利用人工智能和移动数据进行实时传染病监测的创新方法

Janet Aderonke Olaboye, Chukwudi Cosmos Maha, Tolulope Olagoke Kolawole, Samira Abdul
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摘要

人工智能(AI)与移动健康数据的整合开创了传染病实时监控的新时代,为疾病动态提供了前所未有的洞察力,并促成了积极主动的公共卫生干预措施。本文探讨了人工智能和移动数据在改变传统传染病监测系统方面的创新应用。通过利用人工智能算法的力量,再加上移动设备产生的大量数据,研究人员和公共卫生机构现在可以更准确、更高效地实时监测疾病爆发。人工智能驱动的预测模型分析各种数据集,包括人口信息、旅行模式和社交媒体活动,以检测疾病出现的早期迹象并预测潜在的疫情爆发。移动健康数据的使用提供了大量以前传统监测方法无法获取的信息。移动应用程序、可穿戴设备和其他联网设备可对个人的健康指标进行持续监测,从而及早发现症状并对潜在威胁做出快速反应。此外,来自移动设备的地理定位数据也有助于追踪人口流动和识别疾病传播的高风险地区。然而,这种创新的传染病监测方法也带来了挑战和伦理方面的考虑。必须认真解决收集和使用移动健康数据方面的隐私问题,以确保个人权利得到保护。此外,还需要减少与数据质量、互操作性和算法偏差有关的问题,以确保人工智能驱动的监测系统的可靠性和有效性。总之,人工智能与移动健康数据的整合为彻底改变实时传染病监测带来了巨大希望。通过利用这些技术,公共卫生部门可以获得有关疾病动态的宝贵见解,提高早期检测能力,并实施有针对性的干预措施,以防止传染病的传播。然而,必须解决与这种方法相关的挑战和伦理考虑,以确保其负责任和有效的实施。关键词 创新 实时传染病 监控 人工智能 移动数据
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Innovations in real-time infectious disease surveillance using AI and mobile data
The integration of artificial intelligence (AI) and mobile health data has ushered in a new era of real-time infectious disease surveillance, offering unprecedented insights into disease dynamics and enabling proactive public health interventions. This paper explores the innovative applications of AI and mobile data in transforming traditional surveillance systems for infectious diseases. By harnessing the power of AI algorithms, coupled with the vast amount of data generated from mobile devices, researchers and public health authorities can now monitor disease outbreaks in real-time with greater accuracy and efficiency. AI-driven predictive models analyze diverse datasets, including demographic information, travel patterns, and social media activity, to detect early signs of disease emergence and predict potential outbreaks. The use of mobile health data provides a wealth of information that was previously inaccessible to traditional surveillance methods. Mobile apps, wearables, and other connected devices enable continuous monitoring of individuals' health indicators, allowing for early detection of symptoms and rapid response to potential threats. Furthermore, geolocation data from mobile devices facilitates the tracking of population movements and the identification of high-risk areas for disease transmission. However, this innovative approach to infectious disease surveillance also presents challenges and ethical considerations. Privacy concerns regarding the collection and use of mobile health data must be carefully addressed to ensure individuals' rights are protected. Additionally, issues related to data quality, interoperability, and algorithm bias need to be mitigated to ensure the reliability and effectiveness of AI-driven surveillance systems. In conclusion, the integration of AI and mobile health data holds immense promise for revolutionizing real-time infectious disease surveillance. By leveraging these technologies, public health authorities can gain valuable insights into disease dynamics, enhance early detection capabilities, and implement targeted interventions to prevent the spread of infectious diseases. However, it is essential to address the challenges and ethical considerations associated with this approach to ensure its responsible and effective implementation. Keywords:  Innovations, Real-Time Infectious Disease, Surveillance, AI, Mobile Data.
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