About 70% of the world’s malaria burden is focused in just 11 countries; 10 in Sub-Saharan Africa (SSA) (Burkina Faso, Cameroon, the Democratic Republic of Congo, Ghana, Mali, Mozambique, Niger, Nigeria, Uganda and the United Republic of Tanzania) and India. These high-burden nations housed an estimated 151 million cases of malaria and 275 000 deaths. In 2017, all the high burden endemic countries in Africa saw an increase in malaria cases with Cameroon witnessing an increase of 131,000 malaria cases(1), from a total of 1,618,738 confirmed cases reported in health facilities and communities in 2016(2). The epidemiological transmission of malaria in Cameroon is high (> 1 case per 1000 population) in about 71% (16.6 million people) and low (0–1 cases per 1000 population) in about 29% (6.8 million) in people of all sexes and age groups with children less than five at greater risk of the disease(2). In 2014, the morbidity of malaria in Cameroon was 30% in children and 18% in Adults(3). The government of Cameroon and partners have been combating malaria through the creation of national intervention programs including the distribution of free insecticide-treated nets (ITN) that was established in 2011 to populations at high risk, provision of sulfadoxine-pyrimethamine drugs to pregnant woman, parasitological screening of suspected malaria cases, and the application of other WHO standard treatments(3,4). The socio-economic and environmental challenges posed by the malaria disease to Africa countries is a global concern. The WHO’s Global Technical Strategy (GTS) for Malaria 2016- 2030 has been developed with the aim of helping countries reduce the human suffering caused by the disease. Adopted by the World Health Assembly in May 2015, the strategy provides comprehensive technical guidance to countries and development partners for 15 years, emphasizing the importance of scaling up malaria responses and moving towards elimination. It also highlights the urgent need to increase investments across all interventions – including preventive measures, diagnostic testing, treatment and disease surveillance – as well as in harnessing innovation and expanding research(5). Intensifying investments in malaria research by endemic SSA is a key to attaining the GTS targets and eradicating the disease from the SSA geolocations.
The application of spatial statistical methods to geolocational health data research has enabled complex scenarios of the malaria disease to be visualized through the creation of spatial maps within the Geographical information systems (GIS) technology(6–11). The study of the spatial variation between disease outcomes and associative socioeconomic or environmental factors using the GIS has greatly improved our understanding of these factors with the health outcome in question. Malaria has been reported to be associated with environmental and climatic factors such as rainfall, humidity, temperature (12,13) and understanding the behavior of these factors in space with the application of spatial regression statistics(14) will further improve on timely control measures and resource allocations.
Regression analyses are statistical techniques that allow for the modelling, examining, and exploring of spatial relationships, to better understand the factors behind observed spatial patterns and hotspots, and to predict outcomes based on that understanding(14). Ordinary Least Squares regression (OLS) is a global regression method that provides a global model of the variable or process to be predicted or studied. It creates a single regression equation to represent that process. Geographically Weighted Regression (GWR) is a local spatial regression method that allows the relationships to be modelled to vary across the study area by fitting a regression equation to every feature in the dataset using candidate explanatory variables from the OLS. It is a local form of linear regression used to model spatially varying relationships. GWR statistical modelling technique has been applied to a range of malaria studies: Hasyim (15), used the GWR to find the spatial association between malaria cases and environmental factors in South Sumatra, Indonesia where altitude, distance from forest and rainfall were associated with malaria, Moise(16), in the seasonal and geographic variation of pediatric malaria in Burundi, identified the spatial variation between monthly rainfall and malaria prevalence. The GWR spatial modelling technique has been a powerful tool in the understanding of malaria prevention and the spatial variability of malaria cases and environmental factors (15–18). It application has been valuable in the understanding of other infectious diseases such as the spatial association between dengue fever, and socioeconomic and environmental determinants(19,20). Also, GWR has been applied in other health outcomes and social science studies including cancer events(21), ,mental depression(22),fire events(23), hospital accessibility study (24), alcohol and violence(25) and real estate housing crisis(26).
Massoda (27), compared malaria survey programs in different ecological zones in Cameroon and recommended on the needs of intervention programs during high transmission rainy seasons. Furthermore, Tewara (28), in a recent study on small area spatial statistical analysis of malaria clusters and hotspots in Cameroon, illustrated the linear association between malaria cases and environmental factors using the Pearson correlation statistics(29) but didn’t demonstrate any spatial variability that would become the main aim of this study. The specific objective of this study is to find the spatial variability between malaria hotspot cases and environmental predictors using the GWR spatial modelling technique.