Paper Title
SPATIOTEMPORAL MODELLING OF THE RELATIONSHIP AND SEASONAL VARIABILITY BETWEEN MALARIA PREVALENCE AND ENVIRONMENTAL VARIABLES IN THE NORTHERN ZONE OF PLATEAU STATE, NIGERIA

Abstract
Problem statement: Recent statistics show an increasing trend since the post-COVID era, it is important to model the relationship between malaria and environmental variables as they mainly influence malaria transmissions in the tropics. Methods: Temporal trends of malaria prevalence and corresponding hotspots, spatial autocorrelation and hotspot analysis were conducted in this study. The spatial relationship between various explanatory environmental variables and malaria prevalence was modelled using geographically weighted regression to understand the seasonal variability and spatial influence of these variables on malaria transmission. Results: The cumulative spatial distribution of malaria prevalence shows that Jos North LGA had the highest number of cases with 29.2% of cases (514,034). Whereas Barkin Ladi, Jos South and Jos East, Bassa and Riyom with 16.9% and 14.8%, 14.7%, 13.5% and 10.9% respectively.The hotspot analysis equally shows that the Jos North LGA had a very high-risk level (CLu = 95-99%) in 2018, 2019, 2020 and 2021, while the Jos South LGA also had a higher risk level (CLu = 95%). The year 2017 is observed to have the strongest evidence of global spatial clustering (Moran's I=0.823, z=8.518, P=0.00). An evaluation of environmental covariates revealed a positive association between rainfall (β = 0.062; se = 0.088) and soil moisture index (β = 0.426; se = 0.271) and malaria prevalence during the wet and dry season respectively. There was a negative relationship between malaria prevalence and other variables in both wet and dry seasons. The statistical significance of the association between the covariates and malaria showed an alternating trend and pattern for both dry and wet season. Conclusion: By assessing the spatial influence of seasonal variability on environmental variables and malaria prevalence, this study provides valuable insights for a better understanding of the relationship between malaria and explanatory environmental variables in the study area. This serves as an important guide for policy implementation and effective vector management. Keywords - Environmental Covariates, Hotspot, Malaria Prevalence, Relationship, Spatial Relationship.