Emerging Topic Area

Early Warning Systems

Early warning systems are critical components of public health infrastructure that can help countries rapidly detect emerging health threats.

There is a global need and opportunity to implement and scale effective early warning systems (EWS) to be better equipped to rapidly detect future outbreaks of infectious disease with epidemic or pandemic potential. A crucial first step is to establish a strong base of knowledge to build upon.

The Advance Warning and Response Exemplars (AWARE) project seeks to understand key drivers that lead to successful EWS by studying the experiences and achievements of those who have already begun forging this path. The goal of AWARE is to examine barriers and facilitators to developing, implementing, and sustaining robust EWS. The research team will identify positive outliers in successful early detection and response to significant public health events, including outbreaks of climate-sensitive infectious diseases and those with pandemic potential.

To achieve this, the AWARE research team intends to:

Determine the foundational epidemic intelligence infrastructure, capabilities, and processes necessary and sufficient for effective EWS in low-resource environments.
Frame which best practices and core capacities enable successful multisectoral outbreak response governance and decision-making.

For more information, please reach out to us at support@exemplars.health

EWS enable public health officials to detect, assess, and mitigate public health threats, such as infectious disease outbreaks.¹ An effective EWS is built on foundational epidemic intelligence (EI), defined as “the systematic collection, analysis, and communication of any information to detect, verify, assess, and investigate events and health risks with an early warning objective.”² The core components of EI are (1) indicator-based surveillance (IBS), which uses structured routine data collected from health facilities, including official health records from hospitals or laboratories, and (2) event-based surveillance (EBS), which uses unstructured ad hoc information collected from diverse sources throughout the community, including reports from news or social media.² Many EWS have explored integrating innovative technologies, such as predictive modeling software, to analyze a range of datasets (e.g. health, climate, administrative areas) to forecast infectious disease outbreaks.³ Together, these systems form an essential surveillance apparatus for early and actionable detection and response to public health risks. They also provide policymakers with valuable information on the likelihood and severity of potential hazards and risks.

The global health community has developed data-sharing systems, analytical methods, technologies, and toolkits that can help identify, characterize, track, and inform responses. Despite these advancements, the COVID-19 pandemic has demonstrated that current surveillance systems are inadequate for rapidly detecting and responding to outbreaks with epidemic and pandemic potential. It also revealed that effective governance structures are crucial in supporting decision-makers to effectively use EWS to allocate resources and respond to emergencies. Despite the pandemic’s devastating impact, it presents an opportunity to leverage engaged stakeholders, political will, and scientific advancements to build better surveillance and response systems. Such systems will be crucial for predicting and preparing for infectious disease outbreaks due to a changing climate, as more than half of all known pathogenic diseases are aggravated by climate change.⁴ Integrating seasonal or sub seasonal climate forecasting data into routine EWS can help detect potential pathogen threats earlier and respond to outbreaks faster.⁵

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