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Predictive analytics of COVID-19 cases and tourist arrivals in ASEAN based on covid-19 cases.
- Source :
- Health & Technology; Nov2022, Vol. 12 Issue 6, p1237-1258, 22p
- Publication Year :
- 2022
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 21907188
- Volume :
- 12
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- Health & Technology
- Publication Type :
- Academic Journal
- Accession number :
- 160294287
- Full Text :
- https://doi.org/10.1007/s12553-022-00701-7