tetano
Editor, Senior Moderator
Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub. 2017 Oct 17. doi: 10.5507/bp.2017.042. [Epub ahead of print]
[h=1]Analysis of the seasonal incidence of acute respiratory infections including influenza (ARI) in the Czech Republic - possible contribution of the functional data boxplot in epidemiology.[/h] Vencalek O[SUP]1[/SUP], Kyncl J[SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]AIMS:[/h] The detection of an epidemic outbreak is possible only if the baseline incidence level of a given disease is well defined. The determination of the baseline is complicated by the presence of epidemic outbreaks in historical data. The aim of the paper is to provide a new way of determining the baseline.
[h=4]METHODS:[/h] The analyzed data containing weekly records on the incidence of acute respiratory infections including influenza (ARI) in the Czech Republic and its regions are taken from the nationwide surveillance system; data on 15 seasons from 2001/02 to 2015/16 are included. Functional boxplots of the data are constructed and five distinct methods (componentwise mean, componentwise median, median, trimmed mean, and adjusted mean) were used for the computation of the baseline level function.
[h=4]RESULTS:[/h] It was shown that the methods based on functional data analysis could successfully overcome the problems that arise when the conventional methods are used for the determination of the baseline function.
[h=4]CONCLUSION:[/h] The functional boxplot - a new statistical tool - can bring not only a transparent visualisation of comprehensive data, but can also help epidemiologists and other public health experts to determine the baseline incidence level of a given disease as well as to detect unusual epidemic seasons.
PMID: 29042708 DOI: 10.5507/bp.2017.042
[h=1]Analysis of the seasonal incidence of acute respiratory infections including influenza (ARI) in the Czech Republic - possible contribution of the functional data boxplot in epidemiology.[/h] Vencalek O[SUP]1[/SUP], Kyncl J[SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]AIMS:[/h] The detection of an epidemic outbreak is possible only if the baseline incidence level of a given disease is well defined. The determination of the baseline is complicated by the presence of epidemic outbreaks in historical data. The aim of the paper is to provide a new way of determining the baseline.
[h=4]METHODS:[/h] The analyzed data containing weekly records on the incidence of acute respiratory infections including influenza (ARI) in the Czech Republic and its regions are taken from the nationwide surveillance system; data on 15 seasons from 2001/02 to 2015/16 are included. Functional boxplots of the data are constructed and five distinct methods (componentwise mean, componentwise median, median, trimmed mean, and adjusted mean) were used for the computation of the baseline level function.
[h=4]RESULTS:[/h] It was shown that the methods based on functional data analysis could successfully overcome the problems that arise when the conventional methods are used for the determination of the baseline function.
[h=4]CONCLUSION:[/h] The functional boxplot - a new statistical tool - can bring not only a transparent visualisation of comprehensive data, but can also help epidemiologists and other public health experts to determine the baseline incidence level of a given disease as well as to detect unusual epidemic seasons.
PMID: 29042708 DOI: 10.5507/bp.2017.042