Identifying the measurements required to estimate rates of COVID-19 transmission, infection, and detection, using variational data assimilation
Identifying the measurements required to estimate rates of COVID-19 transmission, infection, and detection, using variational data assimilation
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DOI：
10.1101/2020.05.27.20112987
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发表时间：
2020-05
影响因子：
8.8
通讯作者：
Eve Armstrong;Manuela Runge;J. Gerardin
中科院分区：
文献类型：
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作者：
Eve Armstrong;Manuela Runge;J. Gerardin
我们证明了统计数据同化（SDA）在新型冠状病毒疾病COVID-19的流行病学模型中识别精确状态和参数估计所需的测量值的能力。我们的背景是努力告知有关社会行为的政策，以减轻对医院能力的压力。模型的未知数被认为是：随时间变化的传播率，暴露的情况下，需要住院治疗的分数，以及随时间变化的新的无症状和有症状的病例的检测概率。在模拟中，我们通过测量已发现的病例以及康复者和死亡者来获得未发现（即未测量）的感染人群的估计值-并且不需要假设检测率的知识。鉴于恢复的人口的无噪声测量，所有数量的出色的估计，获得使用时间基线101天，与时间变化的传输率在实施社会距离之前的时间除外。随着低噪声添加到恢复的人口，准确的状态估计需要延长测量的时间基线。所有参数的估计值都对污染敏感，突出表明需要准确和统一的报告方法。本文旨在验证SDA的能力，以确定在一个具有捕捉COVID-19大流行重要特征所需复杂性的模型中，测量的哪些属性将产生对未知参数的估计，以达到所需精度。
We demonstrate the ability of statistical data assimilation (SDA) to identify the measurements required for accurate state and parameter estimation in an epidemiological model for the novel coronavirus disease COVID-19. Our context is an effort to inform policy regarding social behavior, to mitigate strain on hospital capacity. The model unknowns are taken to be: the time-varying transmission rate, the fraction of exposed cases that require hospitalization, and the time-varying detection probabilities of new asymptomatic and symptomatic cases. In simulations, we obtain estimates of undetected (that is, unmeasured) infectious populations, by measuring the detected cases together with the recovered and dead - and without assumed knowledge of the detection rates. Given a noiseless measurement of the recovered population, excellent estimates of all quantities are obtained using a temporal baseline of 101 days, with the exception of the time-varying transmission rate at times prior to the implementation of social distancing. With low noise added to the recovered population, accurate state estimates require a lengthening of the temporal baseline of measurements. Estimates of all parameters are sensitive to the contamination, highlighting the need for accurate and uniform methods of reporting. The aim of this paper is to exemplify the power of SDA to determine what properties of measurements will yield estimates of unknown parameters to a desired precision, in a model with the complexity required to capture important features of the COVID-19 pandemic.