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.