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https://www.selleckchem.com/products/wm-1119.html This paper is to produce different scenarios in forecasts for international tourism demand, in light of the COVID-19 pandemic. By implementing two distinct methodologies (the Long Short Term Memory neural network and the Generalized Additive Model), based on recent crises, we are able to calculate the expected drop in the international tourist arrivals for the next 12 months. We use a rolling-window testing strategy to calculate accuracy metrics and show that even though all models have comparable accuracy, the forecasts produced vary significantly according to the training data set, a finding that should be alarming to researchers. Our results indicate that the drop in tourist arrivals can range between 30.8% and 76.3% and will persist at least until June 2021.The novel coronavirus (COVID-19) exposed individuals to a great uncertainty about its health and economic ramifications, especially in the early days and weeks of the outbreak. This study documents oil and gasoline market implications of individuals' behavior upon such uncertainty by analyzing the relationship between Google search queries related to COVID-19-information search that reflects one's level of concern about the subject (risk perception)-and the performance of oil and gasoline markets during the pandemic. The empirical analysis based on daily data and a structural vector autoregressive model reveals that a unit increase in the popularity of COVID-19 related global search queries, after controlling for COVID-19 cases, results in 0.083% and 0.104% of a cumulative decline in Dow Jones US Oil & Gas Total index and New York Harbor Conventional Gasoline Regular spot price, respectively, after one day, 0.189% and 0.234% of a cumulative decline after one week, and 0.191% and 0.237% of a cumulative decline after two weeks. The reaction of Brent and West Texas Intermediate crude oil prices to the spike in COVID-19 related online searches is found to be stati

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