Rapid Diagnostic Tests, Machine Learning, and Disease Detection in a Climate-Impacted World

Climate change is having a profound impact on the spread of infectious diseases. As ecosystems change, emerging and re-emerging infectious diseases expand their reach, highlighting the need for accurate and rapid detection methods. Human populations are also growing and shifting, increasing stressors on the environment and resources, such as food and water. Rapid Diagnostic Tests (RDTs), such as lateral flow tests, have become indispensable tools in disease control. Their capacity for immediate results make them an essential component in outbreak management. Yet, a critical void in the current RDT landscape is the absence of integrated data. Comprehensive collection, connection, and analysis of this data can offer invaluable insights into disease prevalence, spread, and potential areas of concern. Machine learning offers transformative potential here. By using a library of RDT images, machine learning can provide accurate data interpretation and test result classification. Cholera, with its suitability for RDT-based detection and increasing importance with climate change, stands as a focal point for this strategy. Using an extensive dataset of Cholera RDTs, machine learning algorithms will be developed to improve diagnostics. Simultaneously, this research will develop an application to identify test outcomes rapidly and accurately, while providing an integrated data platform. Field evaluation of new Cholera RDTs will then be assisted using these integrated platforms. Machine learning-driven insights are crucial for both enhancing current RDTs and evaluating new diagnostic tools. However, some infectious diseases, such as Crimean- Congo Hemorrhagic Fever (CCHF), lack RDTs altogether. Recognizing these gaps, combined with an understanding of climate driven changing exposures and vulnerabilities, underscores the need for developing new RDTs. To this end, this work will also focus on developing a Target Product Profile (TPP) for CCHF, working in close collaboration with key stakeholders including Ministries of Health, the CDC and WHO. This TPP will build on the existing "RE-ASSURED" criteria (Real-time connectivity, Ease of specimen collection, Affordable, Sensitive, Specific, User-friendly, Rapid and robust, Equipment-free or simple, and Deliverable to end-users), to ensure data integration, environmental sustainability and early identification of disease. In conclusion, as climate change alters disease epidemiology and exposes human vulnerabilities, integrated RDTs equipped with machine learning insights have the potential to transform disease detection. This research aims to make diagnostic processes not only rapid but also accurate and adaptable, responding to global health challenges efficiently. This work will also explore the epidemiological data collected alongside test results in integrated data platforms, and seek to increase citizen science data collection.