Rapid urbanization and climate change are increasing the risks associated with natural hazards, especially in cities where socio-economic disparities are significant. Current hazard risk assessment frameworks fail to consider socio-economic factors, which limits their ability to effectively address vulnerabilities at the community level. This study introduces a machine learning framework designed to assess flood susceptibility and socio-economic vulnerability, particularly in urban areas with limited data. Using Kigali, Rwanda, as a case study, we quantified socio-economic vulnerability through a composite index that includes indicators of sensitivity and adaptive capacity. We utilized a variety of data sources, such as demographic, environmental, and remotely sensing datasets, applying machine learning algorithms like Multilayer Perceptron (MLP), Random Forest, Support Vector Machine (SVM), and XGBoost. Among these, MLP achieved the best predictive performance, with an AUC score of 0.902 and an F1-score of 0.86. The findings indicate spatial differences in socio-economic vulnerability, with central and southern Kigali showing greater vulnerability due to a mix of socio-economic challenges and high flood risk. The vulnerability maps created were validated against historical flood records, socio-economic research, and expert insights, confirming their accuracy and relevance for urban risk assessment. Additionally, we tested the framework’s scalability and adaptability in Kampala, Uganda, and Dar es Salaam, Tanzania, showing that making context-specific adjustments to the model improves its transferability. This study offers a solid, data-driven approach for combining assessments of flood susceptibility and socio-economic vulnerability, filling important gaps in urban resilience planning. The results support the advancement of risk-informed decision-making, especially in areas with limited access to detailed socio-economic information.

Urbanization significantly enhances socio-economic conditions, health, and well-being for many by improving access to services, education, and economic opportunities. However, socio-economic and public health disparities are also being exacerbated by urbanization. The reliable data required to monitor these conditions are often unavailable, outdated, or inconsistent. This study introduces a multimodal deep learning framework that integrates satellite imagery with street network datasets to predict urban socio-economic indicators and public health determinants at the sector level as a political administrative unit of public health planning in Rwanda. We extracted latent visual and topological embeddings of the urban built environment, using a Convolutional Neural Network (CNN) and Graph Neural Network (GNN). These embeddings were fused through an attentional mechanism to train a multi-task regression model that simultaneously predicts multiple socio-economic indicators and public health determinants. This framework was applied to the City of Kigali in Rwanda. Overall, the multimodal fusion model achieved the best average performance across targets, with an average correlation of 0.68 and MAE of 1.26 for socio-economic indicators, and 0.68 and 1.46 for public health determinants, demonstrating the benefit of integrating visual and topological information. The learned fused embedding space arranges socio-economic indicators and public health determinant deciles along a continuous morphological gradient from sparsely built rural settings to dense urban settings, demonstrating that the urban form encodes latent signals that capture socio-economic indicators and health determinants. Moreover, the study reveals a strong relationship between socio-economic indicators and the public health index, with education, cooking materials, and floor materials exhibiting a correlation above 0.96. This work demonstrates the utility of an integrated framework for socio-economic indicator profiling and public health planning in data-scarce urban contexts, offering a scalable approach for monitoring the indicators of Sustainable Development Goals in rapidly changing urban environments.

Globally, hepatitis C is included in the public health concern. More than 1.4 million deaths are reported. In 170 million who are infected with HCV worldwide,71 million have serious complications. There is a close relationship between hepatitis C infections among family members with one case. Family member’s behaviors and exposure to positive family members without precautions are among the risk factor of getting hepatitis C infection. In Rwanda, the burden of Hepatitis C infections reaches to 4% of infected people. High prevalence of hepatitis C infection can be associated with horizontal transmission among household of positive patients for HCV. In Rwanda no data showing the contribution of horizontal transmission of HCV among household members as one of the risk factors to increase the prevalence of HCV. The objective of this study was to determine the prevalence of HCV among the household members living with HCV positive individual, specifically attending centre universitaire de santé public of BUTARE. The study used a descriptive cross-sectional design to assess the prevalence among 30 families having had positive cases for HCV with 180 sample size. With use of questionnires, HCV risk factors were assessed from our participants and from their responses. Purposive sampling was done for HCV rapid test and positive cases sample were confirmed at university teaching hospital of BUTARE with Elisa. From participants tested, 10.8% were tested positive for HCV counting for the prevalence of our study. Our findings are in line with other same studies conducted in Egypt and Iran revealed that there was 14.1%, which is a bit closer to our finding. we have found that unprotected sex with parnters and marital status between infected cases has a big influence to the high prevelence. Based on the findings from our study, 10.8% of our participants tested positive for HCV due to limited education, awareness, and exposure to contributing risk factors about HCV transmission.

Urbanization and climate change are increasing the risks of natural hazards, particularly in cities with significant socio-economic disparities. Existing hazard risk assessment frameworks often neglect socio-economic dimensions, limiting their utility in addressing community-level vulnerabilities. This study proposes an integrated machine learning and indicator-based framework for assessing flood susceptibility and socio-economic vulnerability, with a focus on data-scarce settings, using a case study of the City of Kigali. Socio-economic vulnerability was quantified through a composite index incorporating sensitivity and adaptive capacity. Multisource data were integrated and modeled using machine learning models, which included Multilayer Perceptron, Random Forest, Support Vector Machine, and XGBoost. In terms of model performance, the MLP has achieved high performance with an AUC score of 0.902 and F1-Score of 0.86. The results indicate intensified vulnerability in central and southern Kigali, with noticeable socio-economic disadvantages and high flood susceptibility. The resulting maps were validated using historical flood data, other socio-economic studies in the area, and local knowledge. The scalability of the framework was evaluated in Kampala, Uganda, and Dar es Salaam, Tanzania, demonstrating scalability with context-specific adaptations. This approach offers a robust methodology for integrating flood susceptibility and socio-economic vulnerability, enabling data-driven prioritization of interventions. The findings contribute to advancing urban resilience strategies, particularly in regions constrained by limited data availability.