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In recent years, with the rapid development of China's air transport industry and the change in market consumption structure, service quality has become one of the important factors affecting airline revenue. How to formulate a reasonable pricing strategy and maintain competitiveness in the fierce market competition has become an urgent problem for airlines. First, the impact factor of service quality level in the traditional pricing model is introduced and a static price competition model for multi-oligopoly airlines based on service quality is established in this paper. And then, a dynamic pricing model based on service quality of the multi-oligopoly airlines is established. The model incorporates the weight factor of service quality impact, which is used to indicate the weight of the service quality level in the process of airline dynamic pricing. The research results show that the service quality level of airlines has an indispensable influence on its development. Airlines should improve service quality as soon as possible to enhance market competitiveness and achieve sustainable development. [ABSTRACT FROM AUTHOR]

Abstract: We computed the daily AVHRR snow cover from all available AVHRR level1b raw data over central Asia (CA) with an aggregated rating based snow identification scheme. The daily AVHRR snow cover was further processed into an 8-day maximum-snow-extent data, and then went through a set of spatial and temporal filters to fill cloud or gap pixels. A correction method based on known long term snow probability in small sub-regions has been developed for computing corrected 8-day AVHRR snow cover, which is comparable to 8-day MODIS snow data. Validation of the daily AVHRR snow product against ground snow survey suggested a high accuracy of the snow identification scheme. Comparison of the daily AVHRR snow cover with the daily MODIS snow cover in Amu Dar'ya River basin within CA showed high accuracy of daily AVHRR snow, with a general accuracy of 99.60% and Kappa Coefficient of 0.92 in the basin. Comparison of the corrected 8-day AVHRR snow cover with 8-day MODIS cloud/gap free snow cover in the same basin also showed high comparability between both data, with a general accuracy of 95.61% and Kappa Coefficient of 0.84. Seasonal snow cover analysis in Amu Dar'ya River basin revealed the spatial and temporal patterns of snow distribution and negative trends in snow cover duration in 1986 to 2008 due to earlier snow melting dates. The newly developed long-term snow dataset from AVHRR and MODIS data over CA, with its high accuracy and internal comparability, is suitable for seasonal snow cover studies in mountainous regions over CA. [Copyright &y& Elsevier]

First discovered in rural areas of middle-eastern China in 2009, severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne zoonosis affecting hundreds of cases reported in China each year. Using the national surveillance data from 2010 to 2013, we conducted this retrospective epidemiological study and risk assessment of SFTS in China. We found that the incidence of SFTS and its epidemic areas are continuing to grow, but the case fatality rate (CFR) has steadily decreased. SFTS most commonly affected elderly farmers who acquired infection between May and July in middle-eastern China. However, other epidemiological characteristics such as incidence, sex ratio, CFR, and seasonality differ substantially across the affected provinces, which seem to be consistent with local agricultural activities and the seasonal abundance of ticks. Spatial scan statistics detected three hot spots of SFTS that accounted for 69.1% of SFTS cases in China. There was a strong association of SFTS incidence with temporal changes in the climate within the clusters. Multivariate modeling identified climate conditions, elevation, forest coverage, cattle density, and the presence of Haemaphysalis longicornis ticks as independent risk factors in the distribution of SFTS, based on which a predicted risk map of the disease was derived. [ABSTRACT FROM AUTHOR]

The geographic range of Zika virus (ZIKV) has expanded from Asia to the Americas, leading to the 2015-2016 pandemic with enhanced neurovirulence. At present, ZIKV is continuously circulating in many Southeast Asian countries. Unfortunately, the persistent evolution of ZIKV in Southeast Asia and its influence on the biological characteristics of the virus remain incompletely understood. In this study, the in vitro and in vivo properties of a new ZIKV isolate obtained from Cambodia in 2019 (CAM/2019) were characterized and compared with those of the Cambodian strain (CAM/2010). Compared with CAM/2010, the CAM/2019 virus showed similar plaque morphology and growth curves in cell cultures and induced comparable viremia and organ viral loads profiles in both BALB/c and A129 (IFNAR1 -/- ) mice upon intraperitoneal (i.p.) inoculation. Remarkably, the CAM/2019 virus exhibited enhanced neurovirulence in neonatal mice compared with CAM/2010, with a 74-fold reduction in the 50% lethal dose (LD 50 ). Consistently, CAM/2019 produced higher viral loads in the brains of BALB/c neonatal mice than CAM/2010 did. Sequence alignment showed that the CAM/2019 virus has acquired 12 amino acid substitutions, several of which were found to be associated with neurovirulence. In particular, the CAM/2019 virus shared an A1204T substitution in NS2A with the Thai isolate SI-BKK02 that was isolated from a microcephaly case. Taken together, our results indicate that a ZIKV strain isolated with specific mutations has emerged in Cambodia, highlighting the need for extensive molecular and disease surveillance in Cambodia and other Asian countries., (© 2022 Wiley Periodicals LLC.)

Two epidemic waves of an avian influenza A (H7N9) virus have so far affected China. Most human cases have been attributable to poultry exposure at live-poultry markets, where most positive isolates were sampled. The potential geographic extent of potential re-emerging epidemics is unknown, as are the factors associated with it. Using newly assembled data sets of the locations of 8,943 live-poultry markets in China and maps of environmental correlates, we develop a statistical model that accurately predicts the risk of H7N9 market infection across Asia. Local density of live-poultry markets is the most important predictor of H7N9 infection risk in markets, underscoring their key role in the spatial epidemiology of H7N9, alongside other poultry, land cover and anthropogenic predictor variables. Identification of areas in Asia with high suitability for H7N9 infection enhances our capacity to target biosurveillance and control, helping to restrict the spread of this important disease.