Syndromic Surveillance 2.0: Emerging Global Surveillance Strategies for Infectious Disease Epidemics

by Rajeev Ramchand, Sangeeta C. Ahluwalia, Mary Avriette, Gary Cecchine, Monika Cooper, Christy Foran, Daniel Hicks, Natasha Lander, Sarita D. Lee

This Article

RAND Health Quarterly, 2023; 11(1):8


The U.S. Army has a long history of preventing, detecting, and treating infectious diseases. Like other organizations and agencies involved in public health, the Army is increasingly interested in syndromic surveillance strategies—those designed to identify outbreaks before clinical data are available. Researchers use various methods to identify surveillance strategies across the globe, investigate these strategies' benefits and limitations, and recommend actions to aid the Army in their efforts to detect emerging epidemics and pandemics.

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The research reported here was completed in June 2022, followed by security review by the sponsor and the U.S. Army Office of the Chief of Public Affairs, with final sign-off in August 2023.

The U.S. Army has a long history of preventing, detecting, and treating infectious diseases for American and allied forces and, by extension, protecting populations worldwide. Like other organizations and agencies involved in public health, the Army is increasingly interested in syndromic surveillance strategies—those that are designed “to identify illness clusters early, before diagnoses are confirmed and reported to public health agencies, and to mobilize a rapid response, thereby reducing morbidity and mortality” (Henning, 2004).

The Army asked the RAND Corporation's Arroyo Center to research how the next generation of epidemic surveillance strategies could identify emerging epidemics and pandemics in the near and far future. We focused this task to investigating how new strategies might be used to gain early insights into emerging epidemics across the globe.

Sources of Information

This study synthesizes information from multiple sources, including data from:

  • Peer-reviewed literature: We conducted a systematic literature review to identify and describe strategies for surveillance of syndromes indicating potential outbreaks, to characterize their implementation and effectiveness, and to describe their potential for continued or expanded use. Our search strategy resulted in 80 unique articles describing syndromic surveillance strategies.
  • Global patent search: We searched a global patent database for patents filed between 2010 and 2020. Twelve patents, including two duplicates, were deemed relevant.
  • Media scan: RAND Arroyo conducted a search of news and working papers limited to the dates 2010 to 2020. The news search returned 75 articles that described a company, service, or method relevant to the research question.
  • Market environmental scan: When literature, news, patents, or company information identified other services, institutions, or strategies, we examined the company using MarketLine or simple web searches.
  • Interviews with subject-matter experts: We conducted interviews with 27 subject-matter experts to confirm findings from the literature review and scan of private enterprises and to capture new insights.

A Framework for Surveillance

We created a three-phase epidemic surveillance framework to categorize the strategies used to conduct syndromic surveillance of infectious diseases. The phases are:

  • Phase 1—Emergence: Strategies for surveilling for the potential emergence of an outbreak track the prevalence and characteristics of pathogens in reservoir populations, known and unknown, with the potential for jumping to human populations.
  • Phase 2—Outbreak: Strategies for surveilling for the presence of an outbreak identify localized events of novel or alarming infections in human populations to determine whether a particular pathogen has spread to or across humans within a region.
  • Phase 3—Spread: Strategies for tracking the extent of or potential for geographic disease spread indicate how widely diseases that have progressed beyond outbreaks have spread within or among populations and the potential for continued spread.

Across these phases, several factors might directly relate to the benefits and limitations of specific syndromic surveillance strategies. We categorized these factors as:

  • Mathematic factors: Epidemics are relatively rare, so probabilistically many strategies designed to detect them will suffer from high rates of false positives.
  • Biologic factors: Systems can be designed to identify specific biologic pathogens, or they could be pathogen agnostic.
  • Geographic and environmental factors: Illnesses might exhibit seasonal variation in their rate of spread, and geographic and environmental factors influence the types of surveillance systems that are developed and implemented.
  • Social and economic factors: Social and economic factors might determine the extent of the human-animal interface in a society, affect public health system resources, affect the efficacy of surveillance systems, and influence behaviors and settings that affect disease spread.
  • Behavioral and cultural factors: Behavioral and cultural factors influence how individuals in society interact, how they perceive health risks, and the extent to which they follow public health guidance and engage with the health care system.
  • Historic, political, and institutional factors: History, particularly past experiences with outbreaks or epidemics, could influence future responses. Political will, transparency, data infrastructure, and logistics affect whether and how accurately pathogen spread is reported. Political and institutional factors influence the design and implementation of disease surveillance systems.
  • Demographic factors: For many communicable diseases, large differences in the disease burden across population characteristics, such as age, gender, ethnicity, and race, influence surveillance efforts.

Strategies for Surveillance

Table 1 lists the strategies for syndromic surveillance identified across the three epidemic phases.

Table 1. Summary of Syndromic Surveillance Strategies

Strategy Benefits Limitations Private-Sector Enterprises
Phase 1—Strategies for surveilling the potential emergence of an outbreak
Veterinary public health surveillance: surveillance in animal populations to detect known and novel zoonotic diseases to prevent a spillover event
  • Ability to identify potential outbreaks early
  • Ability to be implemented in regions with high levels of human-animal interface
  • Affordability
  • Possibility of missing diseases with high and rapid mortality (e.g., highly pathogenic avian flu)
  • Inability of some pathogen-specific systems to detect novel diseases
  • Reliance on humans and associated delays
  • Less effective in remote areas with spotty mobile phone coverage
  • IDseq
  • PREDICT (a collaboration between U.S. Agency for International Development and EcoHealth Alliance)
Remote sensing: use of satellite imagery to track environmental (e.g., rising sea levels, forest fires) or socioecological (e.g., urbanization) drivers of infection
  • Broad geographic coverage
  • Little need to rely on other governments for information
  • Technologically complex
  • Currently primarily academic
  • Gap between where surveillance systems are developed (richer countries) and where they are needed (low-income settings with weaker infrastructure)
  • None identified
Phase 2—Strategies for surveilling for the presence of an outbreak
Surveys: high frequency, geographically disaggregated indicators (e.g., self-reports of symptoms, symptom severity, and/or risk and protective behaviors) to capture the severity of the outbreak
  • Ability to flexibly collect data on factors that are known to affect risk
  • Potential data lag
  • Possible intensive and costly data processing
  • Possible unavailability of in-person collection of surveys during epidemics
  • Magpi
Web searches: tracking internet search terms to detect new or emerging disease outbreaks or disease symptoms, or to track the course of an outbreak
  • Timeliness of data
  • Nonreliance on government data
  • Inclusion of symptoms outside the health care setting
  • Ability to aid with forecasting
  • Voluminous amounts of data
  • Regional variation in source data
  • Limited data availability (from third party owners)
  • Time dependency
  • Potential bias for cities in regions with low internet penetration or media coverage on the disease
  • BlueDot
  • Google Trends
Social media mentions: tracking mentions of diseases and/or symptoms on social media platforms
  • Timeliness of data
  • Nonreliance on government data
  • Inclusion of symptoms outside the health care setting
  • Ability to aid with forecasting
  • Voluminous amounts of data
  • Regional variation in source data
  • Poor data availability
  • Time dependency
  • Potential bias for cities in regions with low internet penetration
  • Sensitivity to targeted disinformation
  • Bellingcat
  • Kinsa Health
  • Sickweather
Media monitoring and web scraping: algorithms that scan electronic news sources for indicators of confirmed or potential outbreaks
  • Quicker identification of emerging issues than traditional systems and ability to support secondary prevention
  • Limitation to local news media because of language and accessibility
  • Selection bias in reporting that could unduly influence predictive ability
  • Resource-intensive manual scanning and abstraction
  • Travax by Shoreland, Inc.
  • ProMED
  • HealthMap
  • BlueDot
  • Google Trends
  • Bellingcat
Pharmaceutical sales: examining the trends in over-the-counter (OTC) drug sale data, which measures consumers’ purchasing immediately upon recognizing symptoms
  • Timeliness of data
  • Inclusion of symptoms outside the health care setting
  • Limited data availability
  • Lack of specificity because most sales are for a broad variety of symptoms
  • Possible reflection of only certain populations
  • Real-time Outbreak and Disease Surveillance (RODS)
School and work absences: tracking absences from school and from work
  • Low-cost
  • School-based programs that focus on children (key population for spread)
  • Inclusion of symptoms outside the health care setting
  • Unreliable data collection across systems
  • Best detection only for rapid and severe symptom onset resulting in absenteeism
  • Possible inability to capture work-from-home arrangements
  • None identified
Data collection by health care staff: health care workers and/or administrative staff report prespecified surveillance data (e.g., temperatures or respiratory symptoms) in health care or other high-risk settings (e.g., postdisaster)
  • Ease of use
  • Low cost
  • Poor data quality
  • Resource burden
  • Difficulty to assess utility (i.e., sensitivity, positive predictive value)
  • Requirement of a known symptom set or syndrome to aggregate cases
  • nference
  • IBM Explorys solutions
Consumer expenditure data: examining trends in consumer expenditures to measure and map the extent of an outbreak
  • Behavioral indicator of epidemic risk and spread that does not rely on health infrastructure
  • Potential for the inclusion of symptoms outside the health care setting
  • Poor data access and availability
  • Lack of specificity about the type of pathogen
  • Different expectations for different populations or socioeconomic conditions
None identified
Geospatial techniques: use of user-supplied or automated geographic information system data to identify geographic concentrations of disease risk and map epidemic emergence and spread
  • Low cost
  • Online dashboards and mapping technologies that provide the public with information about epidemic spread and risk levels in their community
  • Ethical concerns regarding privacy
  • Techniques that are not well established
  • Lack of formal systems in government or other institutions
  • Kinsa Health (uses location and thermometer data to show large-scale fever data)
Wastewater sampling: collecting and testing municipal wastewater
  • Cost- and time-efficient approach
  • Targeted monitoring based on geographic area or population
  • Ability to provide early warnings of outbreaks in new areas
  • Inability to perform contact tracing or to identify affected persons
  • Reliance on municipal sewer systems
  • Lower effectiveness among highly mobile or transient populations
  • Biobot Analytics (samples wastewater systems for viral RNA and maps findings over time)
Phase 3—Strategies for tracking the extent of or potential for geographic disease spread
Population movement indicators: tracking day-to-day population mobility, mass migrations, and/or gatherings among displaced populations or those gathering for cultural
  • Promising evidence for tracking spread
  • New tools in development
  • Data that is generally derived from proxy indicators of time spent places and moving between places
  • Travax by Shoreland,

Notable Surveillance Systems

The syndromic surveillance strategies listed above are already in use in some surveillance systems or could be added to these systems in the future. We identified the following U.S. Department of Defense (DoD) surveillance systems currently in use:

  • Global Emerging Infections Surveillance (GEIS): a global laboratory network tasked with providing technical support to geographic combatant commands (GCCs); conducting infectious disease surveillance; improving DoD laboratory readiness for outbreak; and enhancing collaboration among the GCCs, GEIS partners, and U.S. and international interagency partners
  • Defense Medical Surveillance System (DMSS): person-level collection of medical events, personal characteristics, and military deployments of all service members
  • Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE): a secure web-based system that primarily receives and categorizes electronic emergency department data for both civilian health systems and military treatment facilities
  • Disease Reporting System internet (DSRi): integrated database from DoD's electronic health records (Armed Forces Health Longitudinal Technology Application and Military Health System [MHS] Genesis), laboratory results, DMSS, and ESSENCE.

In addition, the following systems were mentioned routinely in our literature review and/or our interviews:

  • PREDICT EcoHealth: a collaborative that characterizes the virome at the human-wildlife interface by sampling wild animals across the globe and using metagenomic methods to identify viruses
  • Global Public Health Intelligence Network: a big data aggregator, run by Public Health Agency of Canada, that scans online news reports in nine languages for potential signals of emerging public health threats, then uses machine learning algorithms and human analysts (who use their expertise in medicine, public health, and additional scientific fields to comb through the news reports, social media, and manually identified sources) to assess health threats
  • ProMED: a service that invites and accepts reports of potential outbreaks from a variety of sources, including local media, professional networks, on-the-ground experts, and the general public; adds expert review; and curates reports to help disseminate potential threats
  • European Centre for Disease Prevention and Control tools for epidemiological threats and outbreaks: tools used for surveillance and to respond to epidemiological threats and outbreaks.

Perspectives on the Application of Syndromic Surveillance Strategies

Interviewees reported on a variety of factors that might influence the strategies available for outbreak surveillance. There were many issues related to the data that are used, including the quality, biases, availability, and the ways that data are presented to consumers. Interviewees also noted the importance of evaluating systems to ensure that they yield accurate and useful information. They discussed the challenge of data silos—wherein systems for detecting one disease could be used for other diseases, but, according to one interviewee, “people only look for what they're funded to look for.” Interviewees also discussed leaders’ political will for surveilling for and reporting on outbreaks and how the culture of a region or community influences its surveillance capabilities. Western approaches are viewed as myopic in this regard, particularly if they rely on individuals to report illness or seek out care in traditional health care settings. Finally, experts consistently said that one of the key problems with syndromic surveillance is not the lack of systems but ensuring those who need the systems are able to access them and support upgrades and developments that improve the systems for future use.

Our interviews with representatives with DoD suggested uneven knowledge about or access to surveillance systems—different commands prioritize different strategies to retrieve information about disease threats—and some described syndromic surveillance activities as a “patchwork” approach. We were told that, to support the combatant command (CCMD), the Defense Health Agency's Armed Forces Health Surveillance Division is working to establish an improved biosurveillance hub that incorporates existing capabilities to eventually streamline the number of systems, but this is a new effort as of this writing.


Recommendation 1. The Army should track academic and private enterprise efforts to detect diseases during the outbreak phase of epidemic surveillance. Because there is so much activity in this domain—activity that we assume will grow in the context of the coronavirus disease 2019 pandemic—the Army should continue to monitor progress but not necessarily invest in additional methods above and beyond its current investments in GEIS, DSRi, DMSS, and ESSENCE.

Recommendation 2. The Army should establish more routine training to aid general medical officers in identifying and obtaining credible data and analyzing and interpreting the data. Our interviews with defense agency and CCMD representatives revealed that, although numerous systems exist to help CCMDs and services track possible disease outbreaks, there appears to be uneven awareness among some military medical personnel regarding what systems and information they can—or should—use for this purpose.

Recommendation 3. The Army should consider investing in surveillance efforts that detect the possible emergence of an epidemic for use during the emergence phase of epidemic surveillance. Detecting viruses in animal populations is crucial to understanding the risk to humans at the human-animal interface, where most spillover events occur. The Army Veterinary Corps might be exceptionally well positioned to create or complement existing global veterinary public health surveillance efforts in support of the operational force. The Army might also be able to contribute to science or systems that apply remote sensing strategies to identify where environmental changes could increase risk for the emergence of an outbreak. Currently, human health outbreak monitoring is anchored in the MHS and might pick up only threats warranting medical attention. Investment in the emergence phase might be more predictive and reduce risk.

Recommendation 4. The Army should consider investing in surveillance efforts that detect the confirmed or potential geographic spread of an outbreak for use during the spread phase of epidemic surveillance. If the Army has or can negotiate direct access to data on population movement, it might be able to contribute to modeling the potential for disease spread globally.

Recommendation 5. The Army should leverage opportunities to engage in regional and international dialogues, where appropriate, to enhance coordination and information-sharing. The Army should seek to inform or participate in engagements with foreign partners led by civilian counterparts in the U.S. Department of State, U.S. Agency for International Development, or U.S. Centers for Disease Control and Prevention, for example. In addition, military-to-military engagements led by CCMDs or defense agencies also provide opportunities for Army personnel to become better integrated into discussions regarding data sharing and integration.

Recommendation 6. The Army should sustain, maintain, and update current disease surveillance efforts and encourage the same investment throughout DoD. Current Army surveillance efforts are exemplified by GEIS, DMSS, DSRi, and ESSENCE. Sustaining, maintaining, and updating DoD systems and additional resources for syndromic surveillance can help mitigate risks to all the armed forces, not just the U.S. Army.


Henning, K. J., "What Is Syndromic Surveillance?" MMWR Supplements, Vol. 53, September 2004.

The research described in this article was sponsored by the U.S. Army Office of the Surgeon General and conducted by the Personnel, Training, and Health Program within the RAND Arroyo Center.

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