Center for Complex Networks and Systems Research, School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA.
BMC Infect Dis. 2010 Jun 29;10:190. doi: 10.1186/1471-2334-10-190.
In 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.
We 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.
The 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.
The 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.
近年来,人们越来越频繁地使用大规模计算模型来对疫情爆发进行真实模拟。方法学根据研究规模进行了调整,从非常详细的基于代理的模型到具有空间结构的复合种群模型。因此,一个主要问题是不同建模方法发现的时空传播模式可能存在多大差异,以及这种差异取决于所使用的不同近似和假设。
我们首次提供了一个侧面比较,比较了基于意大利大流行基线事件进展的随机基于代理的模型和结构化复合种群随机模型的结果。基于代理的模型基于通过对人口社会经济结构的高度详细数据来明确表示意大利人口。复合种群模拟使用全球流行和流动性(GLEaM)模型,该模型基于全球高分辨率人口普查数据,并整合了全球范围内的航空旅行流量数据和短期的全球人类流动性模式。该模型还考虑了意大利的年龄结构数据。通过使用相同的疾病参数化和从国际旅行定义相同的感染病例输入,GLEaM 和基于代理的模型在其初始条件方面实现了同步。
结果表明,这两种模型在两者都能达到的粒度水平上提供了非常一致的疫情模式,峰值时间差异在几天左右。疫情规模的相对差异取决于基本繁殖数 R0,以及复合种群模型始终比基于代理的模型产生更大的发病率,这是由于两种方法的种群内接触模式结构差异所致。年龄细分分析表明,对于年轻年龄组,获得了相似的攻击率。
两种建模方法之间的良好一致性对于定义数据可用性与模型提供的信息之间的权衡非常重要。我们提出的结果定义了根据可用数据和计算资源组合基于代理和复合种群方法的混合模型的可能性。