Predictive Capacity of COVID-19 Test Positivity Rate.
Predictive Capacity of COVID-19 Test Positivity Rate.
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DOI：
10.3390/s21072435
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发表时间：
2021-04-01
期刊：
影响因子：
--
通讯作者：
Gaspari M
中科院分区：
文献类型：
--
作者：
Fenga L;Gaspari M
新冠肺炎感染可悄悄传播，因为同时存在大量危重和无症状至轻度病例。虽然前者有可靠的数据(以住院和/或重症监护病房床位的形式)，但后者并非如此。因此，应采用旨在生成可靠预测和未来情景的分析工具，以帮助决策者提前计划(例如，医疗结构和设备)。其中一位作者以前的工作表明，检测阳性率(TPR)的另一种公式，即在给定的一天中检测呈阳性的人数的比例，与医院和重症监护病房的入院人数显示出很强的相关性。本文利用季节自回归移动平均模型(SARIMA)研究了新定义的TPR与住院人数时间序列之间的滞后相关结构。所选择的严格的分析框架，即随机过程理论，允许在这些数量之前大约12天进行可靠的预测。拟议的方法还将允许决策者提前12天预测医院和重症监护病房所需的床位数量。研究结果表明，标准化的新冠肺炎疫情监测指标是监测疫情发展的有价值的指标。该指数可以按天计算，它可能是当今预测医院和重症监护病房超载的最佳预测工具之一，是计算简单性和准确性之间的最佳折衷。
COVID-19 infections can spread silently, due to the simultaneous presence of significant numbers of both critical and asymptomatic to mild cases. While, for the former reliable data are available (in the form of number of hospitalization and/or beds in intensive care units), this is not the case of the latter. Hence, analytical tools designed to generate reliable forecast and future scenarios, should be implemented to help decision-makers to plan ahead (e.g., medical structures and equipment). Previous work of one of the authors shows that an alternative formulation of the Test Positivity Rate (TPR), i.e., the proportion of the number of persons tested positive in a given day, exhibits a strong correlation with the number of patients admitted in hospitals and intensive care units. In this paper, we investigate the lagged correlation structure between the newly defined TPR and the hospitalized people time series, exploiting a rigorous statistical model, the Seasonal Auto Regressive Moving Average (SARIMA). The rigorous analytical framework chosen, i.e., the stochastic processes theory, allowed for a reliable forecasting about 12 days ahead of those quantities. The proposed approach would also allow decision-makers to forecast the number of beds in hospitals and intensive care units needed 12 days ahead. The obtained results show that a standardized TPR index is a valuable metric to monitor the growth of the COVID-19 epidemic. The index can be computed on daily basis and it is probably one of the best forecasting tools available today for predicting hospital and intensive care units overload, being an optimal compromise between simplicity of calculation and accuracy.
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影响因子：
9.8
作者：
Hasell J;Mathieu E;Beltekian D;Macdonald B;Giattino C;Ortiz-Ospina E;Roser M;Ritchie H
通讯作者：
Ritchie H
DOI：
10.1016/s0305-0483(99)00033-x
发表时间：
2000-02-01
影响因子：
6.9
作者：
Beynon, M;Curry, B;Morgan, P
通讯作者：
Morgan, P
影响因子：
3
作者：
BOZDOGAN, H
通讯作者：
BOZDOGAN, H
影响因子：
6.8
作者：
AKAIKE, H
通讯作者：
AKAIKE, H
影响因子：
2
作者：
Nobre, FF;Monteiro, ABS;Williamson, GD
通讯作者：
Williamson, GD