Affiliations 

  • 1 Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia
  • 2 Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia
  • 3 Faculty of Information Technology, Inti International University, Persiaran Perdana BBN Putra Nilai, 71800 Nilai, Negeri Sembilan Malaysia
Health Technol (Berl), 2022;12(6):1237-1258.
PMID: 36246540 DOI: 10.1007/s12553-022-00701-7

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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.