As found in the health studies literature, the levels of climate association between epidemiological diseases have been found to vary across regions. Therefore, it seems reasonable to allow for the possibility that relationships might vary spatially within regions. We implemented the geographically weighted random forest (GWRF) machine learning method to analyze ecological disease patterns caused by spatially non-stationary processes using a malaria incidence dataset for Rwanda. We first compared the geographically weighted regression (WGR), the global random forest (GRF), and the geographically weighted random forest (GWRF) to examine the spatial non-stationarity in the non-linear relationships between malaria incidence and their risk factors. We used the Gaussian areal kriging model to disaggregate the malaria incidence at the local administrative cell level to understand the relationships at a fine scale since the model goodness of fit was not satisfactory to explain malaria incidence due to the limited number of sample values. Our results show that in terms of the coefficients of determination and prediction accuracy, the geographical random forest model performs better than the GWR and the global random forest model. The coefficients of determination of the geographically weighted regression (R2), the global RF (R2), and the GWRF (R2) were 4.74, 0.76, and 0.79, respectively. The GWRF algorithm achieves the best result and reveals that risk factors (rainfall, land surface temperature, elevation, and air temperature) have a strong non-linear relationship with the spatial distribution of malaria incidence rates, which could have implications for supporting local initiatives for malaria elimination in Rwanda.
Rural isolation can limit access to basic services and income-generating opportunities. Among some communities, rainfall induced flooding can cause increased uncertainty where first-mile transportation infrastructure is limited. In Rwanda, this challenge is apparent, where 90% of the population below the poverty line live in rural areas that are typically mountainous with frequent flooding – events that may be increasing in frequency and severity as the climate changes. To reduce these transportation barriers, the non-profit organization Bridges to Prosperity (B2P) plans to construct hundreds of trailbridges in Rwanda between 2018 and 2023. This scale of rural infrastructure services presents an opportunity for experimental investigation of the effects of these new trailbridges on economic, health, agricultural and education outcomes in rural communities. In this paper, we present a cohort study evaluating the potential community benefits of rural trailbridges – including economic, health and social outcomes for Rwandan communities experiencing environmental change. We examined households living near 12 trailbridge sites and 12 comparison sites over February 2019-March 2020. We found that labor market income increased by 25% attributable to the trailbridges. We did not observe any significant effects on agricultural income, education or health outcomes, however given the small sample and short duration of this study we anticipate observing additional outcomes within the recently started 200 site, 4 year trial.
BACKGROUND: Malaria has continued to be a life-threatening disease among under-five children in sub-Saharan Africa. Recent data indicate rising cases in Rwanda after some years of decline. We aimed at estimating the spatial variations in malaria prevalence at a continuous spatial scale and to quantify locations where the prevalence exceeds the thresholds of 5% and 10% across the country. We also consider the effects of some socioeconomic and climate variables. METHODS: Using data from the 2014-2015 Rwanda Demographic and Health Survey, a geostatistical modeling technique based on stochastic partial differential equation approach was used to analyze the geospatial prevalence of malaria among under-five children in Rwanda. Bayesian inference was based on integrated nested Laplace approximation. RESULTS: The results demonstrate the uneven spatial variation of malaria prevalence with some districts including Kayonza and Kirehe from Eastern province; Huye and Nyanza from Southern province; and Nyamasheke and Rusizi from Western province having higher chances of recording prevalence exceeding 5%. Malaria prevalence was found to increase with rising temperature but decreases with increasing volume for rainfall. The findings also revealed a significant association between malaria and demographic factors including place of residence, mother’s educational level, and child’s age and sex. CONCLUSIONS: Potential intervention programs that focus on individuals living in rural areas, lowest wealth quintile, and the locations with high risks should be reinforced. Variations in climatic factors particularly temperature and rainfall should be taken into account when formulating malaria intervention programs in Rwanda.
