Population-based simulations of influenza pandemics: validity and significance for public health policy.

OBJECTIVE: To examine the validity and usefulness of pandemic simulations aimed at informing practical decision-making in public health. METHODS: We recruited a multidisciplinary group of nine experts to assess a case-study simulation of influenza transmission in a Swedish county. We used a non-statistical nominal group technique to generate evaluations of the plausibility, formal validity (verification) and predictive validity of the simulation. A health-effect assessment structure was used as a framework for data collection. FINDINGS: The unpredictability of social order during disasters was not adequately addressed by simulation methods; even minor disruptions of the social order may invalidate key infrastructural assumptions underpinning current pandemic simulation models. Further, a direct relationship between model flexibility and computation time was noted. Consequently, simulation methods cannot, in practice, support integrated modifications of microbiological, epidemiological and spatial submodels or handle multiple parallel scenarios. CONCLUSION: The combination of incomplete surveillance data and simulation methods that neglect social dynamics limits the ability of national public health agencies to provide policy-makers and the general public with the critical and timely information needed during a pandemic.

Impact of precautionary behaviors during outbreaks of pandemic influenza: modeling of regional differences.

Using time geographic theory for representation of population mixing, we set out to analyze the relative impact from precautionary behaviors on outbreaks of pandemic influenza in Europe and Asia. We extended an existing simulator environment with behavioral parameters from a population survey to model different behaviors. We found that precautionary behaviors even among a minority of the population can have a decisive effect on the probability of the outbreak to propagate. The results also display that assumptions strongly influences the outcome. Depending on the interpretation of how many "children" are kept from "school", R(0) changes from a range where outbreak progression is possible to a range where it is improbable in both European (R(0)=1.77/1.23) and Asian (R(0)=1.70/1.05) conditions. We conclude that unprompted distancing can have a decisive effect on pandemic propagation. An important response strategy can be to promote voluntary precautionary behavior shown to reduce disease transmission.

Assumptions management in simulation of infectious disease outbreaks.

Simulation of outbreaks of infectious disease is an important tool for understanding the dynamics of the outbreak process, the impact of disease and population properties, and the potential effect of interventions. However, the interpretation of the simulation results requires a clear understanding of the assumptions made in the underlying model. Typical simulation tasks, such as exploring the space of different scenarios for population and disease properties, require multiple runs with varying model parameters. For such complex tasks, the management of the assumptions made becomes a daunting and potentially error-prone undertaking. We report explicit assumptions management as an approach to capture, model, and document the assumptions for simulator runs. It was found possible to extend ontology-based simulation, which uses an ontological model to parameterize the simulator, to incorporate an assumptions model in the ontology. We conclude that explicit assumptions modeling should be part of any infectious disease simulation architecture from start.

Ontology based modeling of pandemic simulation scenarios.

Computer-based simulation of influenza outbreaks in local communities can help researchers, epidemiologists, and decision makers better understand the impact of the community structure on the reproduction rate of disease, and the relative benefits of different types of prevention and interventions. The goal of scenario modeling is to develop a description of scenario components, such as the disease, the community, and interventions. An ontology-based representation of the scenario model together with a modeling tool, which is based on an extension to Protégé, assist scenario developers in formulating simulation specifications. This approach allows the exploration of new ideas by rapidly formulating and reconstructing scenarios from novel components.

Towards a simulation environment for modeling of local influenza outbreaks.

OBJECTIVE: To analyze the design of a simulation environment for dynamic prediction of influenza transmission in local communities. METHODS: The technique trade-off method was used to identify and analyze basic design requirements on a simulation environment for modeling of influenza transmission. Data were collected through literature review and interviews with infectious disease experts. The identified requirements were matched to a set of design issues for the simulation environment,and a high-resolution prototype was implemented. RESULTS: Basic reproductive numbers for influenza transmission in a set of Swedish municipalities were calculated. Tradeoffs were necessary in the design between a focus on reproductive numbers vs. case fatality proportions, algorithm validity vs. model adaptability, and specificity in population description vs. generalizability. CONCLUSION: Computer-based simulations can become important tools for local authorities preparing for influenza outbreaks. Balanced tradeoffs between model detail and public health effectiveness are important in simulation environment design.