Abstract：
Influenza epidemics cause considerable morbidity and mortality every year worldwide. Climate-driven epidemiological models are mainstream tools to understand seasonal transmission dynamics and predict future trends of influenza activity, especially in temperate regions. Testing the structural identifiability of these models is a fundamental prerequisite for the model to be applied in practice, by assessing whether the unknown model parameters can be uniquely determined from epidemic data. In this study, we applied a scaling method to analyse the structural identifiability of four types of commonly used humidity-driven epidemiological models. Specifically, we investigated whether the key epidemiological parameters (i.e., infectious period, the average duration of immunity, the average latency period, and the maximum and minimum daily basic reproductive number) can be uniquely determined simultaneously when prevalence data is observable. We found that each model is identifiable when the prevalence of infection is observable. The structural identifiability of these models will lay the foundation for testing practical identifiability in the future using synthetic prevalence data when considering observation noise. In practice, epidemiological models should be examined with caution before using them to estimate model parameters from epidemic data.
摘要：
流感流行每年在世界范围内引起相当大的发病率和死亡率。气候驱动的流行病学模型是了解季节性传播动态和预测未来流感活动趋势的主流工具,尤其是在温带地区。检验这些模型的结构可识别性,是模型在实际应用中的基本前提,通过评估未知模型参数是否可以从流行病数据中唯一确定。在这项研究中,我们应用了一种缩放方法来分析四种常用的湿度驱动流行病学模型的结构可识别性。具体来说,我们调查了关键的流行病学参数(即，感染期,免疫的平均持续时间,平均延迟时间，并且在可观察到患病率数据时，可以同时唯一确定最大和最小每日基本生殖数）。我们发现，当可以观察到感染的患病率时，每个模型都是可识别的。这些模型的结构可识别性将为将来在考虑观测噪声时使用合成患病率数据测试实际可识别性奠定基础。在实践中,在使用流行病学模型从流行病数据估计模型参数之前，应谨慎检查。
