How Urban Factors Affect the Spatiotemporal Distribution of Infectious Diseases in Addition to Intercity Population Movement in China

The outbreak of the 2019 novel coronavirus (COVID-19) has attracted global attention. During the Chinese New Year holiday, population outflow from Wuhan induced the spread of the epidemic to other cities in China. This study analyzed massive intercity movement data from Baidu and epidemic data to study how intercity population outflows affected the spatiotemporal spread of the epidemic. This study further investigated how urban factors influenced the spatiotemporal spread of COVID-19. The analysis indicates that intercity movement was an important factor in the spread of the epidemic in China, and the impact of intercity movement on the spread was heterogeneous across different classes of cities. The spread of the epidemic also varied among cities and was affected by urban factors including the total population, population density, and gross domestic product (GDP). The findings have implications for public health management. Mega-cities should consider tougher measures to contain the spread of the epidemic compared with other cities. It is of great significance for policymakers in any nation to assess the potential risk of epidemics and make cautious plans ahead of time.

Does the ‘Chinese New Year effect’ exist? Hospital mortality in patients admitted to internal medicine departments during official consecutive holidays: a nationwide population-based cohort study

ObjectiveOur study aimed to compare the mortality risk among patients admitted to internal medicine departments during official consecutive holidays (using Chinese New Year holidays as an indicator) with that of weekend and weekday admissions.DesignNationwide population-based cohort study.SettingTaiwan’s National Health Insurance Research Database.PatientsPatients admitted to internal medicine departments in acute care hospitals during January and February each year between 2001 and 2013 were identified. Admissions were categorised as: Chinese New Year holiday (n=10 779), weekend (n=35 870) or weekday admissions (n=143 529).Outcome measuresORs for in-hospital mortality and 30-day mortality were calculated using multivariate logistic regression with adjustment for confounders.ResultsBoth in-hospital and 30-day mortality were significantly higher for patients admitted during the Chinese New Year holidays and on weekends compared with those admitted on weekdays. Chinese New Year holiday admissions had a 38% and 40% increased risk of in-hospital (OR=1.38, 95% CI 1.27 to 1.50, p<0.001) and 30-day (OR=1.40, 95% CI 1.31 to 1.50, p<0.001) mortality, respectively, compared with weekday admissions. Weekend admissions had a 17% and 19% increased risk of in-hospital (OR=1.17, 95% CI 1.10 to 1.23, p<0.001) and 30-day (OR=1.19, 95% CI 1.14 to 1.24, p<0.001) mortality, respectively, compared with weekday admissions. Analyses stratified by principal diagnosis revealed that the increase in in-hospital mortality risk was highest for patients admitted on Chinese New Year holidays with a diagnosis of ischaemic heart disease (OR=3.43, 95% CI 2.46 to 4.80, p<0.001).ConclusionsThe mortality risk was highest for patients admitted during Chinese New Year holidays, followed by weekend admissions, and then weekday admissions. Further studies are necessary to identify the underlying causes and develop strategies to improve outcomes for patients admitted during official consecutive holidays.

PRE AND POST CHINESE NEW YEAR HOLIDAY EFFECTS: EVIDENCE FROM HONG KONG STOCK MARKET

This paper investigated the existence of pre-Chinese New Year (CNY) and post-CNY holiday effect in the Hong Kong stock market for the period covering January 1988 to July 2012. The generalized autoregressive conditional heteroscedasticity (GARCH)-M model is adopted to examine the average returns and associated with symmetrical behavior. Then, asymmetric effect will be identified by using the Threshold GARCH-M (TGARCH-M) and Exponential GARCH-M (EGARCH-M) models. Results obtained indicate the significant two days pre-CNY and one day post-CNY holiday effects. Results also showed that post-CNY is found to be more volatile than the pre-CNY. Besides, the study found evidence of asymmetrical market reactions towards positive and negative news. The CNY holiday effects can be explained with the arguments drawn from behavioral finance, where the Chinese superstition and tradition cultures can alter investors' attitudes toward risk and affect investors' decision making in stock trading.