Application of Functional Data Analysis to Identify Patterns of Malaria Incidence, to Guide Targeted Control Strategies.
作者： Sokhna Dieng , Pierre Michel , Abdoulaye Guindo , Kankoe Sallah , El-Hadj Ba
关键词: Epidemiological indicators 、 Biology 、 Hierarchical clustering 、 Functional data analysis 、 Malaria incidence 、 Malaria control 、 Seasonal transmission 、 Target control 、 Cartography
摘要: We introduce an approach based on functional data analysis to identify patterns of malaria incidence guide effective targeting control in a seasonal transmission area. Using method, smooth function (functional or curve) was fitted from the time series observed for each 575 villages west-central Senegal 2008 2012. These functions were classified using hierarchical clustering (Ward’s method), and several different dissimilarity measures. Validity indices used determine number distinct temporal incidence. Epidemiological indicators characterizing resulting determined velocity acceleration their incidences over time. identified three incidence: high-, intermediate-, low-incidence respectively 2% (12/575), 17% (97/575), 81% (466/575) villages. fluctuations showed that outbreaks started later, ended earlier, pattern. Functional can be incidence, by considering dynamics. derived velocities accelerations, may target measures according patterns.
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Laurent Delsol, Régression sur variable fonctionnelle : estimation, tests de structure et applications Université Paul Sabatier - Toulouse III. ,(2008)
Francois Husson, Sebastien Le, Jérôme Pagès, Exploratory Multivariate Analysis by Example Using R ,(2010)
Giles Hooker, J. O. Ramsay, Spencer Graves, Functional Data Analysis with R and MATLAB ,(2009)
Tomás Vega, Jose Eugenio Lozano, Tamara Meerhoff, René Snacken, Joshua Mott, Raul Ortiz de Lejarazu, Baltazar Nunes, Influenza surveillance in Europe: establishing epidemic thresholds by the moving epidemic method. Influenza and Other Respiratory Viruses. ,vol. 7, pp. 546- 558 ,(2013) , 10.1111/J.1750-2659.2012.00422.X
Tomás Vega, José E. Lozano, Tamara Meerhoff, René Snacken, Julien Beauté, Pernille Jorgensen, Raúl Ortiz de Lejarazu, Lisa Domegan, Joël Mossong, Jens Nielsen, Rita Born, Amparo Larrauri, Caroline Brown, Influenza surveillance in Europe: comparing intensity levels calculated using the moving epidemic method Influenza and Other Respiratory Viruses. ,vol. 9, pp. 234- 246 ,(2015) , 10.1111/IRV.12330
Pablo Montero, José A. Vilar, TSclust: An R Package for Time Series Clustering Journal of Statistical Software. ,vol. 62, pp. 1- 43 ,(2014) , 10.18637/JSS.V062.I01
D.M. Fleming, M. Zambon, A.I.M. Bartelds, J.C. de Jong, The duration and magnitude of influenza epidemics: A study of surveillance data from sentinel general practices in England, Wales and the Netherlands European Journal of Epidemiology. ,vol. 15, pp. 467- 473 ,(1999) , 10.1023/A:1007525402861
Manuel Febrero-Bande, Manuel Oviedo de la Fuente, Statistical Computing in Functional Data Analysis: TheRPackagefda.usc Journal of Statistical Software. ,vol. 51, pp. 1- 28 ,(2012) , 10.18637/JSS.V051.I04
Ahlame Douzal Chouakria, Panduranga Naidu Nagabhushan, Adaptive dissimilarity index for measuring time series proximity Advanced Data Analysis and Classification. ,vol. 1, pp. 5- 21 ,(2007) , 10.1007/S11634-006-0004-6
Peter J. Rousseeuw, Silhouettes: a graphical aid to the interpretation and validation of cluster analysis Journal of Computational and Applied Mathematics. ,vol. 20, pp. 53- 65 ,(1987) , 10.1016/0377-0427(87)90125-7