Riley Steven
Department of Community Medicine and School of Public Health, Faculty of Medicine, University of Hong Kong, Hong Kong, Special Administrative Region, People's Republic of China.
Science. 2007 Jun 1;316(5829):1298-301. doi: 10.1126/science.1134695.
During transmission of seasonal endemic diseases such as measles and influenza, spatial waves of infection have been observed between large distant populations. Also, during the initial stages of an outbreak of a new or reemerging pathogen, disease incidence tends to occur in spatial clusters, which makes containment possible if you can predict the subsequent spread of disease. Spatial models are being used with increasing frequency to help characterize these large-scale patterns and to evaluate the impact of interventions. Here, I review several recent studies on four diseases that show the benefits of different methodologies: measles (patch models), foot-and-mouth disease (distance-transmission models), pandemic influenza (multigroup models), and smallpox (network models). This review highlights the importance of the household in spatial studies of human diseases, such as smallpox and influenza. It also demonstrates the need to develop a simple model of household demographics, so that these large-scale models can be extended to the investigation of long-time scale human pathogens, such as tuberculosis and HIV.
在麻疹和流感等季节性地方病的传播过程中，已观察到远距离大群体之间存在感染的空间波。此外，在新出现或再次出现的病原体爆发的初始阶段，疾病发病率往往呈空间聚集性出现，这使得如果你能预测疾病随后的传播，就有可能控制疫情。空间模型的使用频率越来越高，以帮助描述这些大规模模式并评估干预措施的影响。在此，我回顾了最近关于四种疾病的几项研究，这些研究展示了不同方法的益处：麻疹（斑块模型）、口蹄疫（距离传播模型）、大流行性流感（多群体模型）和天花（网络模型）。这篇综述强调了家庭在人类疾病空间研究（如天花和流感）中的重要性。它还表明需要开发一个简单的家庭人口统计学模型，以便这些大规模模型能够扩展到对长期存在的人类病原体（如结核病和艾滋病毒）的研究。
