Table 1. Overview of early warning systems for infectious disease outbreaks.

EWS name	Geographical scope	Data source	Disease or syndromes	Strengths	Limitations
Abbreviation: CIDARS=China infectious disease automated-alert and response system; NIDRIS=national notifiable infectious diseases reporting information system; PHREDSS=the NSW public health real-time emergency department surveillance system; NSW=New South Wales; ED=emergency departments; ILI=influenza-like illness; ESSNCE=electronic surveillance system for theearly notification of communicable-based epidemics; USA=United States of America; MTFs=military treatment facilities; EARS=early aberration reporting system; ARTSSN=the alberta real time syndromic surveillance; EDSSS=emergency department sentinel syndromic surveillance system; UK=United Kingdom; NHS=national health service; SARI=severe acute respiratory infections surveillance system; SurSaUD=reactive mortality surveillance system-syndromic surveillance system; Vesuv=national web-based outbreak rapid alert system; MSIS=norwegian surveillance system for communicable diseases; CASE=computer supported outbreak detection system; SmiNet=internet-based surveillance system for communicable diseases; ICARES=integrated crisis alert and response system; GP=general practitioner; GPSURV=electronic general-practitioner-based syndromic surveillance system; SID-SSS=school-based syndromic surveillance system; GOARN=global outbreak alert and response network; EIOS=epidemic intelligence from open sources; EBS=event-based surveillance; FAO=food and agriculture organization; OIE=world organization for animal health; GISRS=WHO global influenza surveillance and response system; NICs=national influenza centers; ERLs=WHO essential regulatory laboratories. * Google Flu Trends stopped publishing current estimates on August 9, 2015.
CIDARS (11)	China	NIDRIS	28 infectious diseases notifiable to NIDRIS	CIDARS uses real-time reporting information via the Internet, enhancing the timeliness and completeness of data reporting; it employs three aberration detection methods to detect unusual occurrences of 28 notifiable infectious diseases; nationwide implementation, involving all levels of CDCs in China, facilitates early outbreak detection and prompt reporting across the country	CIDARS relies on notifiable infectious disease surveillance data, which may be less timely and sensitive compared to other outbreak detection systems using pre-diagnosis data or media reports; generation of negative signals, causing unnecessary signal response work for local staff; maintaining normal operations of the system and managing over 6,000 users pose significant challenges.
PHREDSS (30)	Australia/NSW	ED	Notifiable infectious diseases	Real-time data analysis, early warning system, enhance the overall surveillance capabilities	Limited to ED visits, integration and data sharing with other health surveillance systems can be challenging
FluTracking (31)	Australia	Community-Based Surveillance	ILI	Community reporting; real-time data on ILI trends; cost-effective compared to traditional surveillance methods; wide geographical coverage and public engagement	Reliance on self-reported data, lack of detailed clinical information; potential participant bias; dependent on internet access
ESSENCE (19)	USA	MTFs from over 300 military installations worldwide	Communicable diseases	ESSENCE demonstrates high accuracy in data representation, particularly in gastrointestinal diseases, which have the highest overall sensitivity (89%) and specificity (96%); the military system provides near-complete data coverage of outpatient visits, which is advantageous for public health surveillance	The sensitivity for respiratory diseases was lower (65.7%) compared to gastrointestinal diseases; the quality of data depends on the accuracy of ICD-9 coding
EARS (17)	USA	Public health surveillance data	Deliberately emerging infectious diseases	EARS includes various aberration detection methods, providing flexibility and options for different surveillance needs; it allows local and state health departments to modify sensitivity and specificity thresholds according to their public health importance; and providing flexibility and options for different surveillance needs in various data sources, enhancing its applicability	The effectiveness of EARS depends on the quality and reliability of the input data; challenges in differentiating false alarms, leading to potential over-investigation; complexity in operation and cost and resource-intensive requirements
ARTSSN (32)	Canada/Alberta	Province Electronic Health Record (e.g., Health Link calls, ED visits, school absenteeism, laboratory tests)	Communicable and non-communicable diseases	ARTSSN analyses multiple electronic data sources concurrently in real-time, delivers timely, comprehensive, and automated surveillance; capable of monitoring a wide range of health conditions, including infectious and chronic diseases, injuries, and environmental hazards	Dependence on data source stability, interpreting signals from multiple data streams and understanding free-text medical records remain complex tasks
EDSSS (20)	UK	ED	Pre-defined Syndromic indicators	Timely detection of public health threats, comprehensive data collection, real-time surveillance, and can be adapted to focus on specific health conditions or syndromes.	Dependence on data quality, integration with other health surveillance systems, and limited to ED data.
NHS Direct (21)	UK	National Telephone Health Advice Helpline	10 common syndromes	Unique data sources outside of traditional clinical settings, capturing health concerns that might not result in hospital or GP visits; accessible to a large proportion of the population.	The reliance on self-reported symptoms could lead to variability in data quality and challenges in interpretation; limited clinical validation, and integration with other health surveillance systems.
SARI-surveillance (33)	Germany	Private hospital network	SARI	Provides prompt and reliable information on SARI in patients in Germany; explores different diagnosis groups and classes (admission or discharge; primary or secondary); high sensitivity with Sensitive Case Definition (SCD), which includes not only patients with primary discharge from J09 -J22 but also cases with any secondary discharge diagnosis from J09-J22	Lack of complementary virological information; discharge data lagged the information from primary care sentinel surveillance, leading to delayed availability of data
SurSaUD (34)	France	All-cause mortality data	Mortality variations	The system records 77.5% of the total number of deaths, the extensive coverage ensures effective monitoring of expected and unusual mortality outbreaks; high sensitivity and specificity	Lack of medical causes of death limits the ability to target recommendations and determine the specific contributions of events like influenza epidemics and heatwaves to overall mortality
Vesuv (14)	Norway	MSIS	Infectious diseases notifiable to MSIS	Vesuv allows information included in each notification to be modified and updated as the outbreak investigation progresses, which is practical for users conducting outbreak investigations; Vesuv is an event-based reporting system	Awareness and training needs; cannot quantitatively assess its timeliness, as the date of identification of the outbreak is missing.
CASE (15)	Sweden	SmiNet (national notifiable disease database)	Communicable diseases	CASE is adaptable to different contexts, enhancing its usefulness in various settings; CASE uses emails for notifications, presenting information in a familiar format without requiring users to learn a new interface, simplifying the communication process; allowing users to select statistical methods best suited to the characteristics of a specific disease; this flexibility enables more accurate detection and analysis of disease outbreaks	The effectiveness of the system is highly dependent on the quality and completeness of the data from SmiNet.
ICARES (35)	Netherlands	GP, and hospital records	Clusters of infectious diseases	ICARES can detect differences in the incidence of various disease groups in real-time, within a 24-window; capable of making historical comparisons specific to each healthcare provider and adjusting baseline values for seasonal variations in disease incidence	ICARES faces challenges in measuring sensitivity due to imperfections in coding for non-specific syndromes and a limited number of participating healthcare facilities, resulting in both false positive and false negative alerts
GPSURV (36)	New Zealand	GP electronic clinical records	Three acute infectious disease syndromes	Effectively monitors acute infectious disease syndromes like gastroenteritis, influenza-like illness, and skin infections; adaptive denominator definition ensures a more accurate representation of the patient population	Complexity in denominator population definition; GPSURV’s focus on record completion and data capture, rather than diagnostic reliability, raised questions about the system’s data quality
ProMed-mail (37)	Global	Media reports, official reports, online summaries, local observers, and others	Emerging infectious diseases and toxins	Global reach: ProMED-mail disseminates information to over 30,000 people in more than 180 countries, indicating its wide reach and influence and diverse information sources, including media reports, official reports, online summaries, and observations	The postings on ProMED-mail are often drawn from general media sources and lack scientific language, which can affect their credibility among professionals; a large volume of postings originate from doubtful sources, dependence on confirmation from other sources
Google Flu Trends (27)*	Global	Web search queries related to flu	Influenza	Wide reach, it gathers and analyzes healthcare-seeking behavior in the form of online search queries, reflecting a global user base; usefulness in developed countries; correlation with traditional data	Data inaccuracies and sampling issues; the lack of standardized search criteria means different users may enter symptoms differently, affecting the accuracy and consistency of data
GOARN (23)	Global	Member States, EIOS, EBS, disease-specific networks, network of collaborating institutions, direct reports from the field, and other International organizations (e.g., FAO, and OIE)	Infectious disease and emerging infections	Global collaboration, rapid response capability, expertise and experience, information sharing, capacity building, and research and development	Resource limitations, political and bureaucratic challenges, variability in health systems, data quality, and sharing issues.
GISRS (38)	Global	Influenza surveillance data from NICs, WHO H5 Reference laboratories, ERLs and WHO regional databases	Respiratory viruses, including Influenza	Existing global and regional networks for surveillance of circulating and emerging strains of respiratory viruses, including influenza; robust laboratory network	Varying capacities of NICs; limited geographical coverage; inadequate human and veterinary surveillance; workforce constraints; poor intersectoral coordination; and funding constraints
HealthMap (29)	Global	News reports, eyewitness accounts, and official records	a wide range of health threats, such as infectious diseases and drug resistance	Multistream real-time surveillance; extensive data collection; global reach and language expansion; automated system for data organization; wide usage by government agencies	Dealing with unstructured and unorganized internet data; difficulty in differentiating between distinct types of reports; focus on conspicuous but low-impact events; time-consuming for users
FluNet (39)	Global	Influenza virological surveillance data (NICs of the GISRS)	Influenza	Facilitating the collection and sharing influenza surveillance data from NICs of the GISRS and other national influenza reference laboratories collaborating actively with GISRS across the globe; providing near real-time data on influenza virus strains circulating worldwide; allowing for the analysis of global and regional trends in influenza activities	Quality and timeliness of data depend on the participating countries’ surveillance capacities; underrepresentation of some regions in developing countries; not covering other respiratory viruses or emerging pathogens
EPIWATCH (40)	Global	Open-source data	Infectious disease and emerging infections	Real-time or near-real-time monitoring; data integration from various sources; early warning capability; customizable alerts based on specific diseases or geographical areas of interest	Effectiveness depends on the quality and reliability of the data fed into it; a limited scope, focusing on specific diseases or regions; potential false alarms or overreport, leading to unnecessary responses