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Featured researches published by Honglong Zhang.


The Lancet | 2013

Human infection with avian influenza A H7N9 virus: an assessment of clinical severity.

Hongjie Yu; Benjamin J. Cowling; Luzhao Feng; Eric H. Y. Lau; Qiaohong Liao; Tim K. Tsang; Zhibin Peng; Peng Wu; Fengfeng Liu; Vicky J. Fang; Honglong Zhang; Ming Li; Lingjia Zeng; Zhen Xu; Zhongjie Li; Huiming Luo; Qun Li; Zijian Feng; Bin Cao; Weizhong Yang; Joseph T. Wu; Wang Y; Gabriel M. Leung

Summary Background Characterisation of the severity profile of human infections with influenza viruses of animal origin is a part of pandemic risk assessment, and an important part of the assessment of disease epidemiology. Our objective was to assess the clinical severity of human infections with avian influenza A H7N9 virus, which emerged in China in early 2013. Methods We obtained information about laboratory-confirmed cases of avian influenza A H7N9 virus infection reported as of May 28, 2013, from an integrated database built by the Chinese Center for Disease Control and Prevention. We estimated the risk of fatality, mechanical ventilation, and admission to the intensive care unit for patients who required hospital admission for medical reasons. We also used information about laboratory-confirmed cases detected through sentinel influenza-like illness surveillance to estimate the symptomatic case fatality risk. Findings Of 123 patients with laboratory-confirmed avian influenza A H7N9 virus infection who were admitted to hospital, 37 (30%) had died and 69 (56%) had recovered by May 28, 2013. After we accounted for incomplete data for 17 patients who were still in hospital, we estimated the fatality risk for all ages to be 36% (95% CI 26–45) on admission to hospital. Risks of mechanical ventilation or fatality (69%, 95% CI 60–77) and of admission to an intensive care unit, mechanical ventilation, or fatality (83%, 76–90) were high. With assumptions about coverage of the sentinel surveillance network and health-care-seeking behaviour for patients with influenza-like illness associated with influenza A H7N9 virus infection, and pro-rata extrapolation, we estimated that the symptomatic case fatality risk could be between 160 (63–460) and 2800 (1000–9400) per 100 000 symptomatic cases. Interpretation Human infections with avian influenza A H7N9 virus seem to be less serious than has been previously reported. Many mild cases might already have occurred. Continued vigilance and sustained intensive control efforts are needed to minimise the risk of human infection. Funding Chinese Ministry of Science and Technology; Research Fund for the Control of Infectious Disease; Hong Kong University Grants Committee; China–US Collaborative Program on Emerging and Re-emerging Infectious Diseases; Harvard Center for Communicable Disease Dynamics; US National Institute of Allergy and Infectious Disease; and the US National Institutes of Health.


BMC Medicine | 2015

The changing epidemiology of dengue in China, 1990-2014: a descriptive analysis of 25 years of nationwide surveillance data

Shengjie Lai; Zhuojie Huang; Hang Zhou; Katherine L. Anders; T. Alex Perkins; Wenwu Yin; Yu Li; Di Mu; Qiulan Chen; Zike Zhang; Yanzi Qiu; Liping Wang; Honglong Zhang; Linjia Zeng; Xiang Ren; Mengjie Geng; Zhongjie Li; Andrew J. Tatem; Simon I. Hay; Hongjie Yu

BackgroundDengue has been a notifiable disease in China since 1 September 1989. Cases have been reported each year during the past 25 years of dramatic socio-economic changes in China, and reached a historical high in 2014. This study describes the changing epidemiology of dengue in China during this period, to identify high-risk areas and seasons and to inform dengue prevention and control activities.MethodsWe describe the incidence and distribution of dengue in mainland China using notifiable surveillance data from 1990-2014, which includes classification of imported and indigenous cases from 2005-2014.ResultsFrom 1990-2014, 69,321 cases of dengue including 11 deaths were reported in mainland China, equating to 2.2 cases per one million residents. The highest number was recorded in 2014 (47,056 cases). The number of provinces affected has increased, from a median of three provinces per year (range: 1 to 5 provinces) during 1990-2000 to a median of 14.5 provinces per year (range: 5 to 26 provinces) during 2001-2014. During 2005-2014, imported cases were reported almost every month and 28 provinces (90.3%) were affected. However, 99.8% of indigenous cases occurred between July and November. The regions reporting indigenous cases have expanded from the coastal provinces of southern China and provinces adjacent to Southeast Asia to the central part of China. Dengue virus serotypes 1, 2, 3, and 4 were all detected from 2009-2014.ConclusionsIn China, the area affected by dengue has expanded since 2000 and the incidence has increased steadily since 2012, for both imported and indigenous dengue. Surveillance and control strategies should be adjusted to account for these changes, and further research should explore the drivers of these trends.Please see related article: http://dx.doi.org/10.1186/s12916-015-0345-0


PLOS ONE | 2014

Predicting Local Dengue Transmission in Guangzhou, China, through the Influence of Imported Cases, Mosquito Density and Climate Variability

Shaowei Sang; Wenwu Yin; Peng Bi; Honglong Zhang; Chenggang Wang; Xiaobo Liu; Bin Chen; Weizhong Yang; Qiyong Liu

Introduction Each year there are approximately 390 million dengue infections worldwide. Weather variables have a significant impact on the transmission of Dengue Fever (DF), a mosquito borne viral disease. DF in mainland China is characterized as an imported disease. Hence it is necessary to explore the roles of imported cases, mosquito density and climate variability in dengue transmission in China. The study was to identify the relationship between dengue occurrence and possible risk factors and to develop a predicting model for dengue’s control and prevention purpose. Methodology and Principal Findings Three traditional suburbs and one district with an international airport in Guangzhou city were selected as the study areas. Autocorrelation and cross-correlation analysis were used to perform univariate analysis to identify possible risk factors, with relevant lagged effects, associated with local dengue cases. Principal component analysis (PCA) was applied to extract principal components and PCA score was used to represent the original variables to reduce multi-collinearity. Combining the univariate analysis and prior knowledge, time-series Poisson regression analysis was conducted to quantify the relationship between weather variables, Breteau Index, imported DF cases and the local dengue transmission in Guangzhou, China. The goodness-of-fit of the constructed model was determined by pseudo-R2, Akaike information criterion (AIC) and residual test. There were a total of 707 notified local DF cases from March 2006 to December 2012, with a seasonal distribution from August to November. There were a total of 65 notified imported DF cases from 20 countries, with forty-six cases (70.8%) imported from Southeast Asia. The model showed that local DF cases were positively associated with mosquito density, imported cases, temperature, precipitation, vapour pressure and minimum relative humidity, whilst being negatively associated with air pressure, with different time lags. Conclusions Imported DF cases and mosquito density play a critical role in local DF transmission, together with weather variables. The establishment of an early warning system, using existing surveillance datasets will help to control and prevent dengue in Guangzhou, China.


PLOS ONE | 2014

Viral Etiologies of Hospitalized Acute Lower Respiratory Infection Patients in China, 2009-2013

Luzhao Feng; Zhongjie Li; Shiwen Zhao; Harish Nair; Shengjie Lai; Wenbo Xu; Mengfeng Li; Jianguo Wu; Lili Ren; Wei Liu; Zhenghong Yuan; Yu Chen; Xinhua Wang; Zhuo Zhao; Honglong Zhang; Fu Li; Xianfei Ye; Sa Li; Daniel R. Feikin; Hongjie Yu; Weizhong Yang

Background Acute lower respiratory infections (ALRIs) are an important cause of acute illnesses and mortality worldwide and in China. However, a large-scale study on the prevalence of viral infections across multiple provinces and seasons has not been previously reported from China. Here, we aimed to identify the viral etiologies associated with ALRIs from 22 Chinese provinces. Methods and Findings Active surveillance for hospitalized ALRI patients in 108 sentinel hospitals in 24 provinces of China was conducted from January 2009-September 2013. We enrolled hospitalized all-age patients with ALRI, and collected respiratory specimens, blood or serum collected for diagnostic testing for respiratory syncytial virus (RSV), human influenza virus, adenoviruses (ADV), human parainfluenza virus (PIV), human metapneumovirus (hMPV), human coronavirus (hCoV) and human bocavirus (hBoV). We included 28,369 ALRI patients from 81 (of the 108) sentinel hospitals in 22 (of the 24) provinces, and 10,387 (36.6%) were positive for at least one etiology. The most frequently detected virus was RSV (9.9%), followed by influenza (6.6%), PIV (4.8%), ADV (3.4%), hBoV (1.9), hMPV (1.5%) and hCoV (1.4%). Co-detections were found in 7.2% of patients. RSV was the most common etiology (17.0%) in young children aged <2 years. Influenza viruses were the main cause of the ALRIs in adults and elderly. PIV, hBoV, hMPV and ADV infections were more frequent in children, while hCoV infection was distributed evenly in all-age. There were clear seasonal peaks for RSV, influenza, PIV, hBoV and hMPV infections. Conclusions Our findings could serve as robust evidence for public health authorities in drawing up further plans to prevent and control ALRIs associated with viral pathogens. RSV is common in young children and prevention measures could have large public health impact. Influenza was most common in adults and influenza vaccination should be implemented on a wider scale in China.


Malaria Journal | 2014

The epidemiology of Plasmodium vivax and Plasmodium falciparum malaria in China, 2004–2012: from intensified control to elimination

Qian Zhang; Shengjie Lai; Canjun Zheng; Honglong Zhang; Sheng Zhou; Wenbiao Hu; Archie Clements; Xiao-Nong Zhou; Weizhong Yang; Simon I. Hay; Hongjie Yu; Zhongjie Li

BackgroundIn China, the national malaria elimination programme has been operating since 2010. This study aimed to explore the epidemiological changes in patterns of malaria in China from intensified control to elimination stages.MethodsData on nationwide malaria cases from 2004 to 2012 were extracted from the Chinese national malaria surveillance system. The secular trend, gender and age features, seasonality, and spatial distribution by Plasmodium species were analysed.ResultsIn total, 238,443 malaria cases were reported, and the proportion of Plasmodium falciparum increased drastically from <10% before 2010 to 55.2% in 2012. From 2004 to 2006, malaria showed a significantly increasing trend and with the highest incidence peak in 2006 (4.6/100,000), while from 2007 onwards, malaria decreased sharply to only 0.18/100,000 in 2012. Males and young age groups became the predominantly affected population. The areas affected by Plasmodium vivax malaria shrunk, while areas affected by P. falciparum malaria expanded from 294 counties in 2004 to 600 counties in 2012.ConclusionsThis study demonstrated that malaria has decreased dramatically in the last five years, especially since the Chinese government launched a malaria elimination programme in 2010, and areas with reported falciparum malaria cases have expanded over recent years. These findings suggest that elimination efforts should be improved to meet these changes, so as to achieve the nationwide malaria elimination goal in China in 2020.


Journal of the American Medical Informatics Association | 2012

Adjusting outbreak detection algorithms for surveillance during epidemic and non-epidemic periods

Zhongjie Li; Shengjie Lai; David L. Buckeridge; Honglong Zhang; Yajia Lan; Weizhong Yang

Many aberration detection algorithms are used in infectious disease surveillance systems to assist in the early detection of potential outbreaks. In this study, we explored a novel approach to adjusting aberration detection algorithms to account for the impact of seasonality inherent in some surveillance data. By using surveillance data for hand-foot-and-mouth disease in Shandong province, China, we evaluated the use of seasonally-adjusted alerting thresholds with three aberration detection methods (C1, C2, and C3). We found that the optimal thresholds of C1, C2, and C3 varied between the epidemic and non-epidemic seasons of hand-foot-and-mouth disease, and the application of seasonally adjusted thresholds improved the performance of outbreak detection by maintaining the same sensitivity and timeliness while decreasing by nearly half the false alert rate during the non-epidemic season. Our preliminary findings suggest a general approach to improving aberration detection for outbreaks of infectious disease with seasonally variable incidence.


Bulletin of The World Health Organization | 2014

Hand, foot and mouth disease in China: evaluating an automated system for the detection of outbreaks

Zhongjie Li; Shengjie Lai; Honglong Zhang; Liping Wang; Dinglun Zhou; Jizeng Liu; Yajia Lan; Jiaqi Ma; Hongjie Yu; David L. Buckeridge; Chakrarat Pittayawonganan; Archie Clements; Wenbiao Hu; Weizhong Yang

Abstract Objective To evaluate the performance of China’s infectious disease automated alert and response system in the detection of outbreaks of hand, foot and mouth (HFM) disease. Methods We estimated size, duration and delay in reporting HFM disease outbreaks from cases notified between 1 May 2008 and 30 April 2010 and between 1 May 2010 and 30 April 2012, before and after automatic alert and response included HFM disease. Sensitivity, specificity and timeliness of detection of aberrations in the incidence of HFM disease outbreaks were estimated by comparing automated detections to observations of public health staff. Findings The alert and response system recorded 106 005 aberrations in the incidence of HFM disease between 1 May 2010 and 30 April 2012 – a mean of 5.6 aberrations per 100 days in each county that reported HFM disease. The response system had a sensitivity of 92.7% and a specificity of 95.0%. The mean delay between the reporting of the first case of an outbreak and detection of that outbreak by the response system was 2.1 days. Between the first and second study periods, the mean size of an HFM disease outbreak decreased from 19.4 to 15.8 cases and the mean interval between the onset and initial reporting of such an outbreak to the public health emergency reporting system decreased from 10.0 to 9.1 days. Conclusion The automated alert and response system shows good sensitivity in the detection of HFM disease outbreaks and appears to be relatively rapid. Continued use of this system should allow more effective prevention and limitation of such outbreaks in China.


PLOS ONE | 2014

Evaluation of the Performance of a Dengue Outbreak Detection Tool for China

Honglong Zhang; Zhongjie Li; Shengjie Lai; Archie Clements; Liping Wang; Wenwu Yin; Hang Zhou; Hongjie Yu; Wenbiao Hu; Weizhong Yang

An outbreak detection and response system, using time series moving percentile method based on historical data, in China has been used for identifying dengue fever outbreaks since 2008. For dengue fever outbreaks reported from 2009 to 2012, this system achieved a sensitivity of 100%, a specificity of 99.8% and a median time to detection of 3 days, which indicated that the system was a useful decision tool for dengue fever control and risk-management programs in China.


PLOS ONE | 2013

Improving the performance of outbreak detection algorithms by classifying the levels of disease incidence

Honglong Zhang; Shengjie Lai; Liping Wang; Dan Zhao; Dinglun Zhou; Yajia Lan; David L. Buckeridge; Zhongjie Li; Weizhong Yang

We evaluated a novel strategy to improve the performance of outbreak detection algorithms, namely setting the alerting threshold separately in each region according to the disease incidence in that region. By using data on hand, foot and mouth disease in Shandong province, China, we evaluated the impact of disease incidence on the performance of outbreak detection algorithms (EARS-C1, C2 and C3). Compared to applying the same algorithm and threshold to the whole region, setting the optimal threshold in each region according to the level of disease incidence (i.e., high, middle, and low) enhanced sensitivity (C1: from 94.4% to 99.1%, C2: from 93.5% to 95.4%, C3: from 91.7% to 95.4%) and reduced the number of alert signals (the percentage of reduction is C1∶4.3%, C2∶11.9%, C3∶10.3%). Our findings illustrate a general method for improving the accuracy of detection algorithms that is potentially applicable broadly to other diseases and regions.


International Journal of Health Planning and Management | 2017

Surveillance and early warning systems of infectious disease in China: From 2012 to 2014.

Honglong Zhang; Liping Wang; Shengjie Lai; Zhongjie Li; Qiao Sun; Peng Zhang

&NA; Appropriate surveillance and early warning of infectious diseases have very useful roles in disease control and prevention. In 2004, China established the National Notifiable Infectious Disease Surveillance System and the Public Health Emergency Event Surveillance System to report disease surveillance and events on the basis of data sources from the National Notifiable Infectious Disease Surveillance System, China Infectious Disease Automated‐alert and Response System in this country. This study provided a descriptive summary and a data analysis, from 2012 to 2014, of these 3 key surveillance and early warning systems of infectious disease in China with the intent to provide suggestions for system improvement and perfection.

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Shengjie Lai

University of Southampton

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Zhongjie Li

Chinese Center for Disease Control and Prevention

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Weizhong Yang

Chinese Center for Disease Control and Prevention

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Liping Wang

Chinese Center for Disease Control and Prevention

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Archie Clements

Australian National University

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Wenbiao Hu

Queensland University of Technology

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Xiang Ren

Chinese Center for Disease Control and Prevention

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