Objective To analyze the performance of China Infectious Diseases Automated-alert and Response System(CIDARS) established in 2008 by China CDC,and provide reference for the improvement of CIDARS. Methods CIDARS used fixed-value detection method and temporal aberration detection method for aberrant detection, with the work flowchart of signals generating, preliminary verification and field investigation. The fixed-value detection method was for 15 diseases, and the temporal aberration detection method was for 33 diseases, including 3 types of sub-methods,such as moving percentile method, cumulative sum control chart algorithm and cluster early-warning method. Data analysis also included the concerned priority diseases which were added by province according to the actual disease control needs. All the signals generated by CIDARS in 31 provinces in 2016 were collected, the response status,such as response rate and response timeliness, and preliminary verification outcome, such as the signals related to suspected outbreaks and its proportion, were deeply analyzed from the dimension of province and disease, respectively. Results A total of 325 208 signals were generated nationwide by the system, in which 323 271 (99.40％) were responded,300 614 (92.44％) were responded within 24 hours. The proportion of signals of suspected outbreaks was 4.91％ (15 964). The first 3 provinces with high number of signals were Shandong, Henan and Guangxi, and Shandong had highest proportion of signals of suspected outbreaks(22.33％). Among all the signals, 97 689 were generated by the fixed-value detection method, the overall response rate of the signals was 98.83％, and in 19 provinces it was 100％. The median interval of the response was 0.72 h (P25 = 0.16 h, P75 = 4.09 h),the response rate within 2 hours was 65.66％. In addition, 227 519 signals were generated by the temporal aberration detection method,in which 3 398 (1.49％) were verified as suspected outbreaks. The response rate of signals was 99.65％ (226 724),and the response rate within 24 hours was 93.23％. The median interval of signals was 0.99 h (P25 = 0.50 h, P75 = 2.44 h). The first 3 provinces with high response rates within 24 hours were Guizhou, Hunan and Shanghai, the first 3 diseases with highest proportion of signals of suspected outbreaks were dengue fever, leptospirosis and rubella. Conclusion In 2016,the performance of CIDARS was well, with response and timely response rates at high levels, but the proportion of signals of suspected outbreaks was low,so it is necessary to further improve CIDARS by identifying the areas and seasons with different disease incidence levels, exploring new data sources and narrowing the space detection coverage.

The spatial heterogeneity and seasonality of infectious diseases may affect the performance of early warning models. In this chapter, three explorative studies on hand, foot, and mouth disease are introduced, which try to improve detection performance by optimizing parameter values of temporal and spatial aberration detection models.

This chapter provides brief introductions to the basic models and algorithms for the early warning of infectious diseases. First, it describes several types and principles of early warning models. It then discusses several indicators for evaluating the effectiveness of early warning models.

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.

Syndromic surveillance has been widely used for the early warning of infectious disease outbreaks, especially in mass gatherings, but the collection of electronic data on symptoms in hospitals is one of the fundamental challenges that must be overcome during operating a syndromic surveillance system. The objective of our study is to describe and evaluate the implementation of a symptom-clicking-module (SCM) as a part of the enhanced hospital-based syndromic surveillance during the 41st World Exposition in Shanghai, China, 2010.

Objective To explore the effect of signal strength indictor (SSI) in improving sensitivity of China Infectious Diseases Automated-alert and Response System (CIDARS). Methods Diarrhea cases in 2007-2011 and early warning signals in 2010-2011 were selected by using random digital table method. Then, SSI and event-related ratio (ER) were calculated. The relationship between ER and SSI was analyzed, and the effect of SSI on ER was explored by using multiple logistic regression analysis. Results 9 620 early warning signals in 2010-2011 were generated in two years. Of these, 74, or 0.77%were defined as suspected outbreak signal. The median of SSI related with suspected outbreak signal was 4.0, which was much higher than non-suspected outbreak signal (1.7). ER was significantly correlated with SSI (r=0.917). SSI classification has a good correlation between the ER, ER exceeded 20 after SSI reached 20. The multivariate logistic regression analysis showed OR of SSI related with suspected outbreak signal was 2.52 (95%CI 2.04-3.12). Compared with non-epidemic season, the relationship of SSI and ER in epidemic season was much higher. Conclusion SSI was closely related with ER. The relationship was much closer in large scale outbreak and epidemic season, and compared to non-epidemic,the effect of epidemic season is more obvious.

Objective To evaluate the performance of China Infectious Disease Automated-alert and Response System (CIDARS) in China, and provide evidence for the improvement of the system. Methods The data of the automated alerts generated by the system, the responses to the alerts and alert confirmation were collected from 31 provinces in China in 2014. The analysis results were compared with the performance of the system during 2011-2013. Results A total of 386 578 alerts were generated nationwide on 33 infectious diseases, in which 383 637 (99.24%) were responded, and the median (P25-P75) of time for response was 1.0 (0.4-3.8)h. The overall response rate and the response rate in 24 hours in 2014 were higher than those during 2011-2013. Among all the alerts, 163 649 were generated by fixed-value detection method, in which 162 361 had responses (99.21%), and the median (P25-P75) of time for response was 1.0 (0.2-5.3)h. After the preliminary data verification, field investigation, and laboratory test, 80 923 alerts of disease cases were confirmed, accounting for 49.50% of the total alerts. In addition, 222 929 alerts were generated by the temporal aberration detection methods with an average 1.48 alerts per county in a week, and 3159 suspected outbreaks were confirmed, accounting for 1.42% of the total alerts. The response rate was 99.26%, and the median (P25-P75) of time for response was 1.1 (0.5-2.9)h. Conclusion In 2014, the response rate of alerts generated by CIDARS and its timeliness remained at a high level. The overall alert response rate and the response rate within 24 hours were improved compared with those during 2011-2013, but the confirmation of the alerts for suspected outbreaks needs further improvement.

Dengue 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. We 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. From 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. In 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

早期识别传染病的暴发,及早采取有效控制措施,对提高突发公共卫生事件应急处置能力具有非常重要的公共卫生意义。我国自2004年开始实行传染病病例个案的网络直报,为及时分析与处理监测数据、早期探测和预警传染病暴发奠定了基础。2008年4月21日,中国CDC启用了国家传染病自动预警系统(简称预警系统) [1] ,浙江省同期开展试运行。为评估预警系统的运行效果,笔者对2013年预警系

Objective To analyze the viral etiologies of hospitalized pneumonia patients aged less than five years in six provinces during 2009-2012,and to describe the seasonality of the detected viral etiologies. Methods Eight hospitals were selected in six provinces from a national acute respiratory infection surveillance network. Demographic information,clinical history and physical examination,and laboratory testing results of the enrolled hospitalized patients aged less than five years with pneumonia,including respiratory syncytial virus (RSV),human influenza virus, adenoviruses(ADV),human parainfluenza virus(PIV),human metapneumovirus(hMPV),human coronavirus(hCoV)and human bocavirus(hBoV)were analyzed. The viral etiology spectrum of the enrolled patients was analyzed by age-group,year,and seasonality of the detected viral etiologies were described. Results 4 508 hospitalized children less than five years old,with pneumonia from 8 hospitals were included,and 2 688(59.6%)patients were positive for at least one viral etiology. The most frequent detected virus was RSV(21.3%),followed by PIV(7.1%)and influenza(5.2%),hBoV (3.8%),ADV(3.6%)and hMPV(2.6%). The lowest positive rates in hCoV(1.1%). RSV,influenza, PIV,hBoV and hMPV all showed the nature of seasonality. Conclusion RSV was a most common viral etiology in the hospitalized young children less than 5 years of age with pneumonia. Prevention measures should be conducted to decrease its severe impact to the young infants and children in China.

OBJECTIVE:For providing evidences for further modification of China Infectious Diseases Automated-alert and Response System (CIDARS) by comparing the early-warning performance of the temporal model and temporal-spatial model in CIDARS.METHODS:The application performance for outbreak detection of temporal model and temporal-spatial model simultaneously running among 208 pilot counties in 20 provinces from 2011 to 2013 was compared; the 16 infectious diseases were divided into two classes according to the disease incidence level; cases data in nationwide Notifiable Infectious Diseases Reporting Information System was combined with outbreaks reported to Public Health Emergency Reporting System, by adopting the index of the number of signals, sensitivity, false alarm rate and time for detection.RESULTS:The overall sensitivity of temporal model and temporal-spatial model for 16 diseases was 96.23% (153/159) and 90.57% (144/159) respectively, without significant difference (Z = -1.604, P = 0.109), and the false alarm rate of temporal model (1.57%, 57 068/3 643 279) was significantly higher than that of temporal-spatial model (0.64%, 23 341/3 643 279) (Z = -3.408, P = 0.001), while the median time for detection of these two models was not significantly different, which was 3.0 days and 1.0 day respectively (Z = -1.334, P = 0.182).For 6 diseases of type I which represent the lower incidence, including epidemic hemorrhagic fever,Japanese encephalitis, dengue, meningococcal meningitis, typhus, leptospirosis, the sensitivity was 100% for both models (8/8, 8/8), and the false alarm rate of both temporal model and temporal-spatial model was 0.07% (954/1 367 437, 900/1 367 437), with the median time for detection being 2.5 days and 3.0 days respectively. The number of signals generated by temporal-spatial model was reduced by 2.29% compared with that of temporal model.For 10 diseases of type II which represent the higher incidence, including mumps, dysentery, scarlet fever, influenza, rubella, hepatitis E, acute hemorrhagic conjunctivitis, hepatitis A, typhoid and paratyphoid, and other infectious diarrhea, the sensitivity of temporal model was 96.03% (145/151), and the sensitivity of temporal-spatial model was 90.07% (136/151), the number of signals generated by temporal-spatial model was reduced by 59.36% compared with that of temporal model. Compared to temporal model, temporal-spatial model reduced both the number of signals and the false alarm rate of all the type II diseases;and the median of outbreak detection time of temporal model and temporal-spatial model was 3.0 days and 1.0 day, respectively.CONCLUSION:Overall, the temporal-spatial model had better outbreak detection performance, but the performance of two different models varies for infectious diseases with different incidence levels, and the adjustment and optimization of the temporal model and temporal-spatial model should be conducted according to specific infectious disease in CIDARS.

BACKGROUND:In 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. METHODS:Data 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. RESULTS:In 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. CONCLUSIONS:This 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.

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.

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.

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. ConclusionThe 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.

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.

A public health emergency of international concern regarding the 2014 West African Ebola epidemic was declared by the World Health Organization on August 8, 2014, in view of its potential for further international spread. Based on historic traveller flight itinerary data between October and December 2013 from the International Air Transport Association, we assessed the potential risk of Ebola virus exportation from three West African Countries, Guinea, Liberia, and Sierra Leone, into China via commercial air travel between October 1, 2014 and December 31, 2014. We found 107,113 passengers departed from the three affected countries during the fourth quarter of 2013, with 3167 people (3.0%) arriving in mainland China after transfers at the international airports of eight countries, including France, Belgium, and the U.A.E. The primary airports of entry into China for travelers from Guinea, Liberia, and Sierra Leone are located in Beijing, Guangzhou, Shanghai, Hangzhou, Wuhan, Chongqing, and Dalian, whereas the main final destinations in China include Beijing, Guangzhou, Chongqing, Wuhan, Shanghai, Hangzhou, and Wenzhou. With the assumption that travel behavior and mobility in the fourth quarter of 2014 will be the same as that of 2013, an average of 2235 international travellers would need to be screened at the points of entry into China to capture one traveler with potential exposure to Ebola virus in the three West African countries. In total, our model projects only 0.54 travellers infected with Ebola virus departing the above three countries entering China via commercial flights from October to December 2014 (0.35 infected travellers from Liberia, 0.16 from Sierra Leone, and 0.03 from Guinea). If the incidence of Ebola virus disease increases or the number of travellers to China decreases, the number of travellers with Ebola virus infection would fluctuate accordingly. This study shows that the risk of Ebola imported from West Africa to China via commercial air travel exists, although it is very low. China could support screening of departing international travellers from West Africa for the early detection of individuals with Ebola virus disease, screen high-risk traveller populations at the primary points of entry into China, and heighten surveillance in Chinas leading destination cities.

OBJECTIVE:To analyze the implement performance of China Infectious Diseases Automated-alert and Response System (CIDARS) of 31 provinces in mainland China, and to provide the evidences for further promoting the application and improvement of this system.METHODS:The amount of signals, response situation and verification outcome of signals related to 32 infectious diseases of 31 provinces in mainland China in CIDARS were investigated from 2011 to 2013, the changes by year on the proportion of responded signals and timeliness of signal response were descriptively analyzed.RESULTS:A total of 960 831 signals were generated nationwide on 32 kinds of infectious diseases in the system, with 98.87% signals (949 936) being responded, and the median (the 25(th) percentile to the 75(th) percentile (P25-P75) ) of time to response was 1.0 (0.4-3.3) h. Among all the signals, 242 355 signals were generated by the fixed-value detection method, the proportion of responded signals was 96.37% (62 349/64 703), 98.75% (68 413/69 282) and 99.37% (107 690/108 370), respectively, and the median (P25-P75) of time to response was 1.3 (0.3-9.7), 0.8(0.2-4.9) and 0.7 (0.2-4.2) h, respectively. After the preliminary data verification, field investigation and laboratory test by local public health staffs, 100 232 cases (41.36%) were finally confirmed.In addition, 718 476 signals were generated by the temporal aberration detection methods, and the average amount of signal per county per week throughout the country were 1.53, and 8 155 signals (1.14%) were verified as suspected outbreaks. During these 3 years, the proportion of signal response was 98.89% (231 149/233 746), 98.90% (254 182/257 015) and 99.31% (226 153/227 715), respectively, and the median (P25-P75) of time to response was 1.1 (0.5-3.3), 1.0 (0.5-2.9) and 1.0 (0.5-2.6) h, respectively.CONCLUSION:From 2011 to 2013, the proportion of responded signals and response timeliness of CIDARS maintained a rather high level, and further presented an increasing trend year by year. But the proportion of signals related to suspected outbreaks should be improved.

Objective Providing evidences for further modification of China Infectious Diseases Automated-alert and Response System(CIDARS)via analyzing the outbreak detection performance of Moving Percentile Method(MPM)by optimizing thresholds in different provinces.Methods We collected the amount of MPM signals,response results of signals in CIDARS,cases data in na-