自回归积分滑动平均（ARIMA）模型在2019年冠状病毒病疫情数据集上的应用。

Benvenuto Domenico, Giovanetti Marta, Vassallo Lazzaro, Angeletti Silvia, Ciccozzi Massimo

Unit of Medical Statistics and Molecular Epidemiology, University Campus Bio-Medico of Rome, Italy.

Laboratório de Flavivírus, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.

Data Brief. 2020 Feb 26;29:105340. doi: 10.1016/j.dib.2020.105340. eCollection 2020 Apr.

Coronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time.

2019年冠状病毒病（COVID - 2019）已被视为全球威胁，目前正在进行多项研究，使用各种数学模型来预测这一流行病的可能演变。这些基于各种因素和分析的数学模型可能存在潜在偏差。在此，我们提出一个简单的计量经济学模型，它可能有助于预测COVID - 2019的传播。我们对约翰·霍普金斯大学的流行病学数据进行了自回归积分滑动平均（ARIMA）模型预测，以预测COVID - 2019患病率和发病率的流行病学趋势。为了进一步比较或从未来角度考虑，病例定义和数据收集必须实时进行。