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Spatio-Temporal Variability of Malaria Incidence in the Health District of Kati, Mali, 2015–2019

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  • Abdoulaye Katile

    (INSERM, IRD, SESSTIM, ISSPAM, UMR1252, Faculty of Medicine, Aix Marseille University, 13005 Marseille, France
    Malaria Research and Training Center (MRTC), FMOS-FAPH, Mali-NIAID-ICER, Université des Sciences, des Techniques et des Technologies de Bamako, Bamako BP 423, Mali)

  • Issaka Sagara

    (INSERM, IRD, SESSTIM, ISSPAM, UMR1252, Faculty of Medicine, Aix Marseille University, 13005 Marseille, France
    Malaria Research and Training Center (MRTC), FMOS-FAPH, Mali-NIAID-ICER, Université des Sciences, des Techniques et des Technologies de Bamako, Bamako BP 423, Mali)

  • Mady Cissoko

    (INSERM, IRD, SESSTIM, ISSPAM, UMR1252, Faculty of Medicine, Aix Marseille University, 13005 Marseille, France
    Malaria Research and Training Center (MRTC), FMOS-FAPH, Mali-NIAID-ICER, Université des Sciences, des Techniques et des Technologies de Bamako, Bamako BP 423, Mali)

  • Cedric Stephane Bationo

    (INSERM, IRD, SESSTIM, ISSPAM, UMR1252, Faculty of Medicine, Aix Marseille University, 13005 Marseille, France)

  • Mathias Dolo

    (Malaria Research and Training Center (MRTC), FMOS-FAPH, Mali-NIAID-ICER, Université des Sciences, des Techniques et des Technologies de Bamako, Bamako BP 423, Mali)

  • Ismaila Thera

    (Malaria Research and Training Center (MRTC), FMOS-FAPH, Mali-NIAID-ICER, Université des Sciences, des Techniques et des Technologies de Bamako, Bamako BP 423, Mali)

  • Siriman Traore

    (Malaria Research and Training Center (MRTC), FMOS-FAPH, Mali-NIAID-ICER, Université des Sciences, des Techniques et des Technologies de Bamako, Bamako BP 423, Mali)

  • Mamady Kone

    (Malaria Research and Training Center (MRTC), FMOS-FAPH, Mali-NIAID-ICER, Université des Sciences, des Techniques et des Technologies de Bamako, Bamako BP 423, Mali)

  • Pascal Dembele

    (Programme National de Lutte Contre le Paludisme, Bamako BP 233, Mali)

  • Djoouro Bocoum

    (Direction Nationale de L’Hydraulique, Bamako BP 66, Mali)

  • Ibrahima Sidibe

    (Direction Nationale de L’Hydraulique, Bamako BP 66, Mali)

  • Ismael Simaga

    (Centre de Santé de Référence du District Sanitaire de Kati, Région de Koulikoro, Kati BP 594, Mali)

  • Mahamadou Soumana Sissoko

    (Malaria Research and Training Center (MRTC), FMOS-FAPH, Mali-NIAID-ICER, Université des Sciences, des Techniques et des Technologies de Bamako, Bamako BP 423, Mali)

  • Jordi Landier

    (INSERM, IRD, SESSTIM, ISSPAM, UMR1252, Faculty of Medicine, Aix Marseille University, 13005 Marseille, France)

  • Jean Gaudart

    (Malaria Research and Training Center (MRTC), FMOS-FAPH, Mali-NIAID-ICER, Université des Sciences, des Techniques et des Technologies de Bamako, Bamako BP 423, Mali
    APHM, INSERM, IRD, SESSTIM, ISSPAM, UMR1252, Hop Timone, BioSTIC, Biostatistic & ICT, Faculty of Medicine, Aix Marseille University, 13005 Marseille, France)

Abstract

Introduction: Despite the implementation of control strategies at the national scale, the malaria burden remains high in Mali, with more than 2.8 million cases reported in 2019. In this context, a new approach is needed, which accounts for the spatio-temporal variability of malaria transmission at the local scale. This study aimed to describe the spatio-temporal variability of malaria incidence and the associated meteorological and environmental factors in the health district of Kati, Mali. Methods: Daily malaria cases were collected from the consultation records of the 35 health areas of Kati’s health district, for the period 2015–2019. Data on rainfall, relative humidity, temperature, wind speed, the normalized difference vegetation index, air pressure, and land use–land cover were extracted from open-access remote sensing sources, while data on the Niger River’s height and flow were obtained from the National Department of Hydraulics. To reduce the dimension and account for collinearity, strongly correlated meteorological and environmental variables were combined into synthetic indicators (SI), using a principal component analysis. A generalized additive model was built to determine the lag and the relationship between the main SIs and malaria incidence. The transmission periods were determined using a change-point analysis. High-risk clusters (hotspots) were detected using the SatScan method and were ranked according to risk level, using a classification and regression tree analysis. Results: The peak of the malaria incidence generally occurred in October. Peak incidence decreased from 60 cases per 1000 person–weeks in 2015, to 27 cases per 1000 person–weeks in 2019. The relationship between the first SI (river flow and height, relative humidity, and rainfall) and malaria incidence was positive and almost linear. A non-linear relationship was found between the second SI (air pressure and temperature) and malaria incidence. Two transmission periods were determined per year: a low transmission period from January to July—corresponding to a persisting transmission during the dry season—and a high transmission period from July to December. The spatial distribution of malaria hotspots varied according to the transmission period. Discussion: Our study confirmed the important variability of malaria incidence and found malaria transmission to be associated with several meteorological and environmental factors in the Kati district. The persistence of malaria during the dry season and the spatio-temporal variability of malaria hotspots reinforce the need for innovative and targeted strategies.

Suggested Citation

  • Abdoulaye Katile & Issaka Sagara & Mady Cissoko & Cedric Stephane Bationo & Mathias Dolo & Ismaila Thera & Siriman Traore & Mamady Kone & Pascal Dembele & Djoouro Bocoum & Ibrahima Sidibe & Ismael Sim, 2022. "Spatio-Temporal Variability of Malaria Incidence in the Health District of Kati, Mali, 2015–2019," IJERPH, MDPI, vol. 19(21), pages 1-18, November.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:14361-:d:961531
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    References listed on IDEAS

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    1. Aline Guttmann & Xinran Li & Fabien Feschet & Jean Gaudart & Jacques Demongeot & Jean-Yves Boire & Lemlih Ouchchane, 2015. "Cluster Detection Tests in Spatial Epidemiology: A Global Indicator for Performance Assessment," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-17, June.
    2. Oguntade Emmanuel Segun & Shamarina Shohaimi & Meenakshii Nallapan & Alaba Ajibola Lamidi-Sarumoh & Nader Salari, 2020. "Statistical Modelling of the Effects of Weather Factors on Malaria Occurrence in Abuja, Nigeria," IJERPH, MDPI, vol. 17(10), pages 1-12, May.
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