文章基本信息

标题：Forecasting H1N1 Activity in India
本地全文：下载
作者：Divyani Paul ; Bhola Nath Paul
期刊名称：Current Journal of Applied Science and Technology
印刷版ISSN：2457-1024
出版年度：2015
卷号：12
期号：4
页码：1-8
语种：English
出版社：Sciencedomain International
摘要：Aims: Dynamics of invariant influenza frequently defies intuition and qualitative forecasting. In view of the re-emergence of H1N1 infection in India, as well as the rise in H1N1 cases and associated fatalities in the current year (2015), quantitative forecasting of swine flu dynamics is performed here.Methodology: Case fatality rates (CFR) are well established predictors of adverse human health outcomes, independent of overall disease status. To increase our understanding of the potential severity of outbreaks of H1N1 influenza in India, we study the pattern H1N1 infections since the outbreak of pandemic H1N1 in 2009 and derive both linear and non-linear trends for forecasting severity of infection based on CFR of H1N1 cases. Open access data available at different data bases WHO National Influenza Centre, www.who.inf/flunet; FluTrackers.com (flutrackers.com/forum/forum/india/seasonal flu-2009-2014; Press Information Bureau, Government of India, http://pib.nic.in/newsite/erelease. aspx?relid=107145; http://pib.nic.in/newsite/erelease.aspx?relid=115361) and media (http://www.tribuneindia.com/news/nat...jab/25608html), are utilised in this study. The trendline forecast were derived from Microsoft-Excel using scatter chart and trendline option.Results: Time series forecasting of H1N1 infection using smoothing methods reveal infection peaking during winter and rainy seasons of a year. Downward sloping polynomial curve (R2=0.931) indicate declining trend of H1N1-infection over time. Polynomial forecasting the severity of H1N1 infection based on H1N1 case fatality rate (CFR), it is predicted to rise by ~20% in 2015 in comparison to 2014, and 30% in 2016 in comparison to 2015. Conclusion: In the absence of effective control measures, the H1N1 fatalities are predicted to assume severe effect in the winter of 2015 and in 2016. Possibly, disproportionate control measures, loss of immunity and a possible antigenic drift in H1N1 virus underlie the re-emergence of viral onslaught in 2015 in India.
关键词：H1N1 infection;swine-flu prediction