Comparative epidemiology of Middle East respiratory syndrome coronavirus (MERS-CoV) in Saudi Arabia and South Korea

a b s t r a c tBackground: Middle East Respiratory Syndrome (MERS) is a respiratory disease caused by a novel coronavirus that was identified in 2012 in Saudi Arabia. It is associated with significant mortality and morbidity. We identified factors associated with the Middle East Respiratory Syndrome-Coronavirus (MERS-CoV) infection among suspected cases presented with sign and symptoms of upper respiratory infection or exposure to the virus. We also looked at the impact of medication history on virus transmission. Method: We included subjects with suspected MERS-CoV infection and confirmed cases of MERS infection. Subjects were excluded if there were any missing data that prevent the final analysis. Descriptive statistics were used to report demographic data. Percentages and frequencies were used to summarize the categorical variables, while means and standard deviations were calculated for continuous variables. Logistic regression was used to assess the risk of MERS-CoV infection among the suspected cases. A value of p < 0.05 was considered statistically significant. Results: A total of 16,189 suspected cases were identified, complete data were analyzed for 3154 to assess factors that are independently associated with MERS-CoV infection. MERS-CoV infection was associated with age (adjusted odds ratio [AOR] = 1.06; 95% CI [1.02-1.098], P-value = 0.004), male gender (AOR = 1.617; 95% CI [1.365-1.77], P-value < 0.001) and diabetes (AOR = 1.68; 95% CI [1.346-1.848], P-value = 0.002. There was no significant association with the other comorbidities. Medication history was not associated with an increase or decrease the likelihood of the infection. Conclusions: MERS-Cov infection is more common in male, advanced age and diabetes. No medications were associated with an increase or decrease the likelihood of the infection. This is important to focus on screening and detection to this patient population.

From May to July 2015, there was a nation-wide outbreak of Middle East respiratory syndrome (MERS) in Korea. MERS is caused by MERS-CoV, an enveloped, positive-sense, single-stranded RNA virus belonging to the family Coronaviridae. Despite expert opinions that the danger of MERS might be exaggerated, there was an overreaction by the public according to the Korean mass media, which led to a noticeable reduction in social and economic activities during the outbreak. To explain this phenomenon, we presumed that machine learning-based analysis of media outlets would be helpful and collected a number of Korean mass media articles and short-text comments produced during the 10-week outbreak. To process and analyze the collected data (over 86 million words in total) effectively, we created a methodology composed of machine-learning and information-theoretic approaches. Our proposal included techniques for extracting emotions from emoticons and Internet slang, which allowed us to significantly (approximately 73%) increase the number of emotion-bearing texts needed for robust sentiment analysis of social media. As a result, we discovered a plausible explanation for the public overreaction to MERS in terms of the interplay between the disease, mass media, and public emotions.

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