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

Author

Listed:
  • Sokhna Dieng

    (SESSTIM - U1252 INSERM - Aix Marseille Univ - UMR 259 IRD - Sciences Economiques et Sociales de la Santé & Traitement de l'Information Médicale - IRD - Institut de Recherche pour le Développement - AMU - Aix Marseille Université - INSERM - Institut National de la Santé et de la Recherche Médicale)

  • Pierre Michel

    (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Abdoulaye Guindo

    (SESSTIM - U1252 INSERM - Aix Marseille Univ - UMR 259 IRD - Sciences Economiques et Sociales de la Santé & Traitement de l'Information Médicale - IRD - Institut de Recherche pour le Développement - AMU - Aix Marseille Université - INSERM - Institut National de la Santé et de la Recherche Médicale, MERIT - UMR_D 261 - Mère et enfant en milieu tropical : pathogènes, système de santé et transition épidémiologique - IRD - Institut de Recherche pour le Développement - UPCité - Université Paris Cité)

  • Kankoé L Sallah

    (SESSTIM - U1252 INSERM - Aix Marseille Univ - UMR 259 IRD - Sciences Economiques et Sociales de la Santé & Traitement de l'Information Médicale - IRD - Institut de Recherche pour le Développement - AMU - Aix Marseille Université - INSERM - Institut National de la Santé et de la Recherche Médicale, HUPNVS - Hôpitaux Universitaires Paris Nord Val de Seine)

  • El-Hadj Ba

    (VITROME - Vecteurs - Infections tropicales et méditerranéennes - IRD - Institut de Recherche pour le Développement - AMU - Aix Marseille Université - IRBA - Institut de Recherche Biomédicale des Armées [Brétigny-sur-Orge])

  • Badara Cisse

    (IRESSEF - Institut de Recherche en Santé, de Surveillance Épidémiologique et de Formation)

  • Maria Patrizia Carrieri

    (SESSTIM - U1252 INSERM - Aix Marseille Univ - UMR 259 IRD - Sciences Economiques et Sociales de la Santé & Traitement de l'Information Médicale - IRD - Institut de Recherche pour le Développement - AMU - Aix Marseille Université - INSERM - Institut National de la Santé et de la Recherche Médicale)

  • Cheikh Sokhna

    (VITROME - Vecteurs - Infections tropicales et méditerranéennes - IRD - Institut de Recherche pour le Développement - AMU - Aix Marseille Université - IRBA - Institut de Recherche Biomédicale des Armées [Brétigny-sur-Orge])

  • Paul Milligan

    (LSHTM - London School of Hygiene and Tropical Medicine)

  • Jean Gaudart

    (SESSTIM - U1252 INSERM - Aix Marseille Univ - UMR 259 IRD - Sciences Economiques et Sociales de la Santé & Traitement de l'Information Médicale - IRD - Institut de Recherche pour le Développement - AMU - Aix Marseille Université - INSERM - Institut National de la Santé et de la Recherche Médicale)

Abstract

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.

Suggested Citation

  • Sokhna Dieng & Pierre Michel & Abdoulaye Guindo & Kankoé L Sallah & El-Hadj Ba & Badara Cisse & Maria Patrizia Carrieri & Cheikh Sokhna & Paul Milligan & Jean Gaudart, 2020. "Application of Functional Data Analysis to Identify Patterns of Malaria Incidence, to Guide Targeted Control Strategies," Post-Print hal-02866666, HAL.
    • Handle: RePEc:hal:journl:hal-02866666
    DOI: 10.3390/ijerph17114168
    Note: View the original document on HAL open archive server: https://amu.hal.science/hal-02866666
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    References listed on IDEAS

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    3. Fionn Murtagh & Pierre Legendre, 2014. "Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?," Journal of Classification, Springer;The Classification Society, vol. 31(3), pages 274-295, October.
    4. Montero, Pablo & Vilar, José A., 2014. "TSclust: An R Package for Time Series Clustering," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i01).
    5. Gaudart, Jean & Graffeo, Nathalie & Coulibaly, Drissa & Barbet, Guillaume & Rebaudet, Stanilas & Dessay, Nadine & Doumbo, Ogobara K. & Giorgi, Roch, 2015. "SPODT: An R Package to Perform Spatial Partitioning," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i16).
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    Cited by:

    1. Weiwei Wang & Futian Weng & Jianping Zhu & Qiyuan Li & Xiaolong Wu, 2023. "An Analytical Approach for Temporal Infection Mapping and Composite Index Development," Mathematics, MDPI, vol. 11(20), pages 1-16, October.

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    Keywords

    functional data analysis; time series clustering; malaria patterns; malaria dynamic;
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