Response Introduction: Public health emergencies create enormous amounts of information from surveillance systems, laboratories, healthcare facilities, environmental monitoring, media reports, and other sources. We are no longer struggling to gather data; we have problems transforming that data into actionable intelligence to make evidence-based decisions in real time. Artificial intelligence (AI) is changing the way we conduct public health intelligence by quickly analysing vast and varied datasets, identifying patterns that are meaningful in an epidemiological sense, prioritising risks, and speeding and streamlining our responses.
AI doesn’t replace epidemiologists or public health professionals.
Instead, AI complements their work, enhancing the emergency management cycle, from initial detection and preparatory stages through to responses and evaluation. From Data to Early Detection In order to effectively monitor populations’ health, it’s necessary to first detect unusual health events before these grow into broader health emergencies. Due to the exponential rise of surveillance data – too much to analyse manually in time to be useful – new methods are required. AI augments surveillance by analysing multiple streams of information together, detecting irregular disease patterns, uncovering incipient trends, flagging signals that merit closer examination, and reducing the time between data collection and signal detection.
This enables public health officials to act sooner by confirming the existence of a potential event.
From Detection to Actionable Public Health Intelligence Early detection alone is only the beginning. When signals are detected, we need a clear understanding of whether this represents a real threat and what we should do about it – both immediately and going forward. AI integrates surveillance information, lab results, environmental data, and broader contextual information to give health professionals a fuller picture of emerging events.
This supports both rapid risk assessment and quick prioritisation of issues requiring timely investigation. This transformation of raw data into readily usable information is what we term AI-powered public health intelligence. Supporting an All-Hazards Approach Today’s health response challenges extend beyond infectious disease monitoring to include all other sources of threats, including novel zoonoses, foodborne illness, chemical exposure, environmental risks, and even the health impacts of climate change.
This all-hazards approach produces immense amounts of disparate information which even experienced analysts have difficulties interpreting using traditional tools alone.
AI enables public health to analyse a multiplicity of such data, discern the underlying significance of patterns, prioritize potential threats, and respond efficiently in a coordinated manner. Preparing for Events Long Before They Arrive Effective outbreak detection doesn’t achieve much if it doesn’t lead to a stronger, better-prepared system and faster public health response once such an outbreak does arise. Preparedness, which happens well before official declarations of an emergency, is aided by AI by continuously analysing current and historic surveillance data for patterns that might not have previously been recognised or apparent in conventional methods. These insights inform contingency planning, priority setting, and forecasting healthcare demands and enable anticipation of public vulnerability before an emergency declaration.
A more proactive stance strengthens emergency preparations by preparing public health authorities to act more quickly and precisely should an emergency be declared.
As an example, the World Health Organization’s (WHO) epidemic intelligence initiatives using AI are transforming how health agencies are better prepared to prepare for and respond to outbreaks of potentially significant health threats and other public health emergencies. Enabling Faster, More Effective Public Health Response Once a health event is detected, AI allows health authorities to integrate epidemiological, laboratory, environmental and operational data to create useful intelligence. This helps to:
- Support risk assessment
- Improve situational awareness
- Support contact investigation
- Estimate staffing and healthcare resource needs
- Expedite Situation Reports (SITREPs) to guide operations and planning·
- Empower decision-makers throughout an emergency and support their immediate learning.
Help address issues of health inequities.
By analysing patterns of data across geographical and social population strata and through a number of key health system indicators, AI may be used by public health authorities to target and address underserved populations. AI can support the prioritizing of the interventions and allocate resources according to population need, as supported by the WHO Guidance on Ethics and Governance of Artificial Intelligence for Health. In particular, with the implementation of “health intelligence at scale” with data across geographical, ethnic and vulnerable segments, AI can significantly highlight and support the inclusion of underserved communities in health sector planning, resource allocation and response efforts, so enabling more efficient and more equitable public health work.
Challenges and Considerations
AI needs to have good, complete, current data to be of significant value; thus, establishing the foundation-robust data governance, interoperable information systems, clear privacy protocols,Transparency and robust ethics protocols-will be critical in order to utilize the power of AI effectively for disease surveillance and health response systems.
Furthermore, the role of human experts remains invaluable to ensure AI outputs are contextualized; interpreted effectively within the context of the operational response,and integrated effectively in robust decision-making, thus maximizing AI not to replace but to assist the valuable expertise of our epidemiologists, risk analysts, and other health system experts.
Artificial intelligence, with a clear understanding of its strengths and weaknesses, is rapidly revolutionizing our approach to public health intelligence, turning vast amounts of diverse, scattered information into actionable insight to expedite our decisions and enable stronger, smarter, and better responses to health emergencies. Its promise far transcends the simple detection of initial outbreaks and contributes immeasurable value across a continuum from outbreak risk identification, prioritization and anticipation through to effective risk mitigation efforts, strengthened national and international public health preparedness, timely, effective response, and effective evaluation learning that will make health systems stronger and communities resilient, as they cope with increasingly complex new health challenges.
This is the conversation we are bringing to the GPHCB Global Webinar:
AI for Public Health Surveillance: The Future of Outbreak Detection and Health Intelligence
Join public health professionals, students, researchers, institutions and emerging leaders as we explore how AI and human expertise can work together to detect earlier, decide better and protect communities more effectively.
August 27–29, 2026
Register now: www.gphcb.org/webinar
References:
Public Health Intelligence. Https://www.who.int/teams/health-emergency-intelligence-and-surveillance/public-health-intelligence
After Action Review (AAR). Https://extranet.who.int/ihr/monitoring-evaluation/after-action-review, World Health Organization.
Managing Epidemics: Key Facts About Major Deadly Diseases. Geneva: World Health Organization; 2023.
World Health Organization. Ethics and Governance of Artificial Intelligence for Health. Geneva: World Health Organization; 2021.
Helmholtz Centre for Infection Research. SORMAS (Surveillance Outbreak Response Management and Analysis System).
Https://sormas.org 8. Fhnrich C, Denecke K, Adeoye O, et al. Surveillance Outbreak Response Management and Analysis System (SORMAS) to support the control of the COVID-19 pandemic. Eurosurveillance.
2021;26(43):2003070.
World Health Organization.International Health Regulations (2005). 3rd ed. Geneva: World Health Organization.
