Landslides Hazard Mapping in Rwanda Using Bivariate Statistical Index Method

Landslides hazard mapping (LHM) is essential in delineating hazard prone areas and optimizing low cost mitigation measures. This study applied the Geographic Information System and statistical index method in LHM in Rwanda. Field surveys identified 336 points that were employed to construct a landslides inventory map. Ten landslides predicting factors were analyzed: normalized difference vegetation index, elevation, slope, aspects, lithology, soil texture, distance to rivers, distance to roads, rainfall, and land use. The factor variables were converted into categorized variables according to the percentile divisions of seed cells. Then, values of each factor’s class weight were calculated and summed to create landslides hazard map. The estimated hazard map was split into five hazard classes (very low, low, moderate, high, and very high). The results indicated that the northern, western, and southern provinces are largely exposed to landslides hazard. The major landslides hazard influencing factors are elevation, slope, rainfall, and poor land management. Overall, this LHM would help policy makers to recognize each area’s hazard extent, key triggering factors, and the required hazard mitigation measures. These measures include planting trees to enhance vegetation cover and reduce the runoff, and construction of buildings on low steep slope areas to reduce people’s hazard exposure; while agroforestry and bench terraces would reduce sediments that take out the exposed soil (erosion) and pollute water quality

Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa

Application of suitable methods to generate landslide susceptibility maps (LSM) can play a key role in risk management. Rwanda, located in centre-eastern Africa experiences frequent and intense landslides which cause substantial impacts. The main aim of the current study was to effectively generate susceptibility maps through exploring and comparing different statistical and probabilistic models. These included weights of evidence (WoE), logistic regression (LR), frequency ratio (FR) and statistical index (SI). Experiments were conducted in Rwanda as a study area. Past landslide locations have been identified through extensive field surveys and historical records. Totally, 692 landslide points were collected and prepared to produce the inventory map. This was applied to calibrate and validate the models. Fourteen maps of conditioning factors were produced for landslide susceptibility modeling, namely: elevation, slope degree, topographic wetness index (TWI), curvature, aspect, distance from rivers and streams, distance to main roads, lithology, soil texture, soil depth, topographic factor (LS), land use/land cover (LULC), precipitation and normalized difference vegetation index (NDVI). Thus, the produced susceptibility maps were validated using the receiver operating characteristic curves (ROC/AUC). The findings from this study disclosed that prediction rates were 92.7%, 86.9%, 81.2% and 79.5% respectively for WoE, FR, LR and SI models. The WoE achieved the highest AUC value (92.7%) while the SI produced a lowest AUC value (79.5%). Additionally, 20.42% of Rwanda (5048.07 km2) was modeled as highly susceptible to landslides with the western part the highly susceptible comparing to other parts of the country. Conclusively, the comparison of produced maps revealed that all applied models are promising approaches for landslide susceptibility studying in Rwanda. The results of the present study may be useful for landslide risk mitigation in the study area and in other areas with similar terrain and geomorphological conditions. More studies should be performed to include other important conditioning factors that exacerbate increases in susceptibility especially anthropogenic factors. © 201

InnovAfrica project endline survey data for Ethiopia, Kenya, Malawi, Rwanda, South Africa and Tanzania

A consortium of 16 institutions comprising five institutions from Europe and eleven institutions from Africa implemented a project entitled "Innovations in Technology, Institutional and Extension Approaches towards Sustainable Agriculture and enhanced Food and Nutritional Security in Africa (InnovAfrica)" in six countries of eastern and southern Africa namely Ethiopia, Kenya, Malawi, Rwanda, South Africa and Tanzania from June 2017 to November 2021. The InnovAfrica project collected endline data from 12 pilot sites (two sites per country) in the third years of the project. The data collected during the Endline survey is presented in this document.There is no restriction to use these data set.Funding provided by: H2020*Crossref Funder Registry ID: Award Number: 727201The endline data were collected from 12 pilot study sites comprising two sites each from Ethiopia, Kenya, Malawi, Rwanda, South Africa and Tanzania using structured questionnaire and focus group discussion

Supplementary Material from Can mass drug administration of moxidectin accelerate onchocerciasis elimination in Africa?

Epidemiological and modelling studies suggest that elimination of Onchocerca volvulus transmission (EoT) throughout Africa may not be achievable with annual mass drug administration (MDA) of ivermectin alone, particularly in areas of high endemicity and vector density. Single-dose Phase II and III clinical trials demonstrated moxidectin's superiority over ivermectin for prolonged clearance of O. volvulus microfilariae. We used the stochastic, individual-based EPIONCHO-IBM model to compare the probabilities of reaching EoT between ivermectin and moxidectin MDA for a range of endemicity levels (30% to 70% baseline microfilarial prevalence), treatment frequencies (annual and biannual) and therapeutic coverage/adherence values (65% and 80% of total population, with, respectively, 5% and 1% of systematic non-adherence). EPIONCHO-IBM's projections indicate that biannual (six-monthly) moxidectin MDA can reduce by half the number of years necessary to achieve EoT in mesoendemic areas and might be the only strategy that can achieve EoT in hyperendemic areas. Data needed to improve modelling projections include (i) the effect of repeated annual and biannual moxidectin treatment; (ii) inter- and intra-individual variation in response to successive treatments with moxidectin or ivermectin; (iii) the effect of moxidectin and ivermectin treatment on L3 development into adult worms and (iv) patterns of adherence to moxidectin and ivermectin MDA.This article is part of the theme issue ‘Challenges in the Fight Against Neglected Tropical Diseases’