Efficient Simulation of the Spatial Transmission Dynamics of Influenza

Abstract

Early data from the 2009 H1N1 pandemic (H1N1pdm) suggest that previous studies over-estimated the within-country rate of spatial spread of pandemic influenza. As large spatially resolved data sets are constructed, the need for efficient simulation code with which to investigate the spatial patterns of the pandemic becomes clear. Here, we present a significant improvement to the efficiency of an individual-based stochastic disease simulation framework commonly used in multiple previous studies. We quantify the efficiency of the revised algorithm and present an alternative parameterization of the model in terms of the basic reproductive number. We apply the model to the population of Taiwan and demonstrate how the location of the initial seed can influence spatial incidence profiles and the overall spread of the epidemic. Differences in incidence are driven by the relative connectivity of alternate seed locations. The ability to perform efficient simulation allows us to run a batch of simulations and take account of their average in real time. The averaged data are stable and can be used to differentiate spreading patterns that are not readily seen by only conducting a few runs.

A Simple Efficient Technique to Adjust Time Step Size in a Stochastic Discrete Time Agent-based Simulation

“…Also, we give a reasonable account for the observed differences in results from simulations of different time step sizes. We build the two simulation systems (denoted by ALG 1 and ALG 2 for Algorithm 1 and Algorithm 2, respectively) by modifying a simulation system for epidemic spread developed by (Tsai et al, 2010a), keeping all parameters as original except the ones we ex- for each infectious agent I do 4:…”

“…In this section, we describe the basic model of a stochastic discrete time agent-based simulation system, with specific reference to the simulation system developed by (Tsai et al, 2010a) following approach in (Germann et al, 2006). Algorithm 1 provides a high level description of the agent-based simulation model of an epidemic spread.…”

“…The stochastic discrete time simulation model is a useful and efficient way in simulating agent-based activities and has gained significant popularity in modelling many dynamical biological or physical systems in recent years, such as the dynamics of epidemic spread (Tsai et al, 2010a;Riley, 2007). Often time, such simulations can give insights into problems where traditional models are too complicated and analytic results are very difficult or currently impossible to obtain (Tsai et al, 2010a;Germann et al, 2006).…”

“…Often time, such simulations can give insights into problems where traditional models are too complicated and analytic results are very difficult or currently impossible to obtain (Tsai et al, 2010a;Germann et al, 2006). This high level abstraction, however, introduces artifacts which do not pertain to real world behaviour, namely, the discretization of time.…”

“…We have developed a simple, yet efficient, technique based on reasonable assumptions to split each time step into any integral number of equally spaced sub-steps with small increase in simulation time in a stochastic discrete time agent based simulation system for epidemic spread we developed earlier (Tsai et al, 2010a;Tsai et al, 2010b). More precisely, we first modify the probabilities of events according to the basic probability theory such that they correspond to events occurring in a smaller time frame.…”