%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Dieng, S.
%A Michel, P.
%A Guindo, A.
%A Sallah, K.
%A Ba, E. H.
%A Cisse, B.
%A Carrieri, M. P.
%A Sokhna, Cheikh
%A Milligan, P.
%A Gaudart, J.
%T Application of functional data analysis to identify patterns of malaria incidence, to guide targeted control strategies
%D 2020
%L fdi:010079147
%G ENG
%J International Journal of Environmental Research and Public Health
%K functional data analysis ; time series clustering ; malaria patterns ; malaria dynamic
%M ISI:000542629600421
%N 11
%P art. 4168 [23 ]
%R 10.3390/ijerph17114168
%U https://www.documentation.ird.fr/hor/fdi:010079147
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers20-07/010079147.pdf
%V 17
%W Horizon (IRD)
%X We introduce an approach based on functional data analysis to identify patterns of malaria incidence to guide effective targeting of malaria control in a seasonal transmission area. Using functional data method, a smooth function (functional data or curve) was fitted from the time series of observed malaria incidence for each of 575 villages in west-central Senegal from 2008 to 2012. These 575 smooth functions were classified using hierarchical clustering (Ward's method), and several different dissimilarity measures. Validity indices were used to determine the number of distinct temporal patterns of malaria incidence. Epidemiological indicators characterizing the resulting malaria incidence patterns were determined from the velocity and acceleration of their incidences over time. We identified three distinct patterns of malaria incidence: high-, intermediate-, and low-incidence patterns in respectively 2% (12/575), 17% (97/575), and 81% (466/575) of villages. Epidemiological indicators characterizing the fluctuations in malaria incidence showed that seasonal outbreaks started later, and ended earlier, in the low-incidence pattern. Functional data analysis can be used to identify patterns of malaria incidence, by considering their temporal dynamics. Epidemiological indicators derived from their velocities and accelerations, may guide to target control measures according to patterns.
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