Transmission characteristics of different students during a school outbreak of (H1N1) pdm09 influenza in China, 2009.

Many outbreaks of A(H1N1)pdm09 influenza have occurred in schools with a high population density. Containment of school outbreaks is predicted to help mitigate pandemic influenza. Understanding disease transmission characteristics within the school setting is critical to implementing effective control measures. Based on a school outbreak survey, we found almost all (93.7%) disease transmission occurred within a single grade, only 6.3% crossed grades. Transmissions originating from freshmen exhibited a star-shaped network; other grades exhibited branch- or line-shaped networks, indicating freshmen have higher activity and are more likely to cause infection. R0 for freshmen, calculated as 2.04, estimated as 2.76, was greater than for other grades (P < 0.01). Without intervention, the estimated number of cases was much greater when the outbreak was initiated by freshmen than by other grades. Furthermore, the estimated number of cases required to be under quarantine and isolation for freshmen was less than that of equivalent other grades. So we concluded that different grades have different transmission mode. Freshmen were the main facilitators of the spread of A(H1N1)pdm09 influenza during this school outbreak, so control measures (e.g. close contact isolation) priority used for freshmen would likely have effectively reduced spread of influenza in school settings.

Spatio-temporal evolution of Beijing 2003 SARS epidemic.

Studying spatio-temporal evolution of epidemics can uncover important aspects of interaction among people, infectious diseases, and the environment, providing useful insights and modeling support to facilitate public health response and possibly prevention measures. This paper presents an empirical spatio-temporal analysis of epidemiological data concerning 2321 SARS-infected patients in Beijing in 2003. We mapped the SARS morbidity data with the spatial data resolution at the level of street and township. Two smoothing methods, Bayesian adjustment and spatial smoothing, were applied to identify the spatial risks and spatial transmission trends. Furthermore, we explored various spatial patterns and spatio-temporal evolution of Beijing 2003 SARS epidemic using spatial statistics such as Moran's I and LISA. Part of this study is targeted at evaluating the effectiveness of public health control measures implemented during the SARS epidemic. The main findings are as follows. (1) The diffusion speed of SARS in the northwest-southeast direction is weaker than that in northeast-southwest direction. (2) SARS's spread risk is positively spatially associated and the strength of this spatial association has experienced changes from weak to strong and then back to weak during the lifetime of the Beijing SARS epidemic. (3) Two spatial clusters of disease cases are identified: one in the city center and the other in the eastern suburban area. These two clusters followed different evolutionary paths but interacted with each other as well. (4) Although the government missed the opportunity to contain the early outbreak of SARS in March 2003, the response strategies implemented after the mid of April were effective. These response measures not only controlled the growth of the disease cases, but also mitigated the spatial diffusion.

[Quantitative evaluation on the effectiveness of prevention and control measures against pandemic influenza A (H1N1) in Beijing, 2009].

OBJECTIVE: To quantitatively evaluate the effectiveness of prevention and control measures against pandemic influenza A (H1N1) in Beijing, 2009 and to provide evidence for developing and adjusting strategies for prevention and control of the disease. METHODS: Considering the seasonality and the number of vaccination on pandemic influenza A (H1N1), data regarding pandemic influenza A (H1N1) in Beijing were collected and analyzed. Based on the dynamics of infectious disease transmission, a quantitative model for evaluation of prevention and control measures was developed. RESULTS: Both latency and infectious periods of pandemic influenza A (H1N1) were estimated to be 1.82 days and 2.08 days, respectively. The effective reproduction numbers of the three periods were 1.13, 1.65 and 0.96, respectively. Thanks to the implementation of a series of measures to prevent and control pandemic influenza A (H1N1), the cumulative number of laboratory-confirmed cases of pandemic influenza A (H1N1) was reduced, making it much smaller than what would have been under the natural situation. Specifically, the program on pandemic (H1N1) 2009 vaccination reduced the cumulative number of laboratory-confirmed cases by 24.08% and postponed the peak time. CONCLUSION: Measures that had been taken during this period, had greatly contributed to the successful prevention and control of pandemic influenza A (H1N1). The 2009 Pandemic (H1N1) vaccination was confirmed to have contributed to the decrease of cumulative number of laboratory-confirmed cases and postponed the peak arrival time.

[Comparison between early outbreak detection models and simulated outbreaks of measles in Beijing].

OBJECTIVE: Using simulated outbreaks to choose the optimal model and its related parameters on measles so as to provide technical support for developing an Auto Warning System (AWS). METHODS: AEGIS-Cluster Creation Tool was applied to simulate a range of unique outbreak signals. Then these simulations were added to the actual daily counts of measles from the National Disease Surveillance System, between 2005 and 2007. Exponential weighted moving average (EWMA), C1-MILD (C1), C2-MEDIUM (C2), C3-ULTRA (C3) and space-time permutation scan statistic model were comprehensively applied to detect these simulations. Tools for evaluation as Youden' s index and detection time were calculated to optimize parameters before an optimal model was finally chosen. RESULTS: EWMA (lamda = 0.6, k = 1.0), CI (k = 0.1, H=3sigma), C2 (k = 0.1, H=3sigma), C3 (k = 1.0, H=4sigma) and space-time permutation scan statistic (maximum temporal cluster size=7 d, maximum spatial cluster size = 5 km) appeared to be the optimal parameters among these models. Youden's index of EWMA was 90.8% and detection time being 0.121 d. Youden's index of C1 was 88.7% and detection time being 0.142 d. Youden's index of C2 was 92.9% and detection time being 0.121 d. Youden's index of C3 was 87.9% and detection time being 0.058 d. Youden's index of space-time permutation scan statistic was 94.3% and detection time being 0.176 d. CONCLUSION: Among these five early warning detection models, space-time permutation scan statistic model had the highest efficacy.