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Article citations


Hall, C.B., Weinberg, G.A., Iwane, M.K., et al. (2009) The Burden of Respiratory Syncytial Virus Infection in Young Children. New England Journal of Medicine, 360, 588-598.

has been cited by the following article:

  • TITLE: Application of the Probability Model for Starting Period to Initiate to Take Palivizumab by Prefecture Using National Official Sentinel Surveillance in Japan

    AUTHORS: Junko Kurita, Tamie Sugawara, Yasushi Ohkusa, Michiko Nohara

    KEYWORDS: Respiratory Syncytial Virus Infection, National Official Sentinel Surveillance Palivizumab, Probability Model, Starting Period

    JOURNAL NAME: Journal of Biosciences and Medicines, Vol.8 No.1, January 9, 2020

    ABSTRACT: Background: Palivizumab were used for the premature infant or a high-risk infant with congenital heart disease. However, recently outbreak pattern of respiratory syncytial (RS) virus infection has been varying year by year. Moreover, it also has some regional difference. Therefore, the object of the present study was to develop early detection of the timing of that outbreak had started in each prefecture. Method: We used data in National Official Sentinel Surveillance for Infectious Diseases (NOSSID). Study period was March 16th, 2011 to December 30th, 2018. We defined stating period to initiate to take palivizumab as 8 - 12 weeks before from the peak of outbreak. We estimated whether the week is included in starting period or not from April 1st to the peak of outbreak by the past number of patients of RS virus infection on week and its squared. Additionally, we have to take delay in NOSSID into consideration. Results: In nationwide, the last two seasons, the model predicted precisely the starting period. At prefectural level, the model predicted the starting period precisely in 16.6% of all year and prefectures pairs. When we consider the delay in NOSSID into consideration, the patients can start to take in 14.9% of all year and prefectures pairs. Discussion and Conclusion: The result of the probability model was not good, and thus we have to develop more sophisticated model for prediction at prefecture level.