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PROSPER

Smittsamma sjukdomars inverkan på det svenska samhället: mot evidensbaserade responsstrategier

Toomas Timpka Henrik Eriksson Elin A Gursky Anders Grimvall Joakim Ekberg Olle ErikssonMagnus Strömgren Einar Holm Lars Valter James M Nyce

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 Spanishdisease (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/S1473-3099(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 evidence

Hypothesis testing

Descriptive analysis

(Re) evaluation

Research questions

Empirical observation

Empirical data collection

Policy decisions

Multi-level simulations

ForecastsIntervention experiments

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

Overview of the methods used forA. Data collectionB. Data analysis

Test bed functions

Program analysis scopeExpert panel

Expert panel

Coordinator

Test bed realization

assignments

Test bed requirements

A. Requirements data collection

B. Design pattern specification and test bed realization

Coordinator Expert panel Design patterns

Methods

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):376-80.

Methods

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

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.

Response outcomes

- for individuals- for organization

Context of response

Stages of implementation

Infectious disease

response

Capacity and needs assessmentAnalysis scenario managementAccess to epidemiological and population data

Outcome and impact evaluation Comparative analyses of outbreak detection and intervention effectiveness

Implementation process evaluation

Iterative response process analyses

Maintenance of knowledge base onintervention program design

Response

program process

design

Responseprogram processdesign

Program context Effectiveness Process design

Information infrastructure

defined by PROSPER

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

Analysis scenario management

Access to epidemiological and population

data


Maintenance of knowledge base on intervention program design

Functional level

Technical system level

Ontology manage-

ment system

Social geographic population database

Population based

admini-strative

healthcare databases

Laboratory and

syndromic databases

Disease model

repositories

Ontology manage-

ment system

Program databases

Statistical analysis

packages

Evidence level

Simulation software

A1 A2 A3,I2 I2 D,I2 D1,I


Population based

admini-strative


Laboratory and

syndromic databases

Capacity and needs assessment

Program context

Table 1


Process design

Table 2

Outcome and impact evaluation

Effectiveness

Table 3

Comparative analyses of

outbreak detection alorithms

Comparative analyses of intervention

effectiveness

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).





data



Functional level


Ontology manage-

ment system


Population based admini-strative


Laboratory and

syndromic databases

Disease model

repositories

Ontology manage-

ment system

Program databases


packages

Evidence level

Simulation software

A1 A2 A3,I2 I2 D,I2 D1,I




Laboratory and

syndromic databases


Program context

Table 1


Process design

Table 2


Effectiveness

Table 3




effectiveness

Results 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 co-worker 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).





data



Functional level


Ontology manage-

ment system




Laboratory and

syndromic databases

Disease model

repositories

Ontology manage-

ment system

Program databases


packages

Evidence level

Simulation software

A1 A2 A3,I2 I2 D,I2 D1,I




Laboratory and

syndromic databases


Program context

Table 1


Process design

Table 2


Effectiveness

Table 3




effectiveness

Results

Table 2. PROSPER design pattern Implementation process evaluation (section) 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.

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).





data



Functional level


Ontology manage-

ment system




Laboratory and

syndromic databases

Disease model

repositories

Ontology manage-

ment system

Program databases


packages

Evidence level

Simulation software

A1 A2 A3,I2 I2 D,I2 D1,I




Laboratory and

syndromic databases


Program context

Table 1


Process design

Table 2


Effectiveness

Table 3




effectiveness

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

Incubation 1-3 days (average 1.9 days)

Contagious 3-6 days (average 4.1 days)

Sick, staying home

Sick, not staying home

No symptom

RecoveredIncubationSusceptible infection

PROSPER examples A two-tier model of epidemic progression: the sociogeographical tier

Geographical Logistical Social Cultural

Mixing network approach to represent meeting places as social pockets Households are central social pockets

Neighborhood Household

Child care Workplace

School

Community

Withdrawalfrom normal social interaction

Withdrawal from normal social interaction Infected

Agent-vectorContagiousnessVirulence

Individual personDecision to perform ordinary daily activities

Biological data and assumptions regarding virus performance

Biological simulation tier

Social geografic simulation tier

Non-intervenable conditions modifying pandemic spread Individual personSexAge Genetic constitutionEthnicityFormal educationEmploymentPhysical environmentClimateUrbanisation levelTransportation networkSocial environmentCommunityMarket and economySocial capitalFamilyFinancial resourcesFamily structure and roles Social network

Intervenable mechanisms mediating pandemic spread Individual personPrevention knowledgeCompliance to policiesSelf-protective behaviourImmunization Nutritional statusPhysical environmentHealthcare facilitiesSchoolsWorkplacesSanitary standardInformation infrastructureSocial environment CommunityLaws and regulations Pandemic plansMass media FamilyFamily behaviour

Individual personRegular social interaction in personal social ‘pockets’ in the communityExposure to infectious individuals

Symptoms development

Not infected

Day rest with symptomsDecision modifiersPersonal care resourcesHealthcare accessHealth beliefs

Asymptomaticinfection

Social geographic data and assumptions regarding social order

Day rest Decision modifiersHealth beliefsMental models-of pandemic-of society

PROSPER examples Management of two-tier simulation models - the PROTEGÉ ontology handling system

PROSPER examples Cloud computing for outsourcing of complex computations

Simulations are defined in the ontology management system and thereafter distributed to anetworked computing environment

Scenario ontology

Interventionmodel

Diseasemodel

Communitymodel

Job

Rep

osito

ry

Assumptions and settings

Scenario developerXML

Simulation manager

AmaCondA Web interface

<Java> Report generator

Condor

Amazon workers Local workers

XML

Res

ult

Rep

osito

ry

Simulator Simulator

SQ

L

XML

Protégé manager

Scenario:Indata XMLResults XMLAssumptions XML

UsersJobsResults?

WE SET OUT TO COMPUTE A PRELIMINARY NEIGHBORHOOD INFLUENZA SUSCEPTIBILITY INDEX (NISI) DESCRIBING THE VULNERABILITY OF LOCAL COMMUNITIES OF DIFFERENT GEO-SOCIO-PHYSICAL STRUCTURE TO A PANDEMIC INFLUENZA OUTBREAK.

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.

The Neighborhood Influenza Susceptibility Index (NISI) The aim was to pre-compute maps describing

local variations between geographical areas with regard to susceptibility to influenza transmission.

The maps can be employed by local public health officials for planning of response measures before factual transmission data are at hand.

Specifically, computation of a preliminary Neighborhood Influenza Susceptibility Index (NISI) is used to describe the vulnerability of local communities of different geo-socio-physical 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.

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.

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.

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.

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

Epidemic curves for the virtual outbreaks (n=10) generated for Linköping municipality

0

2000

4000

6000

8000

10000

12000

1 15 29 43 57 71 85 99 113

Days

Case

s

Epidemic curves for the standardized outbreak displayed by neighborhood (n=13).

0

200

400

600

800

1000

1200

1400

1600

1800

2000

1 15 29 43 57 71 85 99 113

Days

Case

s

Lju

Skä

Kär

Ryd

Lam

Kun

Sko

Tan

Lin

Ber

Joh

Ekh

Cit

Numbers of uninfected individuals at selected days of the virtual outbreak displayed by neighborhood

Neigborhood n t 14 t 28 t 63

Ljungsbro10184 9846 7642 6256

Skäggetorp8554 8385 6916 5617

Kärna13370 13088 10882 8466

Ryd8464 8350 7643 6427

Lambohov9160 8845 7124 5528

Kungsgatan9298 9243 8766 7758

Skogsfrid14505 14402 13478 11390

Tannefors10536 10381 9233 7453

Linghem5835 5592 4203 3437

Berga12669 12326 10740 8866

Johannelund7536 7438 6821 5672

Ekholmen18783 17472 14061 11925

City7301 7254 6874 6065

Aggregate-level neighborhood socioeconomic data in percent displayed by descending NISI/H1N1 t120 (proportion of infected individuals) at the end of the virtual outbreak.

Neighborhood NISI t120

0-6 years

7-18 years

19-65 years

66- years

Universityeducation

Employed Born abroad

Sicknesspension

Linghem .41 9 21 60 10 25 82 3 5

Lambohov .4

0 12 21 62 6 37 71 15 6Ljungsbro .39 8 21 58 13 28 82 4 5Kärna .37 8 20 59 13 24 82 4 5Ekholmen .37 8 19 57 16 32 80 5 7Skäggetorp .35 9 16 61 15 14 55 26 11Berga .30 7 15 58 21 34 71 13 6Tannefors .30 9 14 63 14 30 78 7 5Johannelund .25 6 12 58 23 29 76 9 6Ryd .24 6 10 76 8 39 39 20 5Skogsfrid .22 5 10 67 17 45 64 9 4City .17 4 6 73 18 37 72 11 4Kungsgatan .17 3 4 74 19 43 71 8 4

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 straight-forward 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.

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