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Communication Dans Un Congrès Année : 2023

Malaria Risk Predictive Modelling In The Northern Zone Of Plateau State, Nigeria

Résumé

Despite the existence of multiple malaria control and elimination initiatives for many years, Nigeria continues to bear the largest burden of the disease, accounting for approximately 26.6% of global cases and 31.3% of global deaths. As a contribution to the fight against malaria in Nigeria, the relationship between mosquito breeding habitats and malaria transmission dynamics to determine the risk of malaria transmission in the Northern Zone of Plateau State, Nigeria is examined. Geographic Information System-Multi-Criteria Decision Analysis, a complex and dynamic process with both managerial and engineering elements, is used to assess and combine the various risk factors by incorporating the insights and experience of malaria experts who are familiar with the malaria situation to determine the relative preference, influence, and significance of each factor related to mosquito breeding habitats and malaria transmission. To develop the model, the imputes of forty-five experts from different institutions with varying levels of expertise in malaria control, field experience, education, and project management is used through a brainstorming session using a Pair-Wise Comparison matrix. A malaria risk model for the Northern Zone of Plateau State, Nigeria is developed by calculating the relative weights of ecological, meteorological, and socioeconomic risk factors. The temporal dimension of the model is achieved through the analysis of confirmed malaria cases collected from various health facilities and their corresponding distribution rates, while the spatial dimension is represented by analysis of the various parameters and the three risk factors determined through expert consensus. The Rstudio package is used to validate the spatiotemporal predictive model by computing the Receiver Operating Characteristics and Area Under the Curve in addition to an analysis of the isoline distribution of confirmed malaria cases. The spatial analysis of risk factors and epidemiological data in developing the model is helpful for health authorities to comprehend the spatial spread of malaria risk, direct malaria control programs and assist in determining appropriate locations for intervention programs. The analysis of parameter weights recommends that efforts for malaria control and elimination in the study area should emphasize controlling the immature stages of mosquitoes' life cycle.

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Dates et versions

hal-04208551 , version 1 (15-09-2023)

Identifiants

  • HAL Id : hal-04208551 , version 1

Citer

Nanlok Henry Nimlang, Gilles Dusserre, Sandrine Bayle, Kivanc Ertugay, Sebnem Duzgun. Malaria Risk Predictive Modelling In The Northern Zone Of Plateau State, Nigeria. TIEMS 2023 - The International Emergency Management Society Hybrid Annual Conference, Sep 2023, Eastern Cape Province, South Africa. ⟨hal-04208551⟩
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