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62 results on '"Haogao Gu"'

58. SARS-CoV-2 Superspread in Fitness Center, Hong Kong, China, March 2021.

Author

Chu, Daniel K. W., Haogao Gu, Chang, Lydia D. J., Cheuk, Sammi S. Y., Gurung, Shreya, Krishnan, Pavithra, Ng, Daisy Y. M., Liu, Gigi Y. Z., Wan, Carrie K. C., Tsang, Dominic N. C., Peiris, Malik, Poon, Leo L. M., and Gu, Haogao

Subjects
*PHYSICAL fitness centers, *SARS-CoV-2, *SUPERSPREADING events
Abstract

To investigate a superspreading event at a fitness center in Hong Kong, China, we used genomic sequencing to analyze 102 reverse transcription PCR-confirmed cases of severe acute respiratory syndrome coronavirus 2 infection. Our finding highlights the risk for virus transmission in confined spaces with poor ventilation and limited public health interventions. [ABSTRACT FROM AUTHOR]

Published
2021
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59. [Predicating risk area of human infection with avian influenza A (H7N9) virus by using early warning model in China]

Author

Haogao, Gu, Wangjian, Zhang, Hao, Xu, Pengyuan, Li, Luolin, Wu, Pi, Guo, Yuantao, Hao, Jiahai, Lu, and Dingmei, Zhang

Subjects
Risk, China, Models, Statistical, Risk Factors, Incidence, Population Surveillance, Influenza, Human, Humans, Influenza A Virus, H7N9 Subtype, Disease Outbreaks
Abstract

To establish a risk early warning model of human infection with avian influenza A (H7N9) virus and predict the area with high risk of the outbreak of H7N9 virus infection.The incidence data of human infection with H7N9 virus at prefecture level in China from February 2013 to June 2014 were collected, and the geographic and meteorological data during the same period in these areas were collected too. Spatial auto regression (SAR) model and generalized additive model (GAM) were used to estimate different risk factors. Afterwards, the risk area map was created based on the predicted value of both models.All the human infections with H7N9 virus occurred in the predicted areas by the early warning model in February 2014. The early warning model successfully predicted the spatial moving trend of the disease, and this trend was verified by two outbreaks in northern China in April and May 2014.The established early warning model showed accuracy and precision in short-term prediction, which might be applied in the active surveillance, early warning and prevention/control of the outbreak of human infection with H7N9 virus.

Published
2015

60. Circulation of Influenza A(H5N8) Virus, Saudi Arabia.

Author

Al-Ghadeer, Hussain, Chu, Daniel K. W., Rihan, Ehab A., Abd-Allah, Ehab M., Haogao Gu, Chin, Alex W. H., Qasim, Ibrahim A., Aldoweriej, Ali, Alharbi, Sanad S., Al-Aqil, Marshad A., Al-Sahaf, Ali, Rahman, Salah S. Abdel, Aljassem, Ali H., Abdul-Al, Ali, Aljasir, Mohammed R., Alhammad, Yousef M. O., Kasem, Samy, Peiris, Malik, Zaki, Ahmed Z. S. A., and Poon, Leo L. M.

Subjects
AVIAN influenza, MICROBIAL virulence, STAPHYLOCOCCUS aureus, PATHOGENIC microorganisms, DISEASE prevalence
Abstract

Highly pathogenic avian influenza A(H5N8) viruses have been detected in several continents. However, limited viral sequence data are available from countries in the Middle East. We report full-genome analyses of highly pathogenic H5N8 viruses recently detected in different provinces in Saudi Arabia. [ABSTRACT FROM AUTHOR]

Published
2018
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61. Meteorological Factors for Dengue Fever Control and Prevention in South China

Author

Zhicong Yang, Ross Ka-Kit Leung, Jiahai Lu, Haogao Gu, Qinlong Jing, Yuantao Hao, Dingmei Zhang, and Wangjian Zhang

Subjects
China, Mosquito Control, South china, Meteorological Concepts, Health, Toxicology and Mutagenesis, Lag, 030231 tropical medicine, boosted regression trees, lcsh:Medicine, Mosquito Vectors, meteorological effects, Article, Disease Outbreaks, Dengue fever, Dengue, 03 medical and health sciences, 0302 clinical medicine, Environmental health, Risk index, medicine, Animals, Humans, dengue fever, 030212 general & internal medicine, Population Density, Warning system, lcsh:R, Temperature, Public Health, Environmental and Occupational Health, Climatic variables, Models, Theoretical, medicine.disease, Regression, Mosquito control, Culicidae, Geography
Abstract

Dengue fever (DF) is endemic in Guangzhou and has been circulating for decades, causing significant economic loss. DF prevention mainly relies on mosquito control and change in lifestyle. However, alert fatigue may partially limit the success of these countermeasures. This study investigated the delayed effect of meteorological factors, as well as the relationships between five climatic variables and the risk for DF by boosted regression trees (BRT) over the period of 2005–2011, to determine the best timing and strategy for adapting such preventive measures. The most important meteorological factor was daily average temperature. We used BRT to investigate the lagged relationship between dengue clinical burden and climatic variables, with the 58 and 62 day lag models attaining the largest area under the curve. The climatic factors presented similar patterns between these two lag models, which can be used as references for DF prevention in the early stage. Our results facilitate the development of the Mosquito Breeding Risk Index for early warning systems. The availability of meteorological data and modeling methods enables the extension of the application to other vector-borne diseases endemic in tropical and subtropical countries.

Published
2016

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