article · Tanzania journal of health research/Tanzania Journal of Health Research
Rabies remains a significant public health threat in low- and middle-income countries due to gaps in timely diagnosis, disease surveillance, and outbreak forecasting. While artificial intelligence and machine learning can help resolve these challenges, a lack of structured, high-quality data has limited their application. To address this, a structured dataset ready for machine learning was developed using records from Tanzania. Sourced from Integrated Bite Case Management and contact tracing systems, the collection comprises over 15,000 bite patient records and 3,000 confirmed rabies cases. After preprocessing, annotation, and feature engineering, baseline models were tested. Gradient Boosting achieved the strongest diagnostic performance with an Area Under the Curve of 0.82, followed closely by Logistic Regression at 0.81. The data also exposed distinct spatiotemporal trends, offering a resource for predictive planning and AI-driven disease surveillance.
Rabies is preventable through vaccination, yet tracking and diagnosing outbreaks in low- and middle-income regions remains difficult. Preparing and sharing high-quality, structured health records allows computational tools to predict outbreaks and diagnose cases more accurately. This evidence supports health authorities in deploying preventive resources effectively, assisting global initiatives to eliminate dog-mediated rabies deaths by 2030.
This research provides applied and tested baseline models alongside a structured dataset that could support public health software and diagnostic decision-support tools. Potential users include healthcare planners, epidemiological surveillance teams, and policy makers managing vaccine and intervention resources. While the baseline algorithms show diagnostic capability, the work currently serves as early-stage and foundational data infrastructure rather than a deployed commercial product.
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Introduction: Rabies remains a major public health concern in low- and middle-income countries (LMICs), despite being vaccine-preventable. Limitations in timely diagnosis, surveillance, and outbreak prediction hinder progress toward global elimination targets. Artificial Intelligence (AI) and Machine Learning (ML) offer promising solutions, but their uptake in LMICs has been constrained by the lack of structured, high-quality datasets. This study aimed to establish machine learning-ready datasets to support rabies diagnosis, outbreak prediction, and improved allocation of preventive resources in Tanzania. Methods: Data were sourced from Integrated Bite Case Management (IBCM) and contact tracing systems, including over 15,000 bite patient records and 3,000 confirmed rabies cases. Data preprocessing, annotation, and feature engineering were conducted to create structured datasets suitable for ML applications. Several baseline models were developed and evaluated using ROC curve analysis. Results: Gradient Boosting achieved the highest diagnostic performance with an Area Under the Curve (AUC) of 0.82, followed by Logistic Regression at 0.81. The processed datasets also revealed clear spatiotemporal trends in rabies outbreaks, highlighting their potential for predictive analytics and strategic planning. Conclusion: This work provides a foundational dataset for AI-driven rabies surveillance and control in Tanzania. Public access to the dataset is expected to enhance research, support data-driven health policy, and contribute to achieving the WHO's goal of eliminating dog-mediated human rabies deaths by 2030.
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DOI: 10.4314/thrb.v26i6.21
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