This work investigated the future (2021–2050) impact of Climate Change on Malaria Prevalence in the Upper River Region of The Gambia under two representative concentration pathways, RCP4.5 and RCP8.5, comparing it with the observed evaluation period of 2011–2022. The observed climatic variable data used was obtained from the Department of Water Resources and the corresponding malaria cases from the archive of the primary Health database, Banjul, The Gambia. Projected monthly temperature, precipitation, and relative humidity were downloaded from the coordinated Regional downscaling experiment (CORDEX) stimulation of the Rossby Centre Regional Atmospheric regional climate (RCA4). The dataset spans the decades from 2021 to 2050, providing insight into future climatic and epidemiological trends. Gradient Boost Machine Learning algorithm was utilized for the malaria projection both in the population below 5 and above five years. The result revealed an increase in malaria incidence under RCP4.5 and RCP8.5 climatic scenarios for both age categories with a clear indication in the population above five years. The result pictures how climate change will impact malaria under RCP4.5 and RCP8.5 emission scenarios in the region and also clearly reveals that the upper river region of the Gambia population is at risk of malaria infection, thus, a strategic and robust intervention scheme is highly solicited.

Despite increased malaria interventions, the Upper River Region of The Gambia still faces a higher malaria burden compared to other parts of the country. This issue may be related to the population’s knowledge of malaria, as well as environmental and socioeconomic factors that influence their prevention and treatment choices. Therefore, this study focused on assessing the level of malaria knowledge, perceptions, and attitudes among the residents of the Upper River Region, as limited studies have been conducted across the region’s seven districts. To achieve the study’s objectives, a descriptive research survey using a parallel mixed method was adopted, with a robust multistage sampling approach for selecting 381 household heads involved in the quantitative study, 31 key informants, and 7 focus group discussions for the qualitative study. Descriptive statistics were performed on the assessed variables using StataSE18, and the association of socio-demographic factors with malaria prevalence was analyzed using Chi-square (Χ2). Thematic analysis was conducted for the qualitative part using NVivo. The results indicate a very high level of knowledge of basic malaria concepts, with a percentage index of 81. There is a statistically significant influence from factors such as district, number of health centers in the region, gender, distance to the health centre, and occupation, all at < 5%. Using a treated bed net was the most effective prevention tool, while hospital visits and herbs remain the main treatment options among the population. The trend of malaria infection shows a general decrease across most communities, although a few still experience an increase over the year. Malaria profile data reveal an 87% reduction in cases for individuals above 5 years old and a 45% reduction for those under five. Additionally, mortality rates have decreased by 98% and 89%, respectively, over the 12 years (2011–2023). This study examined knowledge, perception, and attitudes toward malaria prevention and treatment. Results showed a high level of understanding of basic malaria concepts among the population, which influenced their perceptions and fostered a positive attitude toward managing and preventing the infection. However, differences in knowledge levels exist across districts, along with some contradictions in perceptions and attitudes. An overall decline in malaria cases has been observed across the districts and communities in the region, with a few exceptions. These findings are expected to inform the national malaria prevention efforts and assist policymakers in planning the next steps for the malaria elimination campaign.

Climate change is intensifying the threat of malaria in Africa by altering the geographical range, seasonality, and transmission intensity of the disease. Rising temperatures promote mosquito breeding and parasite development, whereas extreme weather events, particularly flooding, disrupt control efforts and create breeding sites, enabling their spread into previously unaffected highland areas. These factors pose an evolving public health challenge. Malaria-control strategies, often designed for stable climatic conditions, risk being outpaced by rapid environmental changes. However, little is known regarding how national policies integrate climate adaptation into strategic planning. We qualitatively analysed national malaria strategic plans and related policy documents from 11 west and central African countries, published between 2015 and 2025. We assessed incorporation of climate and weather information into malaria surveillance, early-warning systems, and vector-control planning. Although most countries recognise the influence of climate and seasonality on malaria transmission, explicit integration of climate data into planning and surveillance remains scarce. Ghana and Nigeria use advanced data-driven approaches, including predictive agent-based modelling and subnational stratification, to guide interventions, whereas Togo, Benin, Senegal, and The Gambia show emerging progress. However, most national malaria strategic plans remain reactive rather than predictive because of data, technical, and coordination gaps. This Review highlights policy and implementation gaps in integrating climate adaptation within malaria-control programmes. We recommend climate-resilient malaria strategies centred on enhanced surveillance, integrated meteorological forecasting, and strengthened institutional capacity to support adaptive, evidence-based interventions.

Despite the scale-up of intervention, malaria remains a burden in the Upper River Region of The Gambia. Climate changes and non-climatic conditions can substantially influence malaria prevalence, and further affect the coverage of preventive interventions. This work aimed at exploring the different climatic and non-climatic risk factors associated with malaria. A descriptive research method using a parallel mixed method approach was adopted. Using a multistage approach, 381 households were selected from the region, and primary data was collected from household heads. Descriptive statistics were performed using StataSE18, and the significant influence of environmental and socioeconomic factors was analyzed using Chi-square (Χ2). Thematic analysis for the qualitative part was carried out using Nvivo. Malaria is heavily influenced by rainfalls, floods, and some of the measured non-climatic factors. These results will provide individuals, professionals, government, and policymakers valuable information for better-targeting malaria control efforts.