The effects of past SARS experience and proximity on declines in numbers of travelers to the Republic of Korea during the 2015 MERS outbreak: A retrospective study.

BACKGROUND: The experience of previous sizable outbreaks may affect travelers' decisions to travel to an area with an ongoing outbreak. METHODS: We estimated changes in monthly numbers of visitors to the Republic of Korea (ROK) in 2015 compared to projected values by selected areas. We tested whether areas' experience of a previous SARS outbreak of ≥100 cases or distance to the ROK had a significant effect on travel to the ROK during the MERS outbreak using t-tests and regression models. RESULTS: The percentage changes in visitors from areas with a previous SARS outbreak of ≥100 cases decreased more than the percentage changes in visitors from their counterparts in June (52.4% vs. 23.3%) and July (60.0% vs. 31.4%) during the 2015 MERS outbreak. The percentage changes in visitors from the close and intermediate categories decreased more than the far category. The results from regression models and sensitivity analyses demonstrated that areas with ≥100 SARS cases and closer proximity to the ROK had significantly larger percentage decreases in traveler volumes during the outbreak. CONCLUSIONS: During the 2015 MERS outbreak, areas with a previous sizable SARS outbreak and areas near the ROK showed greater decreases in percentage changes in visitors to the ROK.

Economic Impact of the 2015 MERS Outbreak on the Republic of Korea's Tourism-Related Industries.

The 2015 Middle East respiratory syndrome (MERS) outbreak in the Republic of Korea (ROK) is an example of an infectious disease outbreak initiated by international travelers to a high-income country. This study was conducted to determine the economic impact of the MERS outbreak on the tourism and travel-related service sectors, including accommodation, food and beverage, and transportation, in the ROK. We projected monthly numbers of noncitizen arrivals and indices of services for 3 travel-related service sectors during and after the MERS outbreak (June 2015 to June 2016) using seasonal autoregressive integrated moving average models. Tourism losses were estimated by multiplying the monthly differences between projected and actual numbers of noncitizen arrivals by average tourism expenditure per capita. Estimated tourism losses were allocated to travel-related service sectors to understand the distribution of losses across service sectors. The MERS outbreak was correlated with a reduction of 2.1 million noncitizen visitors corresponding with US$2.6 billion in tourism loss for the ROK. Estimated losses in the accommodation, food and beverage service, and transportation sectors associated with the decrease of noncitizen visitors were US$542 million, US$359 million, and US$106 million, respectively. The losses were demonstrated by lower than expected indices of services for the accommodation and food and beverage service sectors in June and July 2015 and for the transportation sector in June 2015. The results support previous findings that public health emergencies due to traveler-associated outbreaks of infectious diseases can cause significant losses to the broader economies of affected countries.

A model-based tool to predict the propagation of infectious disease via airports.

Epidemics of novel or re-emerging infectious diseases have quickly spread globally via air travel, as highlighted by pandemic H1N1 influenza in 2009 (pH1N1). Federal, state, and local public health responders must be able to plan for and respond to these events at aviation points of entry. The emergence of a novel influenza virus and its spread to the United States were simulated for February 2009 from 55 international metropolitan areas using three basic reproduction numbers (R(0)): 1.53, 1.70, and 1.90. Empirical data from the pH1N1 virus were used to validate our SEIR model. Time to entry to the U.S. during the early stages of a prototypical novel communicable disease was predicted based on the aviation network patterns and the epidemiology of the disease. For example, approximately 96% of origins (R(0) of 1.53) propagated a disease into the U.S. in under 75 days, 90% of these origins propagated a disease in under 50 days. An R(0) of 1.53 reproduced the pH1NI observations. The ability to anticipate the rate and location of disease introduction into the U.S. provides greater opportunity to plan responses based on the scenario as it is unfolding. This simulation tool can aid public health officials to assess risk and leverage resources efficiently.