Dauji Saha
Nuclear Recycle Board, Bhabha Atomic Research Center, Mumbai, 400094 India.
Homi Bhabha National Institute, Mumbai, 400094 India.
Trans Indian Natl Acad Eng. 2021;6(2):507-521. doi: 10.1007/s41403-021-00219-w. Epub 2021 Mar 18.
Analysis of trend of epidemiological data helps to appreciate the progression of an epidemic and to develop monitoring and control strategies by the government agencies. Sen's Innovative Method suggests a graphical analysis, which can overcome many limitations of data such as short length, non-Gaussian nature, skewness or serial correlation. In this article, this method is applied for the first time on epidemiological data. For the case study, Covid-19 or SARS-CoV-2 data from India were employed. The results show that Sen's Innovative Method is capable of indicating the shift in epidemiological trend quite efficiently, before it is reflected in the time series or moving average plots. The graphical analysis worked particularly well in comparing the trends of monthly data. It is concluded that this method would be especially suitable for monitoring the epidemiological trend by breaking up the data into smaller segments, as was illustrated in the study.
分析流行病学数据趋势有助于了解疫情的发展,并帮助政府机构制定监测和控制策略。森氏创新方法提出了一种图形分析方法,该方法可以克服数据的许多局限性,如数据长度短、非高斯性质、偏度或序列相关性。在本文中,该方法首次应用于流行病学数据。在案例研究中,使用了来自印度的新冠病毒(Covid-19)或严重急性呼吸综合征冠状病毒2(SARS-CoV-2)数据。结果表明,在时间序列或移动平均图反映之前,森氏创新方法能够相当有效地指示流行病学趋势的转变。图形分析在比较月度数据趋势方面效果特别好。得出的结论是,如本研究所示,该方法通过将数据分解为较小的片段,将特别适合于监测流行病学趋势。