The increasing incidence of emerging infectious diseases emphasizes the urgent need for timely and accurate global surveillance and early warning systems. In recent years, infectious disease surveillance has become more diversified, and early warning technologies have seen significant advancements in sensitivity and timeliness. This review outlines the evolution and application of infectious disease surveillance and focuses on the potential of the One Health approach. By integrating data across human, animal, and environmental domains, the One Health approach provides a more comprehensive and effective framework for addressing future pandemics. Furthermore, this review systematically introduces key concepts in infectious disease early warning that include the selection of warning thresholds and the categorization of warning models. This review also summarizes representative global early warning systems for infectious diseases, discusses their prospects, and offers insights for developing intelligent, multi-source data-driven monitoring and early warning systems globally.
In recent years, the incidence and impact of emerging infectious diseases have increased significantly, with notable outbreaks such as the 2003 severe acute respiratory syndrome (SARS), the 2009 swine flu pandemic, the 2012 Middle East respiratory syndrome (MERS), and, most recently, the coronavirus disease 2019 (COVID-19) pandemic.1 These events highlight the urgent need for more robust and holistic infectious disease surveillance and early warning systems that allow the timely monitoring, detection, and assessment of emerging health threats.2, 3
Surveillance systems made significant advancements during the COVID-19 pandemic, particularly in the use of wastewater surveillance and digital methods such as mobility tracking and infodemiology.4 However, notable gaps still remain. One major issue is that existing systems continue to focus primarily on human health, with hospital-based surveillance forming the backbone. However, hospital-based systems often experience delays in detecting anomalies, emphasizing the need for further enhancements to address future pandemic risks.5 Another challenge is the disparity in monitoring capabilities across countries. Whereas some nations have well-established infrastructures, others face barriers, including limited resources, a lack of laboratory capabilities, political instability, and inadequate data-sharing systems, which further hamper global efforts in detecting and responding to emerging infectious diseases.6, 7 For example, since October 2024, the Democratic Republic of the Congo has been struck by an undiagnosed disease that includes symptoms of fever, headache, cough, runny nose, and body ache. However, the cause was not identified until 2 months later, in December, as an acute respiratory infection complicated by malaria, further indicating regional disparities in resources and diagnostic capabilities.
The performance of early warning systems is closely linked to the effectiveness of disease surveillance because these systems transform data into actionable insights. Early warning techniques have also evolved considerably with the expansion of data sources and advancements in computational power. Today, multi-channel surveillance data are integrated to assess factors that influence the spatiotemporal spread of diseases, expanding the range of indicators to include symptoms, risk factors (e.g., meteorological conditions, vector density, and pathogen data), population mobility, and even internet queries.8 Moreover, the scope of early warning has broadened to include spatial and spatiotemporal warnings, thus offering a more comprehensive view of disease dynamics.9 Furthermore, earlier warning techniques have relied primarily on time series models that issue real-time alerts. However, modern systems now employ a range of advanced algorithms, including machine learning and deep learning,10 to forecast trends and provide proactive alerts that enable earlier resource preparation and better allocation.
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