Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge CB3 0WA, UK.
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford OX3 7LF, UK.
Philos Trans R Soc Lond B Biol Sci. 2021 Jul 19;376(1829):20200263. doi: 10.1098/rstb.2020.0263. Epub 2021 May 31.
Analytical expressions and approximations from simple models have performed a pivotal role in our understanding of infectious disease epidemiology. During the current COVID-19 pandemic, while there has been proliferation of increasingly complex models, still the most basic models have provided the core framework for our thinking and interpreting policy decisions. Here, classic results are presented that give insights into both the role of transmission-reducing interventions (such as social distancing) in controlling an emerging epidemic, and also what would happen if insufficient control is applied. Though these are simple results from the most basic of epidemic models, they give valuable benchmarks for comparison with the outputs of more complex modelling approaches. This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'.
从简单模型中得出的分析表达式和近似值在我们对传染病流行病学的理解中发挥了关键作用。在当前的 COVID-19 大流行期间，尽管越来越复杂的模型不断涌现，但最基本的模型仍然为我们的思考和解释政策决策提供了核心框架。在这里，呈现了经典的结果，深入了解了减少传播干预（如社交距离）在控制新兴传染病中的作用，以及如果控制不足会发生什么情况。虽然这些是最基本的传染病模型的简单结果，但它们为与更复杂的建模方法的输出进行比较提供了有价值的基准。本文是“塑造英国早期 COVID-19 大流行应对措施的模型”主题特刊的一部分。
