Under the variant climate conditions in the transitional regions between tropics and subtropics, the impacts of climate factors on influenza subtypes have rarely been evaluated. With the available influenza A (Flu-A) and influenza B (Flu-B) outbreak data in Shenzhen, China, which is an excellent example of a transitional marine climate, the associations of multiple climate variables with these outbreaks were explored in this study. Daily laboratory-confirmed influenza virus and climate data were collected from 2009 to 2015. Potential impacts of daily mean/maximum/minimum temperatures ( T / T max / T min ), relative humidity (RH), wind velocity ( V ), and diurnal temperature range (DTR) were analyzed using the distributed lag nonlinear model (DLNM) and generalized additive model (GAM). Under its local climate partitions, Flu-A mainly prevailed in summer months (May to June), and a second peak appeared in early winter (December to January). Flu-B outbreaks usually occurred in transitional seasons, especially in autumn. Although low temperature caused an instant increase in both Flu-A and Flu-B risks, its effect could persist for up to 10 days for Flu-B and peak at 17 C (relative risk (RR) = 14.16, 95% CI: 7.46–26.88). For both subtypes, moderate–high temperature (28 C) had a significant but delayed effect on influenza, especially for Flu-A (RR = 26.20, 95% CI: 13.22–51.20). The Flu-A virus was sensitive to RH higher than 76%, while higher Flu-B risks were observed at both low (< 65%) and high (> 83%) humidity. Flu-A was active for a short term after exposure to large DTR (e.g., DTR = 10 C, RR = 12.45, 95% CI: 6.50–23.87), whereas Flu-B mainly circulated under stable temperatures. Although the overall wind speed in Shenzhen was low, moderate wind (2–3 m/s) was found to favor the outbreaks of both subtypes. This study revealed the thresholds of various climatic variables promoting influenza outbreaks, as well as the distinctions between the flu subtypes. These data can be helpful in predicting seasonal influenza outbreaks and minimizing the impacts, based on integrated forecast systems coupled with short-term climate models. Supplementary Information The online version contains supplementary material available at 10.1007/s00484-021-02204-y.
Under the background of global warming, the frequency of meteorological disasters caused by extreme weather and the impact on people's lives have gradually increased from 1951 to 2019 (Yamaguchi, 2020). China is one of the countries with the most severe meteorological disasters in the world. Meteorological disasters account for more than 70% of all-natural disaster losses in the country every year (Gao et al., 2012). Guangdong Province is located on the southeast coast and adjacent to the Northwest Pacific Ocean, with the longest coastline for a province in China, and is extremely vulnerable to landfall typhoons, resulting in huge economic losses and casualties (Ni et al., 2015;Yin et al., 2012). Shenzhen, located on the central coast of Southern Guangdong and bordering the northern part of Hong Kong, is a metropolitan with a permanent resident population of 17.56 million by the end of 2020 (Shenzhen Government Online, 2021). Shenzhen ranks fifth in Asia in terms of economic power, and plays a vital role in the national economy (Shenzhen Government Online, 2021). According to statistics, there are more than a thousand tall buildings with a height of more than 100 m in the city. Most of the facades of the high-rise buildings in Shenzhen are glass enclosure structures. For such a prosperous city with booming skyscrapers, the safety of high-rise buildings under the influence of strong winds is essential, especially under the influence of frequent typhoons (Yu et al., 2019). Therefore, the local government must understand the wind variation characteristics with the changing building height for wind engineering in terms of design and condition assessment (Zhang et al., 2021) to protect people's lives and properties.The power law is one of the most common methods in wind engineering for expressing the relationship between wind speed and height above ground (Lim et al., 2017). The wind speed at a given location increases with height
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