Remote sensing and multi-criteria evaluation for malaria risk mapping to support indoor residual spraying prioritization in the Central highlands of Madagascar

Rakotoarison Hobiniaina Anthonio, Rasamimalala Mampionona, Rakotondramanga Jean Marius, Ramiranirina Brune, Franchard Thierry, Kapesa Laurent, Razafindrakoto Jocelyn, Guis Hélène, Tantely Luciano Michaël, Girod Romain, Rakotoniaina Solofoarisoa, Baril Laurence, Piola Patrice, Rakotomanana Fanjasoa. 2020. Remote sensing and multi-criteria evaluation for malaria risk mapping to support indoor residual spraying prioritization in the Central highlands of Madagascar. Remote Sensing, 12, n.spéc. Remote Sensing for Health: from Fine-Scale Investigations towards Early-Warning Systems:1585, 22 p.

Quartile : Q1, Sujet : GEOSCIENCES, MULTIDISCIPLINARY / Quartile : Q2, Sujet : ENVIRONMENTAL SCIENCES / Quartile : Q2, Sujet : IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY / Quartile : Q2, Sujet : REMOTE SENSING

Résumé : The National Malaria Control Program (NMCP) in Madagascar classifies Malagasy districts into two malaria situations: districts in the pre-elimination phase and districts in the control phase. Indoor residual spraying (IRS) is identified as the main intervention means to control malaria in the Central Highlands. However, it involves an important logistical mobilization and thus necessitates prioritization of interventions according to the magnitude of malaria risks. Our objectives were to map the malaria transmission risk and to develop a tool to support the Malagasy Ministry of Public Health (MoH) for selective IRS implementation. For the 2014–2016 period, different sources of remotely sensed data were used to update land cover information and substitute in situ climatic data. Spatial modeling was performed based on multi-criteria evaluation (MCE) to assess malaria risk. Models were mainly based on environment and climate. Three annual malaria risk maps were obtained for 2014, 2015, and 2016. Annual parasite incidence data were used to validate the results. In 2016, the validation of the model using a receiver operating characteristic (ROC) curve showed an accuracy of 0.736; 95% CI [0.669–0.803]. A free plugin for QGIS software was made available for NMCP decision makers to prioritize areas for IRS. An annual update of the model provides the basic information for decision making before each IRS campaign. In Madagascar and beyond, the availability of the free plugin for open-source software facilitates the transfer to the MoH and allows further application to other problems and contexts.

Mots-clés Agrovoc : malaria, télédétection, évaluation du risque, distribution spatiale

Mots-clés géographiques Agrovoc : Madagascar

Mots-clés libres : Malaria, Paludisme, MCDA, Multi criteria assessment, Multi-criteria evaluation, Madagascar, Remote Sensing, Spatial modelling, Risk assessment, Maladie vectorielle

Classification Agris : L73 - Maladies des animaux
U30 - Méthodes de recherche

Champ stratégique Cirad : CTS 4 (2019-) - Santé des plantes, des animaux et des écosystèmes

Auteurs et affiliations

• Rakotoarison Hobiniaina Anthonio, CIRAD-ES-UMR TETIS (REU) - auteur correspondant
• Rasamimalala Mampionona, Institut Pasteur de Madagascar (MDG)
• Rakotondramanga Jean Marius, Institut Pasteur de Madagascar (MDG)
• Ramiranirina Brune, Ministère de la santé publique (Madagascar) (MDG)
• Franchard Thierry, Ministère de la santé publique (Madagascar) (MDG)
• Kapesa Laurent, USAID (MDG)
• Razafindrakoto Jocelyn, USAID (MDG)
• Guis Hélène, CIRAD-BIOS-UMR ASTRE (MDG) ORCID: 0000-0002-0355-0898
• Tantely Luciano Michaël, Institut Pasteur de Madagascar (MDG)
• Girod Romain, Institut Pasteur de Guyane française (GUF)
• Rakotoniaina Solofoarisoa, Université d'Antananarivo (MDG)
• Baril Laurence, Institut Pasteur de Madagascar (MDG)
• Piola Patrice, Institut Pasteur du Cambodge (KHM)
• Rakotomanana Fanjasoa, Institut Pasteur de Madagascar (MDG)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/595784/)

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