Abdulmajeed Kabir, Adeleke Monsuru, Popoola Labode
Georgia Institute of Technology, Atlanta, GA, USA.
Osun State University, Osogbo, Nigeria.
Data Brief. 2020 May 8;30:105683. doi: 10.1016/j.dib.2020.105683. eCollection 2020 Jun.
The novel Coronavirus disease (COVID-19) was first identified in Wuhan, China in December 2019 but later spread to other parts of the world. The disease as at the point of writing this paper has been declared a pandemic by the World Health Organization (WHO). The application of mathematical models, artificial intelligence, big data, and similar methodologies are potential tools to predict the extent of the spread and effectiveness of containment strategies to stem the transmission of this disease. In societies with constrained data infrastructures, modeling and forecasting COVID-19 becomes an extremely difficult endeavor. Nonetheless, we propose an online forecasting mechanism that streams data from the Nigeria Center for Disease Control to update the parameters of an ensemble model which in turn provides updated COVID-19 forecasts every 24 hours. The ensemble combines an Auto-Regressive Integrated Moving Average model (ARIMA), Prophet - an additive regression model developed by Facebook, and a Holt-Winters Exponential Smoothing model combined with Generalized Autoregressive Conditional Heteroscedasticity (GARCH). The outcomes of these efforts are expected to provide academic thrust in guiding the policymakers in the deployment of containment strategies and/or assessment of containment interventions in stemming the spread of the disease in Nigeria.
新型冠状病毒病（COVID-19）于2019年12月在中国武汉首次被发现，随后传播到世界其他地区。在撰写本文时，该疾病已被世界卫生组织（WHO）宣布为大流行病。应用数学模型、人工智能、大数据及类似方法是预测疾病传播范围和遏制策略有效性以阻止该疾病传播的潜在工具。在数据基础设施有限的社会中，对COVID-19进行建模和预测是一项极其困难的工作。尽管如此，我们提出一种在线预测机制，该机制从尼日利亚疾病控制中心流式传输数据，以更新一个集成模型的参数，该模型进而每24小时提供一次更新后的COVID-19预测。该集成模型结合了自回归积分移动平均模型（ARIMA）、由Facebook开发的加法回归模型Prophet以及结合广义自回归条件异方差（GARCH）的霍尔特-温特斯指数平滑模型。这些努力的成果有望为指导政策制定者部署遏制策略和/或评估遏制干预措施以阻止该疾病在尼日利亚传播提供学术支持。
