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4. New progress in HBV control and the cascade of health care for people living with HBV in China: evidence from the fourth national serological survey, 2020Research in context

Author

Zheng Hui, Wang Yu, Wang Fuzhen, Shen Liping, Zhang Guomin, Liu Jianhua, Wang Feng, Miao Ning, Li Jian, Ding Guowei, Meng Tongtong, Tang Lin, Zhang Shuang, Li Mingshuang, Li Yuan, Wang Xiaoqi, Liu Qianqian, Zhang Qian, Wu Dan, Yan Tingting, Su Qiudong, Wang Miao, Li Li, Hou Qian, Li Yixing, Liu Yi, Ye Shaodong, An Zhijie, Lance E. Rodewald, Jia Jidong, Wang Huaqing, Yu Wenzhou, Liu Zhongfu, Li Qun, Feng Zijian, and Yin Zundong

Subjects
Hepatitis B, National serological survey, Disease burden, Public aspects of medicine, RA1-1270
Abstract

Summary: Background: Since 1992, when recombinant hepatitis B vaccine was introduced in China, government health officials have used nationally representative serological surveys to monitor progress in prevention and control of hepatitis B. In 2020, we conducted the fourth seroepidemiological survey, which for the first time included medical evaluation of the clinical status of HBsAg positive subjects over the age of 15 and their medical management. We report survey results in comparison with the three previous surveys. Methods: Consistent with previous national surveys, the 2020 survey used a stratified, three-stage cluster random sampling method to select for evaluation 1-69-year-olds in 120 national disease surveillance points. Blood samples were tested for HBsAg, anti-HBV surface antigen (anti-HBs), and anti-HBV core antigen (anti-HBc) in the National Hepatitis Laboratory of the Institute for Viral Disease Control and Prevention of China CDC. HBsAg positive subjects aged ≥15-year were evaluated for evidence of liver disease, and through face-to-face questionnaire-based survey, we determined the healthcare management cascade of HBV-infected individuals. Findings: HBsAg prevalence in 1–69-year-olds was 5.86%; in children 1–4 years of age, seroprevalence was 0.30%; 75 million people were living with HBV nationwide. Among HBsAg-positive individuals 15 years and older, expert medical examination found that 78.03% were HBsAg carriers with no evidence of liver damage, 19.63% had chronic HBV with liver enzyme abnormalities, 0.84% had evidence of cirrhosis, and 0.15% had evidence of liver cancer. 59.78% of HBsAg + individuals were aware that they were positive before the survey, 30 million were unaware; 38.25% of those who knew they were positive (17 million) had medical indications for antiviral treatment, and 17.33% of these individuals (3 million) were being treated with antivirals. Interpretation: The decline in HBsAg prevalence in the general population, from 9.72% in 1992 to 5.86% in 2020, and in 1–4-year-olds from 9.67% in 1992 to 0.30% in 2020, shows progress that continues on track toward WHO targets for prevention of new infections. Implementation of acceptable strategies to identify infected individuals and offer long-term medical monitoring and management will be important to prevent complications from hepatitis B infection and for meeting WHO cascade-of-care targets. Funding: The study was funded by the Major Science and Technology Special Project of China’s 13th 5-Year Plan (grant no. 2017ZX10105015); Central finance-operation of public health emergency response mechanism of Chinese Center for Disease Control and Prevention (131031001000200001, 102393220020010000017).

Published
2024
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7. GAIL-PT: A Generic Intelligent Penetration Testing Framework with Generative Adversarial Imitation Learning

Author

Chen, Jinyin, Hu, Shulong, Zheng, Haibin, Xing, Changyou, and Zhang, Guomin

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

Penetration testing (PT) is an efficient network testing and vulnerability mining tool by simulating a hacker's attack for valuable information applied in some areas. Compared with manual PT, intelligent PT has become a dominating mainstream due to less time-consuming and lower labor costs. Unfortunately, RL-based PT is still challenged in real exploitation scenarios because the agent's action space is usually high-dimensional discrete, thus leading to algorithm convergence difficulty. Besides, most PT methods still rely on the decisions of security experts. Addressing the challenges, for the first time, we introduce expert knowledge to guide the agent to make better decisions in RL-based PT and propose a Generative Adversarial Imitation Learning-based generic intelligent Penetration testing framework, denoted as GAIL-PT, to solve the problems of higher labor costs due to the involvement of security experts and high-dimensional discrete action space. Specifically, first, we manually collect the state-action pairs to construct an expert knowledge base when the pre-trained RL / DRL model executes successful penetration testings. Second, we input the expert knowledge and the state-action pairs generated online by the different RL / DRL models into the discriminator of GAIL for training. At last, we apply the output reward of the discriminator to guide the agent to perform the action with a higher penetration success rate to improve PT's performance. Extensive experiments conducted on the real target host and simulated network scenarios show that GAIL-PT achieves the SOTA penetration performance against DeepExploit in exploiting actual target Metasploitable2 and Q-learning in optimizing penetration path, not only in small-scale with or without honey-pot network environments but also in the large-scale virtual network environment.

Published
2022

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