Individual-level modeling for infectious disease spread

传染病对整个社会都很重要。流行性感冒、人乳头瘤病毒、SARS或口蹄疫等疾病的爆发可对公共卫生构成直接威胁和/或产生严重的经济影响。为了控制这类疾病，了解疾病如何随着时间的推移在地理上传播，以及哪些因素导致一些人感染，而另一些人则没有感染，往往至关重要。为此，一类新的数学模型，传染病的个体水平模型（ILM），已经开发出来。这些模型直观，灵活，并已被证明可以准确地描述先前观察到的流行病在空间和时间上的模式。然而，ILM是相对较新的，因此有许多关于如何建模各种传染病问题的问题仍有待解决。

Infectious diseases are of great importance across society. Epidemic outbreaks of diseases such as influenza, human papillomavirus (HPV), SARS, or foot-and-mouth disease, can pose direct threats to public health and/or have serious economic effects. In order to control such diseases, it is often vital to understand how the disease spreads geographically over time, and what factors lead to some individuals becoming infected, and some not. To this end, a new class of mathematical models, individual-level models (ILMs) for infectious diseases, has been developed. These models are intuitive, flexible, and have been shown to accurately describe the patterns of previously observed epidemics over space and time. However, ILMs are relatively new and so there are many issues about how to model various infectious disease problems that remain to be resolved.