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Pandemic simulation is a useful tool for analyzing outbreaks and exploring the impact of variations in disease, population, and intervention models. Unfortunately, this type of simulation can be quite time-consuming especially for large models and significant outbreaks, which makes it difficult to run the simulations interactively and to use simulation for decision support during ongoing outbreaks. Improved run-time performance enables new applications of pandemic simulations, and can potentially allow decision makers to explore different scenarios and intervention effects. Parallelization of infection-probability calculations and multicore architectures can take advantage of modern processors to achieve significant run-time performance improvements. However, because of the varying computational load during each simulation run, which originates from the changing number of infectious persons during the outbreak, it is not useful to us the same multicore setup during the simulation run. The best performance can be achieved by dynamically changing the use of the available processor cores to balance the overhead of multithreading with the performance gains of parallelization.

Simulation is an important resource for studying the dynamics of pandemic influenza and predicting the potential impact of interventions. However, there are several challenges for the design of such simulator architectures. Specifically, it is difficult to develop simulators that combine flexibility with run-time performance. This tradeoff is problematic in the pandemic-response setting because it makes it challenging to extend and adapt simulators for ongoing situations where rapid results are indispensable. Simulation architectures based on aspect-oriented programming can model specific concerns of the simulator and can allow developers to rapidly extend the simulator in new ways without sacrificing run-time performance. It is possible to use such aspects in conjunction with separate simulation models, which define community, disease, and intervention properties. The implication of this research for pandemic response is that aspects can add a novel layer of flexibility to simulation environments, which enables modelers to extend the simulator run-time component to new requirements that go beyond the original modeling framework.

The global spread of a novel A (H1N1) influenza virus in 2009 has highlighted the possibility of a devastating pandemic similar to the 'Spanish flu' of 1917-1918. Responding to such pandemics requires careful planning for the early phases where there is no availability of pandemic vaccine. We set out to compute a Neighborhood Influenza Susceptibility Index (NISI) describing the vulnerability of local communities of different geo-socio-physical structure to a pandemic influenza outbreak. We used a spatially explicit geo-physical model of Linköping municipality (pop. 136,240) in Sweden, and employed an ontology-modeling tool to define simulation models and transmission settings. We found considerable differences in NISI between neighborhoods corresponding to primary care areas with regard to early progress of the outbreak, as well as in terms of the total accumulated share of infected residents counted after the outbreak. The NISI can be used in local preparations of physical response measures during pandemics.

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.