Consistent with previous findings which were conducted in healthcare workers and nurses [13, 17] , we confirm that the acceptance rate of pandemic influenza vaccination is low amongst community nurses. Since community nurses are at high risk of contracting influenza infection, and play a significant role in caring for community cases, special attention should be paid to this group as successful vaccination strategy has been shown to be beneficial in disease transmission [27] . Future work, including interventional studies evaluating potential interventions based on the ecological model or interventions that aim to increase awareness among nurses on the importance of vaccination in healthcare workers to protect vulnerable populations [16] is needed. The need to address low influenza vaccination rates in this high-risk group is urgent in the context of pandemic response. Development of an optimized RNA-based murine norovirus reverse genetics system Murine norovirus (MNV), identified in 2003, is the only norovirus which replicates efficiently in tissue culture and as a result has been used extensively as a model for human noroviruses, a major cause of acute gastroenteritis. The current report describes the generation of a new approach to reverse genetics recovery of genetically defined MNV that relies on the transfection of in vitro transcribed capped RNA directly into cells. The use of the recently developed ScriptCap post-transcriptional enzymatic capping system, followed by optimized Neon mediated electroporation of the highly permissive RAW 264.7 cells, resulted in the rapid and robust recovery of infectious MNV. Transfection of cells capable of supporting virus replication but not permissive to virus infection, namely human or hamster kidney cells, also resulted in robust recovery of infectious virus without subsequent amplification by multiple rounds of re-infection. This latter system may provide a reproducible method to measure the specific infectivity of mutant norovirus RNA allowing the accurate quantitation of the effect of mutations on norovirus replication.  On epidemic modeling in real time: An application to the 2009 Novel A (H1N1) influenza outbreak in Canada BACKGROUND: Management of emerging infectious diseases such as the 2009 influenza pandemic A (H1N1) poses great challenges for real-time mathematical modeling of disease transmission due to limited information on disease natural history and epidemiology, stochastic variation in the course of epidemics, and changing case definitions and surveillance practices. FINDINGS: The Richards model and its variants are used to fit the cumulative epidemic curve for laboratory-confirmed pandemic H1N1 (pH1N1) infections in Canada, made available by the Public Health Agency of Canada (PHAC). The model is used to obtain estimates for turning points in the initial outbreak, the basic reproductive number (R(0)), and for expected final outbreak size in the absence of interventions. Confirmed case data were used to construct a best-fit 2-phase model with three turning points. R(0 )was estimated to be 1.30 (95% CI 1.12-1.47) for the first phase (April 1 to May 4) and 1.35 (95% CI 1.16-1.54) for the second phase (May 4 to June 19). Hospitalization data were also used to fit a 1-phase model with R(0 )= 1.35 (1.20-1.49) and a single turning point of June 11. CONCLUSIONS: Application of the Richards model to Canadian pH1N1 data shows that detection of turning points is affected by the quality of data available at the time of data usage. Using a Richards model, robust estimates of R(0 )were obtained approximately one month after the initial outbreak in the case of 2009 A (H1N1) in Canada. Epidemics and outbreaks caused by emerging infectious diseases continue to challenge medical and public health authorities. Outbreak and epidemic control requires swift action, but real-time identification and characterization of epidemics remains difficult [1] . Methods are needed to inform real-time decision making through rapid characterization of disease epidemiology, prediction of shortterm disease trends, and evaluation of the projected impacts of different intervention measures. Real-time mathematical modeling and epidemiological analysis are important tools for such endeavors, but the limited public availability of information on outbreak epidemiology (particularly when the outbreak creates a crisis environment), and on the characteristics of any novel pathogen, present obstacles to the creation of reliable and credible models during a public health emergency. One needs to look no further than the 2003 SARS outbreak, or ongoing concerns related to highly pathogenic avian influenza (H5N1) or bioterrorism to be reminded of the need for and difficulty of real-time modeling.