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Objective: To calculate both the epidemic and intensity thresholds for different levels in Beijing and to establish a tiered alert system in the 2018-2019 influenza season as well as to evaluate the performance of calculated thresholds. Method: Weekly count of influenza-like illness and percentage of influenza-like illness (ILI % ) of the last five influenza seasons were modeled by 'moving epidemic method' (MEM) to calculate the influenza epidemic and intensity thresholds at different levels. A cross-validation procedure was used to evaluate the performance. Indicators of Matthew correlation coefficient, Youden's index, sensitivity and specificity were calculated. Results: For weekly count of influenza-like illness, data showed that the epidemic threshold for 2018-2019 influenza season was 12 984 and the medium, high and very high intensity thresholds were 22 503, 37 589, 47 157, respectively. Matthew correlation coefficient of the epidemic threshold was 62 % and youden's index as 60 % , sensitivity as 69 % , specificity as 91 % . Data on weekly ILI % , the epidemic threshold for 2018-2019 influenza season was 1.66 % , with medium, high and very high intensity thresholds as 2.46 % , 3.84 % and 4.66 % , respectively. The overall Matthew correlation coefficient of the epidemic threshold was 59 % , with 54 % for the Youden's index, sensitivity as 60 % and specificity as 94 % . Conclusions: MEM produced a good specific signal for detecting the influenza epidemics and the accuracy of the method was acceptable. The early warning performance regarding the application of weekly count on influenza-like illness was slightly better than ILI % . This method could be applied in the practical influenza epidemic alert "work in Beijing" .

Objective: To establish a classified evaluation system for recognizing the levels of influenza epidemics and to explore the new reporting system on influenza epidemics. Methods: The following 3 indicators, including 1) the number of influenza-like illness, 2) positive rate of detection on influenza virus nucleic acids, and 3) the number of influenza outbreaks were chosen to calculate the synthetic index and to classify the grades of evaluation. Results: 209 weeks during 2013-2017 were classified into 5 grades: Grade 1 were 110 weeks (52.63%), Grade 2 were 47 weeks (22.49%), Grade 3 were 44 weeks (21.05%), Grade 4 were 8 weeks (3.83%), and Grade 5 were 0 week. Conclusion: This classified evaluation system provided simple, comprehensive and comparable reference indicators and used for the evaluation on influenza epidemics, also providing suggestions for influenza prevention and control accordingly.

Objective: The objective was to identify the incidence of influenza-like syndromes and related health behavior factors among Beijing residents. Methods: From December 6, 2013 to January 16, 2014, we selected 150 villages or communities from 30 towns or streets as survey locations using a multi-stage random sampling method, and then conducted a cross-sectional study among 7 354 residents who aged 18 years or above and had live in Beijing for more than a half year using self-administered anonymous questionnaires, and totally 7 327 valid questionnaires are collected. The questionnaire consisted of demographic information, self-reported influenza-like syndromes in the past two weeks, and health behaviors. Multiple logistic regression models were used to identify the factors associated with self-reported influenza-like syndromes. Results: The mean (SD) age of the partcipants was 44.6 (15.2) years. Among them, 6.9% (506 cases) reported having influenza like illness during the past two weeks. The multiple logistic regression analysis indicated that regular physical exercise, optimal hand hygiene, and avoidance of going to the crowded places during respiratory infectious disease epidemics were significantly associated with a lower likelihood of reporting influenza-like syndromes, compared with those without regular physical exercises, without optimal hand hygiene, and not avoiding going to the crowded places, and the OR (95 %CI ) were 0.80 (0.66-0.97), 0.75 (0.57-0.99) and 0.80 (0.65-0.98), respectively. Conclusion: Personal health behaviors were associated with the incidence of respiratory infectious diseases such as influenza in Beijing, and future interventions to improve personal hygiene behaviors are needed to prevent the spread of respiratory infectious diseases.

Objective: To estimate the influenza infection rate among severe acute respiratory infection(SARI) cases and the hospitalization rates of SARI attributable to influenza, based on two sentinel hospital surveillance databases in Beijing, 2015. Methods: Surveillance was conducted at two sentinel hospitals in Beijing in 2015. A total of 1 842 patients who admitted to the sentinel hospitals and met the definition of SARI were enrolled in the study. The respiratory tract specimens of SARI cases were collected, and sent to laboratories within 48 hours for influenza RNA detection. The catchment area of sentinel hospitals was defined by reviewing the home address of inpatients; A total of 1 491 patients were sampled and tested for influenza. The population size of catchment areas was obtained from demographic year book. We investigated the number of pneumonia patients admitted to the sentinel hospitals and other hospitals in catchment areas in 2015, and calculated the proportions of pneumonia patients that were admitted to sentinel hospitals in catchment areas. The catchment population size was calculated using the number of total population of catchment areas multiply by the proportions of pneumonia patients that were admitted at sentinel hospitals. Results: Among 1 491 patients, 13.7% (205 cases) was test positive for influenza viruses, 2 (0.9%) cases positive for influenza A (H1N1), 91 (44.6%) cases influenza A (H3N2), 1 (0.5%) case influenza B/Victoria, 111 (54.0%) cases influenza B/Yamagata. Influenza was associated with an estimated 30 (95 %CI :9-51) SARI hospitalizations per 100 000 during 2015. The hospitalization rate was 243 (95 %CI : 232-255), 86 (95 %CI : 59-112),1(95 %CI : 0-5), 8 (95 %CI : 0-23) and 92 (95 %CI : 16-168) SARI hospitalizations per 100 000 population for<5 years children, 5-14 years children, 15-24 years adult, 25-59 years adult and ≥60 years population, respectively. The hospitalization rate of SARI attributed to influenza A and B was 14 (95 %CI :4-17) and 16 (95 %CI :0-23) per 100 000 population, respectively. Conclusion: The influenza positive rate among SARI cases was relatively high. The hospitalization burden of SARI attributed to influenza was the greatest in children under 5 year-old.

Objective: To investigate human exposure to live poultry (poultry feeding and purchasing) in the residents in Beijing and related factors during the second wave of avian influenza A(H7N9) epidemic during 2013-2014, and provide scientific evidence for avian influenza prevention and control. Methods: A total of 7 366 adults aged ≥18 years were selected through multi-stage stratified sampling in Beijing for a questionnaire survey. Logistic regression model was used to analyze the influence factors of human exposure to live poultry. Results: The live poultry feeding rate and live poultry purchasing rate in residents in Beijing in the past year were 5.3% (95 %CI : 4.8%-5.8%) and 6.0% (95 %CI : 5.5%-6.5%) respectively. Logistic regression analysis indicated that lower educational level of primary school and below, ( OR =1.82, 95 %CI : 1.22-2.72); being farmer ( OR =2.49, 95 %CI :1.89-3.29) or being unemployed ( OR =1.65, 95 %CI : 1.08-2.52); being non local resident ( OR =1.54, 95 %CI : 1.10-2.16); living in suburban area ( OR =2.36, 95 %CI : 1.77-3.16); having one child ( OR =1.76, 95 %CI : 1.42-2.17) or ≥2 children ( OR =2.15, 95 %CI : 1.43-3.22) in the family were the risk factors associated with feeding poultry compared with higher educational level of college and above, being employed, being local resident, living in urban area and having no child. And being farmer ( OR =1.61, 95 %CI : 1.27-2.02); being non local resident ( OR =1.76, 95 %CI : 1.31-2.35); living in suburban area ( OR =2.05, 95 %CI : 1.61-2.61); having one child ( OR =1.24, 95 %CI : 1.02-1.52) or ≥2 children ( OR =1.78, 95 %CI : 1.21-2.63) were the risk factors for purchasing live poultry. Conclusion: Some residents living in Beijing still have exposure to live poultry, and targeted measures should be taken to reduce the exposure to poultry.

In order to determine the prevalence of antibody against 2009 H1N1 influenza in Beijing, we conducted a serological survey in 710 subjects, 1 month after the epidemic peak. We found that 13·8% of our cohort was seropositive. Subjects aged ≥60 years recorded the lowest seroprevalence (4·5%). The age-weighted seroprevalence of 14·0% was far lower than the supposed infection rate at the epidemic peak, derived from the basic reproduction number for 2009 H1N1 virus. For subjects who had received the pandemic vaccine seroprevalence was 51·4%. In subjects aged ≥60 years the seasonal influenza vaccination was not significantly associated with being seropositive. Our study suggests that many factors, and not just the immunological level against 2009 H1N1 influenza in the community, affected the spread of the virus within the population of Beijing.