Recent outbreaks of monkeypox and Ebola, and worrying waves of COVID-19, influenza and respiratory syncytial virus, have all led to a sharp increase in the use of epidemiological models to estimate key epidemiological parameters. The feasibility of this estimation task is known as the practical identifiability (PI) problem. Here, we investigate the PI of eight commonly reported statistics of the classic Susceptible-Infectious-Recovered model using a new measure that shows how much a researcher can expect to learn in a model-based Bayesian analysis of prevalence data. Our findings show that the basic reproductive number and final outbreak size are often poorly identified, with learning exceeding that of individual model parameters only in the early stages of an outbreak. The peak intensity, peak timing, and initial growth rate are better identified, being in expectation over 20 times more probable having seen the data by the time the underlying outbreak peaks. We then test PI for a variety of true parameter combinations, and find that PI is especially problematic in slow-growing or less-severe outbreaks. These results add to the growing body of literature questioning the reliability of inferences from epidemiological models when limited data are available.

翻译：最近爆发的天花和埃博拉,以及令人担忧的COVID-19、流感和呼吸道同步病毒的浪潮,都导致使用流行病学模型来估计主要流行病学参数的情况急剧增加。这一估计任务的可行性被称为实际的可识别性(PI)问题。在这里,我们调查了八种常见报告的典型可感知传染病复发模型的八种统计数据的PI。我们使用一种新的测量方法,显示研究人员在以模型为基础的巴伊西亚病流行数据分析中能够期望学到多少东西。我们的调查结果显示,基本生殖数和最终爆发规模往往没有很好地确定,只是在爆发的早期阶段才发现单个模型参数参数参数参数参数参数参数参数参数参数参数参数。这些结果使得越来越多的文献更加质疑在有限的数据存在时从流行病学模型推断出来的可靠性。