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基于新冠肺炎病例对东盟国家新冠肺炎病例和游客到访情况的预测分析。

Predictive analytics of COVID-19 cases and tourist arrivals in ASEAN based on covid-19 cases.

作者信息

Velu Shubashini Rathina, Ravi Vinayakumar, Tabianan Kayalvily

机构信息

Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia.

Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia.

出版信息

Health Technol (Berl). 2022;12(6):1237-1258. doi: 10.1007/s12553-022-00701-7. Epub 2022 Oct 8.

DOI:10.1007/s12553-022-00701-7
PMID:36246540
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9546420/
Abstract

PURPOSE

Research into predictive analytics, which helps predict future values using historical data, is crucial. In order to foresee future instances of COVID-19, a method based on the Seasonal ARIMA (SARIMA) model is proposed here. Additionally, the suggested model is able to predict tourist arrivals in the tourism business by factoring in COVID-19 during the pandemic. In this paper, we present a model that uses time-series analysis to predict the impact of a pandemic event, in this case the spread of the Coronavirus pandemic (Covid-19).

METHODS

The proposed approach outperformed the Autoregressive Integrated Moving Average (ARIMA) and Holt Winters models in all experiments for forecasting future values using COVID-19 and tourism datasets, with the lowest mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE), and root mean squared error (RMSE). The SARIMA model predicts COVID-19 and tourist arrivals with and without the COVID-19 pandemic with less than 5% MAPE error.

RESULTS

The suggested method provides a dashboard that shows COVID-19 and tourism-related information to end users. The suggested tool can be deployed in the healthcare, tourism, and government sectors to monitor the number of COVID-19 cases and determine the correlation between COVID-19 cases and tourism.

CONCLUSION

Management in the tourism industries and stakeholders are expected to benefit from this study in making decisions about whether or not to keep funding a given tourism business. The datasets, codes, and all the experiments are available for further research, and details are included in the appendix.

摘要

目的

预测性分析研究至关重要，它有助于利用历史数据预测未来值。为了预见新冠肺炎的未来情况，本文提出一种基于季节性自回归整合移动平均（SARIMA）模型的方法。此外，所建议的模型能够通过考虑疫情期间的新冠肺炎情况来预测旅游业的游客到访量。在本文中，我们提出一种使用时间序列分析来预测大流行事件影响的模型，在这种情况下是冠状病毒大流行（新冠肺炎）的传播。

方法

在所有使用新冠肺炎和旅游数据集预测未来值的实验中，所提出的方法优于自回归整合移动平均（ARIMA）模型和霍尔特·温特斯模型，具有最低的平均绝对误差（MAE）、平均绝对百分比误差（MAPE）、均方误差（MSE）和均方根误差（RMSE）。SARIMA模型预测有和没有新冠肺炎疫情情况下的新冠肺炎和游客到访量时，MAPE误差小于5%。

结果

所建议的方法提供了一个向终端用户展示新冠肺炎和旅游相关信息的仪表板。所建议的工具可部署在医疗保健、旅游和政府部门，以监测新冠肺炎病例数量并确定新冠肺炎病例与旅游业之间的相关性。

结论

旅游业的管理层和利益相关者有望从本研究中受益，从而在是否继续为特定旅游业务提供资金方面做出决策。数据集、代码和所有实验可供进一步研究，详细信息包含在附录中。

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