An econometric analysis of SARS and Avian Flu on international tourist arrivals to Asia

McAleer, Michael; Huang, Bing-Wen; Kuo, Hsiao-I; Chen, Chi‐Chung; Chang, Chia‐Lin

doi:10.1016/j.envsoft.2009.07.015

Cited by 90 publications

(48 citation statements)

References 20 publications

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“…Kuo, Chen, Tseng, Ju, and Huang (2008) also used a time series model to investigate the impacts of infectious diseases, including Avian Flu and severe acute respiratory syndrome, on international tourist arrivals in Asia. The empirical results indicated that the numbers of affected cases had a significant impact in the case of SARS (see also Mao, Ding, & Lee, 2010;McAleer, Huang, Kuo, Chen, & Chang, 2010), but for Avian Flu.…”

Section: Literature Reviewmentioning

confidence: 98%

The effects of natural disasters on international tourism: A global analysis

2020

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Tourism is shaped by a wide range of factors and forces, including exogenous ones that have no direct link with the tourism sector. Natural disasters and unexpected events are prime examples of such determining factors, as they have profound effects on individuals and society, and as a result have the potential to affect tourism flows considerably. Several theoretical arguments exist why natural disasters and unexpected events could influence tourist destination choices. However, empirical research to confirm the nature and extent of impacts of disasters on tourism is lacking. To address this gap, this paper incorporates a dataset on natural and man-made disaster events into a model of international tourism flows to evaluate the effect of different types of disasters on international arrivals at the national level. Findings provide evidence that the occurrence of different types of event change tourist flows to varying degrees. Although in some cases a positive effect is estimated, in general the impacts are negative, resulting in reduced tourist arrivals following an event. Understanding the relationship between disaster events and tourism is helpful for destination managers who make critical decisions in relation to recovery, reconstruction and marketing.

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Section: Literature Reviewmentioning

confidence: 98%

The effects of natural disasters on international tourism: A global analysis

2020

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“…For example, the Severe Acute Respiratory Syndrome (SARS) is a serious disease which affected tourism in most Asian countries. Many authors have analyzed the impacts of various disease outbreaks on travel and tourism for various countries using econometric methods (Chen, Jang, & Kim, 2007;Chien & Law, 2003;Kou, Chen, Tseng, Ju, & Huang, 2008;McAleer, Huang, Kuo, Chen, & Chang, 2010;McKercher & Chon, 2004;WTTC, 2003;Wilder-Smith, 2006;Tung & Chao, 2011;Oukil, Channouf, & Al-Zaidi, 2016;Cuhadar, 2014). By contrast, Cankurt and Subasi (2016), Song, Li, Witt, andAthanasopoulos (2011), Turner (2015) and many others used structural TS analysis to model and forecast tourist demand.…”

Section: A Brief Review Of Literaturementioning

confidence: 99%

The swine flu and its impacts on tourism in Brunei

Haque

Haque²

2018

Journal of Hospitality and Tourism Management

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“…One of the indirect effects of an outbreak is that it has a detrimental economic impact on a country. This is exemplified by the SARS and avian virus outbreaks in 2003, which led to a huge loss of around US$20 million for the tourism industry in most affected Asian countries such China, Hong Kong, Singapore and Vietnam because of an ineffective surveillance system [6]. For this reason, we need to be able to rely on an accurate detection system to monitor for signs of an emerging outbreak.…”

Section: Introductionmentioning

confidence: 99%

Outbreak detection model based on danger theory

Mohsin

Bakar

Hamdan

2014

Applied Soft Computing

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a b s t r a c tIn outbreak detection, one of the key issues is the need to deal with the weakness of early outbreak signals because this causes the detection model to have has less capability in terms of robustness when unseen outbreak patterns vary from those in the trained model. As a result, an imbalance between high detection rate and low false alarm rate occurs. To solve this problem, this study proposes a novel outbreak detection model based on danger theory; a bio-inspired method that replicates how the human body fights pathogens. We propose a signal formalization approach based on cumulative sum and a cumulative mature antigen contact value to suit the outbreak characteristic and danger theory. Two outbreak diseases, dengue and SARS, are subjected to a danger theory algorithm; namely the dendritic cell algorithm. To evaluate the model, four measurement metrics are applied: detection rate, specificity, false alarm rate, and accuracy. From the experiment, the proposed model outperforms the other detection approaches and shows a significant improvement for both diseases outbreak detection. The findings reveal that the robustness of the proposed immune model increases when dealing with inconsistent outbreak signals. The model is able to detect new unknown outbreak patterns and can discriminate between outbreak and non-outbreak cases with a consistent high detection rate, high sensitivity, and lower false alarm rate even without a training phase.

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An econometric analysis of SARS and Avian Flu on international tourist arrivals to Asia

Cited by 90 publications

References 20 publications

The effects of natural disasters on international tourism: A global analysis

The effects of natural disasters on international tourism: A global analysis

The swine flu and its impacts on tourism in Brunei

Outbreak detection model based on danger theory

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