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

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Climate change is having a profound impact on the spread of infectious diseases. Asecosystems change, emerging and re-emerging infectious diseases expand their reach,highlighting the need for accurate and rapid detection methods. Human populations arealso growing and shifting, increasing stressors on the environment and resources, such asfood and water.Rapid Diagnostic Tests (RDTs), such as lateral flow tests, have become indispensabletools in disease control. Their capacity for immediate results make them an essentialcomponent in outbreak management. Yet, a critical void in the current RDT landscape isthe absence of integrated data. Comprehensive collection, connection, and analysis ofthis data can offer invaluable insights into disease prevalence, spread, and potentialareas 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 withclimate change, stands as a focal point for this strategy. Using an extensive dataset ofCholera RDTs, machine learning algorithms will be developed to improve diagnostics.Simultaneously, this research will develop an application to identify test outcomesrapidly and accurately, while providing an integrated data platform. Field evaluation ofnew Cholera RDTs will then be assisted using these integrated platforms.Machine learning-driven insights are crucial for both enhancing current RDTs andevaluating 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 andvulnerabilities, underscores the need for developing new RDTs. To this end, this work willalso focus on developing a Target Product Profile (TPP) for CCHF, working in closecollaboration 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 ofspecimen 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 humanvulnerabilities, integrated RDTs equipped with machine learning insights have thepotential to transform disease detection. This research aims to make diagnosticprocesses not only rapid but also accurate and adaptable, responding to global healthchallenges efficiently. This work will also explore the epidemiological data collectedalongside test results in integrated data platforms, and seek to increase citizen sciencedata collection.