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

气候变化正在对传染病的传播产生深远影响。随着生态系统的变化，新出现和重新出现的传染病扩大了其影响范围，突出了对准确和快速检测方法的需求。人口也在不断增长和变化，对环境和资源（如食物和水）造成了越来越大的压力。快速诊断测试（RDT），如侧流测试，已成为疾病控制中不可或缺的工具。它们能够立即产生效果，这使它们成为疫情管理的一个重要组成部分。然而，当前RDT领域的一个关键空白是缺乏综合数据。对这些数据的全面收集、连接和分析可以为疾病的流行、传播和潜在的关注领域提供宝贵的见解。机器学习在这方面提供了变革的潜力。通过使用RDT图像库，机器学习可以提供准确的数据解释和测试结果分类。霍乱适合基于RDT的检测，并且随着气候变化越来越重要，因此成为这一战略的重点。利用霍乱RDTs的广泛数据集，将开发机器学习算法以改进诊断。同时，本研究将开发一个应用程序，以快速准确地识别测试结果，同时提供一个集成的数据平台。这些集成平台将有助于对新的霍乱RDT进行现场评估。机器学习驱动的洞察力对于增强当前的RDT和降低新诊断工具的价值至关重要。然而，一些传染病，如克里米亚-刚果出血热（CCHF），完全缺乏RDT。认识到这些差距，再加上对气候驱动的风险和脆弱性变化的理解，强调了开发新的RDT的必要性。为此，这项工作还将侧重于与包括卫生部、疾病预防控制中心和世卫组织在内的关键利益攸关方密切合作，为CCHF制定目标产品简介（TPP）。该TPP将建立在现有的“再保证”标准基础上。（实时连接，易于标本采集，价格实惠，灵敏，特异，用户友好，快速和强大，无需设备或简单，可供最终用户使用），确保数据集成、环境可持续性和疾病的早期识别。总之，随着气候变化改变疾病流行病学并暴露人类脆弱性，配备机器学习洞察力的集成RDT有可能改变疾病检测。这项研究旨在使诊断过程不仅快速，而且准确和适应性强，有效应对全球健康挑战。这项工作还将探索在综合数据平台中收集的流行病学数据以及检测结果，并寻求增加公民科学数据收集。

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.