Abstract
BackgroundIn recent years large-scale computational models for the realistic simulation of epidemic outbreaks have been used with increased frequency. Methodologies adapt to the scale of interest and range from very detailed agent-based models to spatially-structured metapopulation models. One major issue thus concerns to what extent the geotemporal spreading pattern found by different modeling approaches may differ and depend on the different approximations and assumptions used.MethodsWe provide for the first time a side-by-side comparison of the results obtained with a stochastic agent-based model and a structured metapopulation stochastic model for the progression of a baseline pandemic event in Italy, a large and geographically heterogeneous European country. The agent-based model is based on the explicit representation of the Italian population through highly detailed data on the socio-demographic structure. The metapopulation simulations use the GLobal Epidemic and Mobility (GLEaM) model, based on high-resolution census data worldwide, and integrating airline travel flow data with short-range human mobility patterns at the global scale. The model also considers age structure data for Italy. GLEaM and the agent-based models are synchronized in their initial conditions by using the same disease parameterization, and by defining the same importation of infected cases from international travels.ResultsThe results obtained show that both models provide epidemic patterns that are in very good agreement at the granularity levels accessible by both approaches, with differences in peak timing on the order of a few days. The relative difference of the epidemic size depends on the basic reproductive ratio, R0, and on the fact that the metapopulation model consistently yields a larger incidence than the agent-based model, as expected due to the differences in the structure in the intra-population contact pattern of the approaches. The age breakdown analysis shows that similar attack rates are obtained for the younger age classes.ConclusionsThe good agreement between the two modeling approaches is very important for defining the tradeoff between data availability and the information provided by the models. The results we present define the possibility of hybrid models combining the agent-based and the metapopulation approaches according to the available data and computational resources.
Highlights
In recent years large-scale computational models for the realistic simulation of epidemic outbreaks have been used with increased frequency
The incidence profiles show that on average the two temporal patterns are in very good agreement, despite the very different data integration and assumptions of the two models
The two peaks are just a few days apart from each other, with GLobal Epidemic and Mobility (GLEaM) on average reaching the peak of the epidemic slightly later than the agent-based model
Summary
In recent years large-scale computational models for the realistic simulation of epidemic outbreaks have been used with increased frequency. Computational approaches for the detailed modeling of epidemic spread in spatially-structured environments make use of a wide array of simulation schemes [1,2]. Agent-based models provide a very rich data scenario, but the computational cost and, most importantly, the need for very detailed input data has limited its use to country level [6,7,8,9,10,11] or continental level [12] scenarios so far. The amount of data to be integrated is less massive than in agent-based frameworks From this perspective, it is clearly important to assess the level of agreement that the two different approaches can provide with respect to the quantities accessible, the respective data needed, and the computational costs associated with both approaches
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