Pandémie de coronavirus et tourisme : modélisation dynamique de l'équilibre général stochastique de l'éclosion des maladies infectieuses
In mid-December 2019, a novel and infectious coronavirus (COVID-19) struck Wuhan, the most populous city in central China. Similar to the severe acute respiratory syndrome (SARS) that emerged in 2003, COVID-19 is an airborne illness that is highly transmittable between humans. Immediately after the Chinese government shared information about the virus publicly in late January 2020, stricter preventive measures, such as community quarantines and temporary business closures, swept across Chinese cities. The local outbreak quickly developed into an emerging public health crisis to the extent that World Health Organization (WHO) soon declared it as an unprecedented global pandemic. In March, Europe and the United States have successively become the epicenter of the pandemic, and many countries imposed restrictions on human mobility. As of March 23, 2020, infections were confirmed in 190 countries/territories/areas, totaling 332,930 cases, more than 14,510 deaths, and an exponentially growing number of suspected cases worldwide (World Health Organization, 2020).
Infectious disease outbreaks, including coronavirus, greatly jeopardize the tourism industry given its reliance on human mobility. The Chinese hotel market witnessed a 71% year-over-year decline of occupancy in 23–26 January (Baker, 2020). In this research note, we propose and calibrate a dynamic stochastic general equilibrium (DSGE) model to understand the effect of infectious disease outbreak on tourism. By applying this model to the case of coronavirus pandemic, this study represents a pioneering research effort on evaluating the impact of coronavirus on tourism. Compared to time-series and econometric analyses, the DSGE model can depict nuanced interactions across market decision makers under the general equilibrium framework. In contrast to the computable general equilibrium model (Blake, Sinclair, & Sugiyarto, 2003), decision makers in DSGE models optimize within a stochastic environment. Since the length and severity of the outbreak is uncertain, we believe it is more suitable to use DSGE model to address the impact of health crisis in this scenario.
Yang Yang, Hongru Zhang, Xiang Chen
↪ Coronavirus pandemic and tourism: Dynamic stochastic general equilibrium modeling of infectious disease outbreak (en anglais sur le site Science Direct)