Collaboration between meteorology and public health: Predicting the dengue epidemic in Guangzhou, China, by meteorological parameters.

Background: Dengue has become an increasing public health threat around the world, and climate conditions have been identified as important factors affecting the transmission of dengue, so this study was aimed to establish a prediction model of dengue epidemic by meteorological methods.

Methods: The dengue case information and meteorological data were collected from Guangdong Provincial Center for Disease Prevention and Control and Guangdong Meteorological Bureau, respectively. We used spatio-temporal analysis to characterize dengue epidemics.

Epidemiological characteristics of gonorrhea and its influential meteorological factors: a 14-year retrospective assessment in China.

This study aimed to investigate the seasonal variation of gonorrhea in China, and to analyze the relationship between the incidence of gonorrhea and meteorological factors. Data from gonorrhea cases were obtained from the Disease Prevention and Control Bureau and the Data-Center for China Public Health Science, Chinese Center for Disease Control and Prevention, and the incidence of gonorrhea in China from 1 January 2006 to 31 December 2019 was analyzed. Meteorological data from the same period were obtained from the South China Meteorological Data Sharing Center, including the average monthly temperature, relative humidity, atmospheric pressure, sunshine hours, number of rainy days, and precipitation.

Prediction of skin disease using a new cytological taxonomy based on cytology and pathology with deep residual learning method.

With the development of artificial intelligence, technique improvement of the classification of skin disease is addressed. However, few study concerned on the current classification system of International Classification of Diseases, Tenth Revision (ICD)-10 on Diseases of the skin and subcutaneous tissue, which is now globally used for classification of skin disease. This study was aimed to develop a new taxonomy of skin disease based on cytology and pathology, and test its predictive effect on skin disease compared to ICD-10.