Computational approaches for the detailed modeling of epidemic spread in spatially-structured environments make use of a wide array of simulation schemes [1
]. In recent years, two major classes of methodologies emerged in the simulation of influenza-like illnesses (ILIs) and other emerging infectious diseases. The first one is the very accurate epidemic description with agent-based models, which keep track of each individual in the population in an extremely detailed way [3
]. The second scheme relies on metapopulation structured models that consider in a detailed way the long range mobility scheme at the inter-population level while using coarse-grained techniques at the intra-population level [15
]. 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
] or continental level [12
] scenarios so far. On the opposite side, the structured metapopulation models are fairly scalable and can be conveniently used to provide worldwide scenarios and patterns with thousands of stochastic realizations [18
]. While on the one hand, the level of information that can be extracted in this latter case is less detailed than those of agent-based models, the spatial and temporal ranges and the number of realizations that can be computationally analyzed is much larger. Also, 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.
Comparing different models is often a hard task. While on one side one would like to assess the role of the differences inherent to each of the modeling frameworks, it is important to establish a common ground between the two frameworks in order to discount unwanted effects due to different parameterization (see for example the discussion of the estimation of the reproductive number for the SARS epidemic obtained from a variety of models in Ref. [26
]). An attempt in this direction was presented in Ref. [10
] where three individual-based models with different assumptions and data - one at the description level of a city and two at the description level of a country - were compared through their predictions in the case of interventions against a new pandemic influenza strain. However, the comparison was constrained to each model's assumptions and to the available simulated scenarios, without explicitly defining a common set of parameters and approximations to be shared by all models. The low transmission scenario was compared in different models by using different values for the reproductive number, with the risk of not being able to discount the effect of this difference in the obtained results.
Here we provide for the first time a side-by-side comparison of the results obtained at the level of a single country by using state-of-the-art structured metapopulation and agent-based models developed independently and employed in previous works to analyze pandemic events [8
]. Both models have been used in realistic scenarios [14
] and incorporating actual data in relation to the H1N1 pandemic [24
]. However, comparing simulation results with real data would require a thorough discussion and analysis of the disease parameters, the identification of the initial conditions, the assessment of the reliability of reporting and notification systems that are the sources of the empirical data. This is not the object of this paper. Instead, we focus on the differences generated by the two modeling approaches.
For the sake of clarity we compare the two models in a clean synthetic experiment of a hypothetical pandemic event for which we assume the same parameterization with regards to the modeling aspects that the models share, such as disease progression and initial conditions. The country used for the study is Italy, a large European country that provides the necessary geographic and population heterogeneity to assess the models' performance in the case of highly-structured populations. The two approaches access different granularity levels and we use as a comparison the finer spatial resolution accessible by both models. This allows us to analyze 39 major subpopulations and project data at the administrative level of municipality.
We find that both models, despite the difference in the data integration and model structure, provide epidemic profiles with spatio-temporal patterns in very good agreement. The epidemic size profile shows an expected overall mismatch of 5-10% depending on the reproductive rate, which is induced by the homogeneous assumption of the metapopulation strategy. Breaking down data at the level of age-structured compartments shows that both models provide very similar results with the exception of the elderly population (60 + age bracket), which show larger epidemic sizes in the metapopulation approach. The good agreement of the two approaches reinforces the message that computational approaches are stable with respect to different data integration strategies and modeling assumption. On the other hand, the agent-based model approach may access information not available to the coarser metapopulation approach, and relevant for individually based or targeted intervention measures. This is at the price of a higher computational cost and the availability of fine resolution data, whereas the metapopulation approach is less dependent on detailed data and is computationally cheaper. The presented results hint to the possibility of combining the two methodologies in order to devise multiscale approaches that use the data parsimony of the metapopulation approaches at the global level and the high resolution of the agent-based model in specific locations of interest where detailed data are available.