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Recently, the tourism industry in Asian countries has been adversely affected by two significant drivers: health emergencies and climatic changes. Virus outbreaks such as severe acute respiratory syndrome (SARS), Middle East respiratory syndrome coronavirus (MERS-CoV), Ebola, avian flu, Zika virus and H1N1 influenza virus have caused much greater damage to the tourism and travel industry of Asian countries as compared to the more localized natural disasters and crises such as tsunami, Kathmandu earthquake, Typhoon Mangkhut in Indonesia, etc., resulting in huge job losses, severe financial losses, shutdowns and human casualties. The purpose of this study is to briefly discuss the major viral outbreaks in the Asian countries and discuss their impact on the tourism industry. It will also discuss the resilience strategies taken by the Asian countries to re-emerge their tourism markets from these outbreaks. It will be based on the systematic review of the earlier literature on the various viral outbreaks and the corresponding resilience measures in the Asian peninsula. While the association between the pandemic and travel has been widely discussed in previous studies (Kuo, Chen, Tseng, Ju, & Huang, 2008; Lee, Son, Bendle, Kim, & Han, 2012), there is still no specific study which provides a comprehensive outlook on the various viral outbreaks and the tourism resilience strategies in Asia. It might also help the tourism industry stakeholders from the Asian countries to adequately identify and thoroughly plan for the possible future outbreaks and align resilience measures accordingly.

Tourism activity is a global industry and, as such, it is subject to global risks. International travel has developed exponentially over the last few decades. At the same time, diseases have increased their geographical spread influenced by ecologic, genetic and human factors. Currently, the increasing virus, epidemic and pandemic outbreaks represent some of the most negative consequences of globalization, causing deaths and significant economic losses due to the negative impacts they have on the tourism industry, one of the sectors that have been the most affected by health crises.

This work presents insights on the epidemics, pandemics and virus outbreaks that have occurred throughout the twenty-first century and how those occurrences have affected the tourism industry and the global economy. A brief literature review on health risks in tourism is presented, followed by a clinical perspective to help people understand the differences between endemics, outbreaks, epidemics and pandemics. Then, the study offers a presentation of the most significant pandemics in recent human history and a deep analysis of the COVID-19 disease. Finally, the effects that the different pandemics, epidemics and outbreaks that occurred in the present century had on tourism are explained, and the challenges tourism has to face are presented and discussed.

The authors examine the role of analysts’ prior experience of forecasting for firms exposed to epidemics on analysts’ forecast accuracy during the COVID-19 pandemic.

The authors examine the impact of analysts’ prior epidemic experience on forecast accuracy by comparing the changes from the pre-COVID-19 period (calendar year 2019) to the post-COVID period extending up to March 2023 across HRE versus non-HRE analysts. The authors consider a full sample (194,980) and a sub-sample (136,836) approach to distinguish “Recent” forecasts from “All” forecasts (including revisions).

The study's findings reveal that forecast accuracy for HRE analysts is significantly higher than that for non-HRE analysts during COVID-19. Specifically, forecast errors significantly decrease by 0.6% and 0.15% for the “Recent” and “All” forecast samples, respectively. This finding suggests that analysts’ prior epidemic experience leads to an enhanced ability to assess the uncertainty around the epidemic, thereby translating to higher forecast accuracy.

The finding that the expertise developed through an experience of following high-risk firms in the past enhances analysts’ performance during the pandemic sheds light on a key differentiator that partially explains the systematic difference in performance across analysts. The authors also show that industry experience alone is not useful in improving forecast accuracy during a pandemic – prior experience of tracking firms during epidemics adds incremental accuracy to analysts’ forecasts during pandemics such as COVID-19.

The study findings should prompt macroeconomic policymakers at the national level, such as the central banks of countries, to include past epidemic experiences as a key determinant when forecasting the economic outlook and making policy-related decisions. Moreover, practitioners and advisory firms can improve the earning prediction models by placing more weight on pandemic-adjusted forecasts made by analysts with past epidemic experience.

The uncertainty induced by the COVID-19 pandemic increases uncertainty in global financial markets. Under such circumstances, the importance of analysts’ role as information intermediaries gains even more importance. This raises the question of what determines analysts’ forecast accuracy during the COVID-19 pandemic. Building upon prior literature on the role of analyst experience in shaping analysts’ forecasts, the authors examine whether experience in tracking firms exposed to prior epidemics allows analysts to forecast more accurately during COVID-19. The authors find that analysts who have experience in forecasting for firms with high exposure to epidemics (H1N1, Zika, Ebola, and SARS) exhibit higher accuracy than analysts who lack such experience. Further, this effect of experience on forecast accuracy is more pronounced while forecasting for firms with higher exposure to the risk of COVID-19 and for firms with a poor ex-ante informational environment.

This study aims to compare many artificial neural network (ANN) methods to find out which method is better for the prediction of Covid19 number of cases in N steps ahead of the current time. Therefore, the authors can be more ready for similar issues in the future.

The authors are going to use many ANNs in this study including, five different long short-term memory (LSTM) methods, polynomial regression (from degree 2 to 5) and online dynamic unsupervised feedforward neural network (ODUFFNN). The authors are going to use these networks over a data set of Covid19 number of cases gathered by World Health Organization. After 1,000 epochs for each network, the authors are going to calculate the accuracy of each network, to be able to compare these networks by their performance and choose the best method for the prediction of Covid19.

The authors concluded that for most of the cases LSTM could predict Covid19 cases with an accuracy of more than 85% after LSTM networks ODUFFNN had medium accuracy of 45% but this network is highly flexible and fast computing. The authors concluded that polynomial regression cant is a good method for the specific purpose.

Considering the fact that Covid19 is a new global issue, less studies have been conducted with a comparative approach toward the prediction of Covid19 using ANN methods to introduce the best model of the prediction of this virus.