ‘pockets’
in the community
Exposure to infectious individuals
Not infected
Withdrawal
from normal
social
interaction
Day rest
Decision modifiers
Health beliefs
Mental models
-of pandemic
-of society
Social geografic simulation tier
Social geographic data and assumptions
regarding social order
Asymptomatic
infection
PROSPER examples
Management of two-tier simulation models
- the PROTEGÉ ontology handling system
PROSPER examples
Job
Repository
Result
Repository
Cloud computing for outsourcing of complex computations
Scenario developer
XML
Scenario:
Indata XML
Results XML
Assumptions XML
Simulation manager
XML
XML
Protégé manager
AmaCondA Web interface
Assumptions and settings
<Java> Report generator
SQL
Condor
Scenario ontology
Users
Jobs
Results?
Community
model
Amazon workers
Local workers
Disease
model
Simulator
Simulator
Intervention
model
Simulations are defined in the ontology management system and thereafter
distributed to anetworked computing environment
Responding to the current pandemic and preparing for future ones requires critical
planning for the early phases where there is no availability of pandemic vaccine.
WE SET OUT TO COMPUTE A PRELIMINARY NEIGHBORHOOD
INFLUENZA SUSCEPTIBILITY INDEX (NISI) DESCRIBING THE
VULNERABILITY OF LOCAL COMMUNITIES OF DIFFERENT GEOSOCIO-PHYSICAL STRUCTURE TO A PANDEMIC INFLUENZA
OUTBREAK.
The Neighborhood Influenza
Susceptibility Index (NISI)
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The aim was to pre-compute maps describing
local variations between geographical areas
with regard to susceptibility to influenza
transmission.
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The maps can be employed by local public
health officials for planning of response
measures before factual transmission data are
at hand.
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Specifically, computation of a preliminary
Neighborhood Influenza Susceptibility Index
(NISI) is used to describe the vulnerability of
local communities of different geo-sociophysical structure to a pandemic influenza
outbreak.
In difference to seasonal influenza, the herd
immunity to a pandemic is by definition low,
leading to that disease transmission largely is
determined by the pattern of social contacts in
the community.
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The NISI is estimated from a standardized
virtual outbreak. One person per 1000
individuals in the fully susceptible study
community is randomly selected and infected at
t0, defined to be 9am the first day of the
simulation.
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The rates of secondary infected individuals in
different neighborhoods and the
sociodemographic characteristics of these
cases are thereafter recorded during the
progress of the outbreak.
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Neither behavioral changes nor any further
introduction of infected individuals by
commuting and national or international travel
are expected to take place during the
standardized virtual outbreak.
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The preliminary NISI is finally computed as the
proportion of infected at the end of the virtual
outbreak.
Geographical distribution of neighborhoods in
the study municipality
12000
10000
Cases
8000
6000
4000
2000
0
1
15
29
43
57
71
85
99
113
Days
Epidemic curves for the virtual outbreaks (n=10)
generated for Linköping municipality
2000
1800
1600
Lju
1400
Skä
Kär
Cases
1200
Ryd
Lam
1000
Kun
Sko
Tan
800
Lin
Ber
600
Joh
Ekh
400
Cit
200
0
1
15
29
43
57
71
85
99
113
Days
Epidemic curves for the standardized outbreak
displayed by neighborhood (n=13).
Neigborhood
Ljungsbro
Skäggetorp
Kärna
Ryd
Lambohov
Kungsgatan
Skogsfrid
Tannefors
Linghem
Berga
Johannelund
Ekholmen
City
n
t 14
t 28
t 63
10184
9846
7642
6256
8554
8385
6916
5617
13370
13088
10882
8466
8464
8350
7643
6427
9160
8845
7124
5528
9298
9243
8766
7758
14505
14402
13478
11390
10536
10381
9233
7453
5835
5592
4203
3437
12669
12326
10740
8866
7536
7438
6821
5672
18783
17472
14061
11925
7301
7254
6874
6065
Numbers of uninfected individuals at selected
days of the virtual outbreak displayed by
neighborhood
Neighborhood
NISI
t120
0-6
years
7-18
years
19-65
years
66years
University
education
Employed
Born
abroad
Sickness
pension
Linghem
.41
9
21
60
10
25
82
3
5
Lambohov
Ljungsbro
Kärna
Ekholmen
Skäggetorp
Berga
Tannefors
Johannelund
Ryd
Skogsfrid
City
Kungsgatan
.40
.39
.37
.37
.35
.30
.30
.25
.24
.22
.17
.17
12
8
8
8
9
7
9
6
6
5
4
3
21
21
20
19
16
15
14
12
10
10
6
4
62
58
59
57
61
58
63
58
76
67
73
74
6
13
13
16
15
21
14
23
8
17
18
19
37
28
24
32
14
34
30
29
39
45
37
43
71
82
82
80
55
71
78
76
39
64
72
71
15
4
4
5
26
13
7
9
20
9
11
8
6
5
5
7
11
6
5
6
5
4
4
4
Aggregate-level neighborhood socioeconomic data in percent
displayed by descending NISI/H1N1 t120 (proportion of
infected individuals) at the end of the virtual outbreak.
Discussion
 We have drafted the PROSPER protocol that can be used to
realize a standardized information infrastructure for rapid
production of pandemic response program evidence in different
organizational and technical settings.
 The protocol is optimized with regard to analyses of response
program effectiveness in particular communities and
populations worldwide.
 In areas such as urban planning, pattern languages have been
extensively used to transfer value-bearing design features
between different milieus (1)
1. Alexander C. The timeless way of building. Oxford: Oxford University Press, 1979.
Discussion
 It is necessary to caution against over-interpretation of predictive
modeling results in policy-making settings, even in situations where
different models display similar effectiveness of interventions (1).
 Analyses based on explorative modeling tools of ‘what-if analysis’ type,
such as FluAid and FluSurge/ have previously been used to directly
inform policy recommendations concerning hospital surge capacity (2)
and loss of medical work time (3) when planning pandemic responses.
 These modeling environments are not adjusted to the requirement that
health intervention programs must be evidence-based.
 The PROSPER protocol is specifically adapted to that without being
able to inspect, understand and adjust baseline assumptions, it is not
clear to what extent policy makers will use, let alone trust and rely on,
the analytic resources included in the infrastructure.
1. Halloran ME, Ferguson NM, Eubank S, Longini IM, Jr., Cummings DA, Lewis B, et al. Modeling targeted
layered containment of an influenza pandemic in the United States. Proc Natl Acad Sci U S A 2008.
2. Ten Eyck RP. Ability of regional hospitals to meet projected avian flu pandemic surge capacity requirements.
Prehosp Disaster Med 2008;23(2):103-12.
3. Wilson N, Baker M, Crampton P, Mansoor O. The potential impact of the next influenza pandemic on a
national primary care medical workforce. Hum Resour Health 2005;3:7.
Discussion – limitations
 As for the technical systems level of PROSPER, some items
identified in the requirements analysis were not covered by the
present version of PROSPER.
 A technology that can support reliable, short-term forecasts is
nowcasting, i.e. short-term predictions that rely on straightforward extrapolation of recent observations in time (1).
 Early identification of the virus genome is central in the
response to pandemic influenza (2). The laboratory system
infrastructure is not in detail included in the present version of
PROSPER.
1. Wilson N, Baker M, Crampton P, Mansoor O. The potential impact of the next influenza pandemic on a
national primary care medical workforce. Hum Resour Health 2005;3:7.
2. Sintchenko V, Gallego B. Laboratory-guided detection of disease outbreaks:three generations of surveillance
systems. Arch Pathol Lab Med. 2009 Jun;133(6):916-25.
Discussion
 As for future research on information infrastructures for
infectious disease response programs, studies are needed on
- how evidence is defined and revised as new infectious
diseases progress, and
- how organizational and intellectual factors influence uptake of
evidence in situations when the timeframe for taking preventive
action is short (1).
 To achieve this, methods for evidence syntheses in the areas of
outbreak detection and predictive modeling need to be
established, including definition of criteria for evaluation of
study quality.
1. Eccles MP, Armstrong D, Baker R, Cleary K, Davies H, Davies S, et al. An implementation research agenda.
Implement Sci 2009;4:18.
Conclusions
 The PROSPER protocol has been drafted for establishment of
evidence-based pandemic response also in developing
countries with limited access to advanced technology.
 The protocol is also useful because it facilitates the systematic
study of the aspects of infrastructure and context that forms
barriers to or facilitates response programs. In this way, existing
and future information technologies can more effectively be
summoned for analyses of new infectious diseases.
 It is necessary to establish consensus guidelines specifically for
reporting of evidence derived from predictive modeling related
to infectious disease.