The Role of Disease Surveillance Systems in Emergency Situations

When a disaster strikes – whether it’s a flood, an earthquake, or a conflict – the immediate threats are obvious: collapsed buildings, displacement, loss of life. But there’s another danger that often follows close behind, and it can be just as deadly: the outbreak of infectious diseases. Overcrowded shelters, contaminated water supplies, and disrupted healthcare systems create the perfect conditions for diseases like cholera, measles, and respiratory infections to spread rapidly. This is precisely where disease surveillance systems step in, acting as the frontline defence that helps detect, track, and contain health threats before they spiral out of control.

Why rapid disease detection matters in emergencies 🔗

Humanitarian emergencies increase the risk of transmission of infectious diseases and other health conditions such as severe malnutrition. When routine healthcare infrastructure collapses – as it often does during disasters – the normal channels for detecting and reporting illness break down. Diseases that were previously under control can resurge, and new outbreaks can emerge undetected for days or even weeks.

This delay is dangerous. An effective disease surveillance system is essential to detecting disease outbreaks quickly before they spread, cost lives and become difficult to control. Consider the aftermath of a major flood: thousands of people packed into temporary shelters, drinking water from untested sources, and children missing routine vaccinations. In such settings, a single case of cholera or measles can rapidly become an epidemic if not caught early.

Speed is the critical factor. During humanitarian emergencies, detecting and responding swiftly to epidemics is key in order to reduce unnecessary illness and death, especially among refugees and displaced people. Surveillance systems provide the structured mechanism to ensure that this rapid detection actually happens, even when everything else has been disrupted.

What happens without surveillance? 🔗

Without a functioning surveillance system, health workers are essentially working blind. They may treat individual patients, but they have no way to see patterns – no way to know that ten clinics across a displaced population are all seeing spikes in watery diarrhoea at the same time. By the time the pattern becomes obvious on its own, the window for effective containment has often closed. Clinical surveillance of infectious disease is inadequate in much of the developing world due to limited funding for public health infrastructure, and because many impoverished regions are also at high risk for emerging disease threats, alternative methods of surveillance are crucial to global health.

Components of a disease surveillance system 🔗

Public health surveillance is the ongoing systematic collection, analysis, and interpretation of data, closely integrated with the timely dissemination of these data to those responsible for preventing and controlling disease and injury. While that definition sounds straightforward, making it work in a disaster zone requires several interconnected components working together.

Case detection and reporting 🔗

The foundation of any surveillance system is the ability to identify and report cases. In emergency settings, this typically relies on syndromic surveillance – identifying patients based on a set of symptoms (or “syndromes”) rather than waiting for laboratory-confirmed diagnoses. Since syndromic surveillance is inexpensive and faster than systems that require laboratory confirmation, it is often the first kind of surveillance begun in a developing country or an emergency setting. For example, instead of waiting for a lab to confirm cholera, health workers report any case of acute watery diarrhoea, which triggers investigation.

Each disease tracked in an emergency surveillance system is defined by a standard case definition – a specific set of signs and symptoms that a health worker can recognise without specialised equipment. For each of the epidemic-prone diseases, a threshold value for alerting to a potential outbreak is set, and whenever the threshold is passed, the system flags the event for rapid investigation.

Data collection and analysis 🔗

Once cases are identified, data needs to flow from health facilities to central coordination points. This includes information about the patient’s location, symptoms, age, and timeline of illness. A line listing of sick individuals with the respective dates is very useful for compiling the number of disease cases by the week or month, and the date of illness onset is the date used for disease surveillance and for plotting an epidemic curve during an outbreak.

Health data analysts then look for patterns: are cases clustering in one location? Is there a sudden spike in a particular syndrome? Are certain age groups disproportionately affected? These patterns guide the public health response – from deploying oral rehydration supplies to launching emergency vaccination campaigns.

Laboratory sampling and confirmation 🔗

While syndromic surveillance provides speed, laboratory confirmation provides accuracy. Even in emergency settings, collecting samples for testing remains critical. A “rash illness,” for instance, could be anything from a mild viral infection to a dangerous haemorrhagic fever. Laboratory data helps distinguish between these and ensures the right response is mobilised. In practice, emergency surveillance systems operate on a two-track approach – acting fast based on syndromic data while simultaneously collecting samples for confirmation.

The EWARN model 🔗

One of the most widely used frameworks for emergency disease surveillance is the Early Warning, Alert and Response Network (EWARN). An EWARN is a network of health partners that collect and report surveillance data on selected epidemic-prone diseases, as part of establishing an early warning system for disease outbreaks in humanitarian situations.

The system has two main components. An immediate alert component signals the early stages of an outbreak, requiring urgent investigation for verification, while a weekly reporting component aggregates data from health facilities to provide information on disease trends for priority health conditions.

WHO has also developed a practical toolkit called “EWARS in a box.” This kit contains 60 mobile phones, laptops, and a local server to collect, report, and manage disease data, along with a solar generator and solar chargers that allow the equipment to function without 24-hour electricity. A single kit costs approximately US$15,000 and can support surveillance for 50 fixed or mobile clinics serving roughly 500,000 people. This kind of deployable, self-contained system is vital in post-disaster settings where infrastructure has been destroyed.

Global disease surveillance efforts and international coordination 🔗

Diseases don’t respect borders. An outbreak in one country can quickly become a regional or global crisis, especially when populations are on the move due to conflict or disaster. This reality makes international coordination in disease surveillance not just useful, but essential.

The International Health Regulations (IHR) 🔗

The backbone of global disease surveillance coordination is the International Health Regulations (IHR), a legally binding instrument adopted by 196 countries. The IHR provide an overarching legal framework that defines countries’ rights and obligations in managing public health risks, events, and emergencies that have the potential to cross borders.

The IHR require States Parties to develop core capacities for rapid detection and response, including for surveillance, laboratories, and risk communication – buttressed by legislation, financing, and national focal points. In practical terms, this means every country is expected to have the ability to spot a potential health emergency and report it to WHO within 24 hours of assessment.

WHO’s role and the PHEIC mechanism 🔗

WHO is the only organisation with the mandate to monitor and respond to global public health threats, as established in the International Health Regulations. When a disease event is assessed as posing an extraordinary risk to multiple countries, the WHO Director-General can declare a Public Health Emergency of International Concern (PHEIC).

Six events were declared PHEIC between 2007 and 2020, including the 2009 H1N1 influenza pandemic, Ebola outbreaks, poliomyelitis, Zika, and COVID-19. These declarations serve to mobilise international coordination, streamline funding, and accelerate the development of medical countermeasures.

Sentinel surveillance and collaborative networks 🔗

Beyond the formal EWARN and IHR frameworks, a range of collaborative surveillance networks operate globally. Sentinel surveillance systems help fill information gaps caused by political unrest, limited infrastructure, or other challenges by closely monitoring the disease situation in specified areas. These sentinel sites serve as early warning points, feeding data into the broader global picture.

Field epidemiology training programmes also play a significant role. Since 1975, the CDC and WHO have collaborated with more than 30 countries to strengthen health systems and address training needs for disease detection and response. These programmes build local capacity so that countries can run their own surveillance systems rather than depending entirely on external support during crises.

Best practices for improving surveillance systems 🔗

While disease surveillance during emergencies has improved significantly over the past two decades, major gaps remain. Several best practices are emerging that can strengthen these systems further.

Integrating artificial intelligence and digital tools 🔗

Technology is rapidly changing what’s possible in disease surveillance. AI has emerged as a powerful tool in infectious disease surveillance, offering the potential to transform early detection and response by analysing extensive datasets from sources such as electronic health records, travel records, social media, and weather data to identify patterns that signal potential outbreaks.

A well-known example is BlueDot, an AI-driven platform that detected the emergence of COVID-19 in Wuhan, China, nine days before the official announcement of the outbreak, and by analysing real-time air travel data, it also predicted which cities were most at risk of spread.

Other innovations include wastewater surveillance for detecting pathogens in communities, wearable health devices that can flag unusual health patterns in real time, and natural language processing tools that scan news reports and social media for early signals of outbreaks. Emerging technologies such as advanced analytics, AI, the Internet of Things, remote sensing, and molecular tools offer the potential to enhance surveillance capabilities through real-time monitoring, early anomaly detection, and improved prediction of outbreak risks.

Investing in workforce training 🔗

No technology can replace trained health workers on the ground. Surveillance systems are only as good as the people operating them – from the community health worker recognising symptoms at a camp clinic to the epidemiologist analysing data patterns at a coordination centre. Investments should focus not only on internet connectivity and data storage but also on building institutional capacity and training skilled personnel, ensuring that every country can collect, store, and analyse its own data.

Investing in training for health workers and establishing ethical frameworks around AI use will ensure trust and transparency in using these powerful technologies. Training must cover both traditional epidemiological skills and new digital tools to prepare the workforce for modern surveillance demands.

Strengthening data sharing and standardisation 🔗

One of the biggest obstacles to effective global surveillance is fragmented data. Different countries use different reporting formats, different case definitions, and different timelines. Health data are often fragmented, inconsistent, and siloed across different regions and institutions, and establishing centralised and collaborative data platforms that allow timely and secure exchange of public health information across countries is essential.

The Global Fund’s SONAR programme, implemented by The Task Force for Global Health, is one initiative working to address this. SONAR aims to enhance early warning surveillance in low- and middle-income countries by implementing surveillance and epidemic intelligence systems, increasing interconnectivity between laboratory and epidemiological data, and improving accessibility of data for decision-makers.

Ensuring ethical and equitable surveillance 🔗

As surveillance systems become more technologically advanced, questions around data privacy, consent, and equity become more pressing. AI systems can utilise federated learning, which allows algorithms to learn from decentralised data sources without transferring sensitive information to a central server, helping maintain patient confidentiality while still enabling valuable insights. Establishing clear ethical frameworks is essential to ensure surveillance tools serve public health without eroding trust or individual rights.

Equity is equally important. Advanced AI-powered surveillance tools are of little value if they are only available in wealthy nations. Achieving transparent and effective AI-driven surveillance will require global collaboration, standardised data practices, sustained investment in infrastructure and workforce training, and clear ethical frameworks.

Looking ahead: building resilient surveillance for future emergencies 🔗

The COVID-19 pandemic exposed both the strengths and weaknesses of global disease surveillance. Countries with robust systems detected cases faster and responded more effectively. Countries without them struggled – and the consequences were measured in lives lost. The lesson is clear: investing in disease surveillance during “peacetime” is far more effective and far less costly than trying to build systems from scratch during a crisis.

The future of emergency disease surveillance lies in combining traditional epidemiological methods with modern digital tools, strengthening international cooperation through the IHR framework, and ensuring that every country – regardless of income level – has the capacity to detect, report, and respond to disease threats. Programmes like EWARS, SONAR, and AI platforms like BlueDot and HealthMap are paving the way, but sustained political will and funding remain the most critical ingredients.

What do you think? How can countries with limited healthcare infrastructure realistically build and maintain effective disease surveillance systems during emergencies? And as AI becomes more central to outbreak detection, how should the global community balance the benefits of data-driven surveillance with the need to protect individual privacy?

References

1. https://www.who.int/health-topics/international-health-regulations
2. https://www.cdc.gov/global-health/topics-programs/ihr.html
3. https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1601151/full
4. https://www.taskforce.org/disease-surveillance/
5. https://pmc.ncbi.nlm.nih.gov/articles/PMC12587752/