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

International audience Despite the existence of multiple malaria control and elimination initiatives for many years, Nigeriacontinues to bear the largest burden of the disease, accounting for approximately 26.6% of global casesand 31.3% of global deaths. As a contribution to the fight against malaria in Nigeria, the relationshipbetween mosquito breeding habitats and malaria transmission dynamics to determine the risk of malariatransmission in the Northern Zone of Plateau State, Nigeria is examined. Geographic InformationSystem-Multi-Criteria Decision Analysis, a complex and dynamic process with both managerial andengineering elements, is used to assess and combine the various risk factors by incorporating theinsights and experience of malaria experts who are familiar with the malaria situation to determine therelative preference, influence, and significance of each factor related to mosquito breeding habitats andmalaria transmission. To develop the model, the imputes of forty-five experts from different institutionswith varying levels of expertise in malaria control, field experience, education, and project managementis used through a brainstorming session using a Pair-Wise Comparison matrix. A malaria risk model forthe Northern Zone of Plateau State, Nigeria is developed by calculating the relative weights ofecological, meteorological, and socioeconomic risk factors. The temporal dimension of the model isachieved through the analysis of confirmed malaria cases collected from various health facilities andtheir corresponding distribution rates, while the spatial dimension is represented by analysis of thevarious parameters and the three risk factors determined through expert consensus. The Rstudio packageis used to validate the spatiotemporal predictive model by computing the Receiver OperatingCharacteristics and Area Under the Curve in addition to an analysis of the isoline distribution ofconfirmed malaria cases. The spatial analysis of risk factors and epidemiological data in developing themodel is helpful for health authorities to comprehend the spatial spread of malaria risk, direct malariacontrol programs and assist in determining appropriate locations for intervention programs. Theanalysis of parameter weights recommends that efforts for malaria control and elimination in the studyarea should emphasize controlling the immature stages of mosquitoes' life cycle.