Spatiotemporal Dynamics and Environmental Predictors of Confirmed Uncomplicated Malaria in Bayelsa State, Nigeria (2017-2024)

Background Bayelsa State, Nigeria with a current prevalence rate of 17% based on the 2021 Nigeria Malaria Indicator Survey. Previous studies on malaria incidence in Bayelsa State, Nigeria, have the absence of longitudinal studies, low survey coverage, limited integration of environmental factors into analyses of local government area (LGA)-level malaria patterns, and few or no comparisons between upland and riverine settings. This study quantifies temporal trends and spatial heterogeneity in confirmed uncomplicated malaria across eight (8) LGAs, compares malaria burdens between upland and riverine LGAs, and identifies and ranks environmental and infrastructural predictors of LGA-level malaria via complementary statistical approaches. Methods Data on confirmed uncomplicated malaria cases from 2017–2024 for all LGAs in Bayelsa were downloaded from the DHIS2 database. Administrative data and the number of healthcare facilities were downloaded from Grid 3.org, whereas environmental data were downloaded from the Google Earth Engine website. Descriptive statistics, univariate Moran’s I, ANOVA, correlation, and exploratory and ordinary least squares regression were the analytical methods used. Results The incidence of confirmed uncomplicated malaria in Bayelsa State rose from 10,745 cases in 2017 to 65,149 cases in 2024, a 6.06-fold increase corresponding to a compound annual growth rate of 29.4%. Yenagoa consistently accounted for the largest share, ranging from 28.3% in 2018 to 49.2% in 2019 and 39.5% in 2024. The incidence was generally greater in upland LGAs than in riverine LGAs, with significantly greater dispersion in upland settings (F(1,62) = 4.904, p = 0.030). The global Moran’s I coefficients were weakly negative across years, suggesting spatial dispersion rather than clustering. Regression analysis revealed Visible Infrared Imaging Radiometer Suite (VIIRS) (t = 25.86), elevation (t = 18.89), and NDVI (|t|=12.88) as the strongest predictors, supported by built-up land (r = 0.954, p < 0.001), roads (r = 0.912, p = 0.001), cropland (r = 0.711, p = 0.024), and healthcare facilities (r = 0.808, p = 0.008). Conclusions The findings show that settlement expansion and environmental conditions strongly shape malaria dynamics, often outweighing broad climate and vegetation measures in thermally optimal, wet areas. Priorities for reducing the malaria burden include peri-urban environmental management, improved housing, and strengthened unbiased surveillance.