Understanding what determines patterns of infection spread in populations is important for controlling infection transmission. The science that advances this understanding uses mathematical and computer models that vary from deterministic models of continuous populations to models of dynamically evolving contact networks between individuals. These provide insight, serve as scientific theories, help design studies, and help analyze data. The key to their use lies in assessing the robustness of inferences made using them to violation of their simplifying assumptions. This involves changing model forms from deterministic to stochastic and from compartmental to network, as well as adding realistic detail and changing parameter values. Currently inferences about infection transmission are often made using stratified rate or risk comparisons, logistic regression models, or proportionate hazards models that assume an absence of transmission. Robustness assessment will show many of these inferences to be wrong. A community of epidemiologist modelers is needed for effective robustness assessment.

中文翻译：

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模拟感染传播。

了解什么因素决定了感染在人群中的传播方式，对于控制感染的传播很重要。促进这种理解的科学使用数学模型和计算机模型，从连续总体的确定性模型到个人之间动态演化的联系网络的模型，其变化范围很大。这些提供洞察力，充当科学理论，帮助设计研究并帮助分析数据。使用它们的关键在于评估使用它们违反其简化假设的推断的鲁棒性。这涉及将模型形式从确定性更改为随机形式，将模型形式从隔间更改为网络，以及添加实际细节和更改参数值。目前，有关感染传播的推论通常是通过分层比率或风险比较得出的，logistic回归模型或假设没有传播的比例风险模型。健壮性评估将显示许多推断是错误的。需要一个流行病学家建模者社区来进行有效的鲁棒性评估。