Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK. Centre for Environment and Health, School of Public Health, Imperial College, UK.
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK. MathSys CDT, University of Warwick, UK.
Epidemics. 2020 Dec;33:100425. doi: 10.1016/j.epidem.2020.100425. Epub 2020 Nov 26.
Infectious disease epidemiology is increasingly reliant on large-scale computation and inference. Models have guided health policy for epidemics including COVID-19 and Ebola and endemic diseases including malaria and tuberculosis. Yet a coding bug may bias results, yielding incorrect conclusions and actions causing avoidable harm. We are ethically obliged to make our code as free of error as possible. Unit testing is a coding method to avoid such bugs, but it is rarely used in epidemiology. We demonstrate how unit testing can handle the particular quirks of infectious disease models and aim to increase the uptake of this methodology in our field.
传染病流行病学越来越依赖于大规模的计算和推理。模型为包括 COVID-19 和埃博拉在内的传染病以及包括疟疾和结核病在内的地方病的卫生政策提供了指导。然而，一个编码错误可能会使结果产生偏差，得出错误的结论和采取行动，从而造成可避免的伤害。我们有道德义务使我们的代码尽可能没有错误。单元测试是一种避免此类错误的编码方法，但在流行病学中很少使用。我们展示了单元测试如何处理传染病模型的特殊问题，并旨在增加我们领域对这种方法的采用。
