Application of Functional Data Analysis to Identify Patterns of Malaria Incidence, to Guide Targeted Control Strategies

Bibliographic Data

ID15468697
AuthorsSokhna Dieng (0000-0003-0603-1986, Aix-Marseille Université, corresponding author), Pierre Michel (0000-0002-6442-2566, Centrale Marseille), Abdoulaye Guindo (0000-0001-8308-1675, Economic & Social Sciences, Health Systems & Medical Informatics), Kankoé Sallah (0000-0003-0320-604X, Aix-Marseille Université), El-Hadj Bâ (0000-0001-8935-6529, Institut de Recherche pour le Développement), Badara Cissé (Institute of Health Research, Epidemiological Surveillance and Training), Patrizia Carrieri (0000-0002-6794-4837, Economic & Social Sciences, Health Systems & Medical Informatics), Cheikh Sokhna (0000-0003-4810-8232, Institut de Recherche pour le Développement), Paul Milligan (0000-0003-3430-3395, London School of Hygiene & Tropical Medicine), Jean Gaudart (0000-0001-9006-5729, Inserm)
Year2020
Volume17
Issue11
Pages4168-4168
Publication date2020-06-11
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph17114168
PMID32545302
OpenAlexW3034762108
LanguageEN
References cited41

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

Control (management · Data science · Environmental health · Incidence (geometry · Malaria · Computer Science · Data-Driven Disease Surveillance · Identification and Quantification in Food · Mathematics · Medicine · Metabolomics and Mass Spectrometry Studies · Artificial Intelligence · Immunology

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