Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
Department of Infectious Disease Epidemiology and Modelling, Division for Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway.
PLoS Comput Biol. 2019 Mar 7;15(3):e1006879. doi: 10.1371/journal.pcbi.1006879. eCollection 2019 Mar.
The world is continuously urbanising, resulting in clusters of densely populated urban areas and more sparsely populated rural areas. We propose a method for generating spatial fields with controllable levels of clustering of the population. We build a synthetic country, and use this method to generate versions of the country with different clustering levels. Combined with a metapopulation model for infectious disease spread, this allows us to in silico explore how urbanisation affects infectious disease spread. In a baseline scenario with no interventions, the underlying population clustering seems to have little effect on the final size and timing of the epidemic. Under within-country restrictions on non-commuting travel, the final size decreases with increased population clustering. The effect of travel restrictions on reducing the final size is larger with higher clustering. The reduction is larger in the more rural areas. Within-country travel restrictions delay the epidemic, and the delay is largest for lower clustering levels. We implemented three different vaccination strategies-uniform vaccination (in space), preferentially vaccinating urban locations and preferentially vaccinating rural locations. The urban and uniform vaccination strategies were most effective in reducing the final size, while the rural vaccination strategy was clearly inferior. Visual inspection of some European countries shows that many countries already have high population clustering. In the future, they will likely become even more clustered. Hence, according to our model, within-country travel restrictions are likely to be less and less effective in delaying epidemics, while they will be more effective in decreasing final sizes. In addition, to minimise final sizes, it is important not to neglect urban locations when distributing vaccines. To our knowledge, this is the first study to systematically investigate the effect of urbanisation on infectious disease spread and in particular, to examine effectiveness of prevention measures as a function of urbanisation.
世界正在不断城市化，导致人口密集的城市区域集群和人口稀疏的农村区域集群。我们提出了一种生成具有可控人口聚类水平的空间场的方法。我们构建了一个合成国家，并使用该方法生成具有不同聚类水平的国家版本。结合传染病传播的复域模型，这使我们能够在计算机上探索城市化如何影响传染病的传播。在没有干预措施的基线情景下，基础人口聚类似乎对疫情的最终规模和时间没有影响。在国内限制非通勤旅行的情况下，随着人口聚类程度的增加，最终规模会减小。旅行限制对降低最终规模的影响随着聚类程度的增加而增大。在农村地区，这种减少的幅度更大。国内旅行限制会延迟疫情，而聚类水平越低，延迟时间越长。我们实施了三种不同的疫苗接种策略——空间均匀接种、优先接种城市地点和优先接种农村地点。城市和均匀接种策略在降低最终规模方面最有效，而农村接种策略明显较差。对一些欧洲国家的直观检查表明，许多国家已经具有很高的人口聚类。在未来，它们可能会变得更加聚类。因此，根据我们的模型，国内旅行限制在延迟疫情方面的效果可能会越来越小，而在降低最终规模方面的效果会越来越大。此外，为了最小化最终规模，在分配疫苗时，重要的是不要忽视城市地区。据我们所知，这是第一项系统研究城市化对传染病传播的影响的研究，特别是研究了作为城市化函数的预防措施的有效性。
