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We evaluated the performance of X-bar chart, exponentially weighted moving average, and C3 cumulative sums aberration detection algorithms for acute diarrheal disease syndromic surveillance at naval sites in Peru during 2007-2011. The 3 algorithms' detection sensitivity was 100%, specificity was 97%-99%, and positive predictive value was 27%-46%.

Public health practitioners and researchers have used traditional medical databases to study and understand public health for a long time. Recently, social media data, particularly Twitter, has seen some use for public health purposes. Every large technological development in history has had an impact on the behaviour of society. The advent of the internet and social media is no different. Social media creates public streams of communication, and scientists are starting to understand that such data can provide some level of access into the people's opinions and situations. As such, this paper aims to review and synthesize the literature on Twitter applications for public health, highlighting current research and products in practice. A scoping review methodology was employed and four leading health, computer science and cross-disciplinary databases were searched. A total of 755 articles were retreived, 92 of which met the criteria for review. From the reviewed literature, six domains for the application of Twitter to public health were identified: (i) Surveillance; (ii) Event Detection; (iii) Pharmacovigilance; (iv) Forecasting; (v) Disease Tracking; and (vi) Geographic Identification. From our review, we were able to obtain a clear picture of the use of Twitter for public health. We gained insights into interesting observations such as how the popularity of different domains changed with time, the diseases and conditions studied and the different approaches to understanding each disease, which algorithms and techniques were popular with each domain, and more. •Twitter is a very popular microblogging platform with over 300 million active users.•We analyse the literature to understand Twitter's capability to provide a useful tool for public health.•We found that Twitter can be used for surveillance, event detection, pharmacovigilance, disease tracking and forecasting.•Twitter is mostly used in the context of flu, drug abuse, depression and dengue.•Our analysis presents the modern landscape of public health using social media data in combination with Machine Learning.

Coronavirus disease (COVID-19) has been an unprecedented challenge to the global health care system. Tools that can improve the focus of surveillance efforts and clinical decision support are of paramount importance. The aim of this study was to illustrate how new medical informatics technologies may enable effective control of the pandemic through the development and successful 72-hour deployment of the Honghu Hybrid System (HHS) for COVID-19 in the city of Honghu in Hubei, China. The HHS was designed for the collection, integration, standardization, and analysis of COVID-19-related data from multiple sources, which includes a case reporting system, diagnostic labs, electronic medical records, and social media on mobile devices. HHS supports four main features: syndromic surveillance on mobile devices, policy-making decision support, clinical decision support and prioritization of resources, and follow-up of discharged patients. The syndromic surveillance component in HHS covered over 95% of the population of over 900,000 people and provided near real time evidence for the control of epidemic emergencies. The clinical decision support component in HHS was also provided to improve patient care and prioritize the limited medical resources. However, the statistical methods still require further evaluations to confirm clinical effectiveness and appropriateness of disposition assigned in this study, which warrants further investigation. The facilitating factors and challenges are discussed to provide useful insights to other cities to build suitable solutions based on cloud technologies. The HHS for COVID-19 was shown to be feasible and effective in this real-world field study, and has the potential to be migrated.

The COVID-19 pandemic is exerting major pressures on society, health and social care services and science. Understanding the progression and current impact of the pandemic is fundamental to planning, management and mitigation of future impact on the population. Surveillance is the core function of any public health system, and a multi-component surveillance system for COVID-19 is essential to understand the burden across the different strata of any health system and the population. Many countries and public health bodies utilise ‘syndromic surveillance’ (using real-time, often non-specific symptom/preliminary diagnosis information collected during routine healthcare provision) to supplement public health surveillance programmes. The current COVID-19 pandemic has revealed a series of unprecedented challenges to syndromic surveillance including: the impact of media reporting during early stages of the pandemic; changes in healthcare-seeking behaviour resulting from government guidance on social distancing and accessing healthcare services; and changes in clinical coding and patient management systems. These have impacted on the presentation of syndromic outputs, with changes in denominators creating challenges for the interpretation of surveillance data. Monitoring changes in healthcare utilisation is key to interpreting COVID-19 surveillance data, which can then be used to better understand the impact of the pandemic on the population. Syndromic surveillance systems have had to adapt to encompass these changes, whilst also innovating by taking opportunities to work with data providers to establish new data feeds and develop new COVID-19 indicators. These developments are supporting the current public health response to COVID-19, and will also be instrumental in the continued and future fight against the disease.

Syndromic surveillance now forms an integral part of the surveillance for a wide range of hazards in many countries. Establishing syndromic surveillance systems can be difficult due to the many different sources of data that can be used, cost pressures, the importance of data security, and the presence of different (and rapidly evolving) technologies. Here we describe major points in the development of the UK Health Security Agency English real-time syndromic surveillance service over its first 2 decades (1998 to 2018). We identify the key wider themes that we believe are important in ensuring a sustainable and useful syndromic surveillance service. We conducted semistructured interviews with current members of the UK Health Security Agency syndromic surveillance team who were involved from the earliest stages and previous senior colleagues who were supportive of the syndromic surveillance work during the early phases. For this viewpoint, we partitioned the development of syndromic surveillance in England into 3 time periods: 1998 to 2005 (“the beginnings”); 2006 to 2011 (“the growth phase”); and 2012 to 2018 (“mainstream”). We asked the interviewees for their views about the development of syndromic surveillance, and in particular the main drivers and events, the team and system, and outputs and uses. The results from the interviews highlighted some key themes including the integration of syndromic surveillance into the public health system, creativity, good collaboration and teamwork, leadership and determination to persevere, and agility and the ability to adapt to new threats. Using the results of the discussions and our personal experience of running the syndromic surveillance service from inception and over decades, we constructed a set of recommendations for establishing and running sustainable syndromic surveillance systems. In this age of increased automation, with the ability to transfer data in real-time and to use machine learning and artificial intelligence, we are approaching a “new age of syndromic surveillance.” We consider that the focus on the public health questions, relationships, collaboration, leadership, and true teamwork should not be underestimated in the success of and usefulness of real-time syndromic surveillance systems.

Syndromic surveillance is a form of surveillance that generates information for public health action by collecting, analysing and interpreting routine health-related data on symptoms and clinical signs reported by patients and clinicians rather than being based on microbiologically or clinically confirmed cases. In England, a suite of national real-time syndromic surveillance systems (SSS) have been developed over the last 20 years, utilising data from a variety of health care settings (a telehealth triage system, general practice and emergency departments). The real-time systems in England have been used for early detection (e.g. seasonal influenza), for situational awareness (e.g. describing the size and demographics of the impact of a heat-wave) and for reassurance of lack of impact on population health of mass gatherings (e.g. the London 2012 Olympic and Paralympic Games). We highlight the lessons learnt from running SSS, for nearly two decades, and propose questions and issues still to be addressed. We feel that syndromic surveillance is an example of the use of 'big data', but contend that the focus for sustainable and useful systems should be on the added value of such systems and the importance of people working together to maximise the value for the public health of syndromic surveillance services.

How do algorithms shape the imaginary and practice of security? Does their proliferation point to a shift in the political rationality of security? If so, what is the nature and extent of that shift? This article argues that efforts to strengthen global health security are major drivers in the development and proliferation of new algorithmic security technologies. In response to a seeming epidemic of potentially lethal infectious disease outbreaks – including HIV/AIDS, Severe Acute Respiratory Syndrome (SARS), pandemic flu, Middle East Respiratory Syndrome (MERS), Ebola and Zika – governments and international organizations are now using several next-generation syndromic surveillance systems to rapidly detect new outbreaks globally. This article analyses the origins, design and function of three such internet-based surveillance systems: (1) the Program for Monitoring Emerging Diseases, (2) the Global Public Health Intelligence Network and (3) HealthMap. The article shows how each newly introduced system became progressively more reliant upon algorithms to mine an ever-growing volume of indirect data sources for the earliest signs of a possible new outbreak – gradually propelling algorithms into the heart of global outbreak detection. That turn to the algorithm marks a significant shift in the underlying problem, nature and role of knowledge in contemporary security policy.

Syndromic surveillance for respiratory infections such as COVID-19 is a crucial part of the public health surveillance toolkit as it allows decision makers to detect and prepare for new waves of the disease in advance. However, it is labor-intensive, costly, and increases exposure to survey personnel. This study assesses the feasibility of conducting a mobile phone-based respiratory syndromic surveillance program in a middle-income country during a public health emergency, providing data to support the inclusion of this method in the standard infection control protocols at the population level. This study aims to assess the feasibility of a national active syndromic surveillance system for COVID-19 disease in Colombia. In total, 2 pilots of syndromic mobile phone surveys (MPSs) were deployed using interactive voice response technology in Colombia (367 complete surveys in March 2022 and 451 complete surveys in April and May 2022). Respondents aged 18 years and older were sampled using random digit dialing, and after obtaining consent, they were sent a 10-minute survey with modules on sociodemographic status, respiratory symptoms, past exposure to COVID-19 infection and vaccination status, preferences about COVID-19 vaccination, and information source for COVID-19. Pilot 1 used a nationally representative sample while pilot 2 used quota sampling to yield representative results at the regional level. In this work, we assessed the performance characteristics of the survey pilots and compared the demographic information collected with a nationally representative household survey. For both pilots, contact rates were between 1% and 2%, while participation rates were above 80%. The results revealed that younger, female, and higher educated participants were more likely to participate in the syndromic survey. Survey rates as well as demographics, COVID-19 vaccination status, and prevalence of respiratory symptoms are reported for both pilots. We found that respondents of the MPSs are more likely to be younger and female. In a COVID-19 pandemic setting, using an interactive voice response MPS to conduct syndromic surveillance may be a transformational, low-risk, and feasible method to detect outbreaks. This evaluation expects to provide a path forward to the inclusion of MPSs as a traditional surveillance method.

Syndromic surveillance has evolved into a vital public health tool, providing near real-time data to detect and respond to health threats. While states administer syndromic surveillance systems, Tribal Epidemiology Centers (TECs) serve American Indian and Alaska Native (AIAN) communities across multistate regions, often encountering significant barriers to data access and utilization. This manuscript explores how TECs access and use syndromic surveillance data to address health disparities in AIAN populations, highlighting successes, innovations, and ongoing challenges. The Alaska Native Epidemiology Center (ANEC), Great Plains Tribal Epidemiology Center (GPTEC), and Northwest Tribal Epidemiology Center (NWTEC) provide insights into their syndromic surveillance practices. This includes data access methods, the creation of dashboards and reports, technical assistance for Tribal Health Organizations (THOs), and strategies for overcoming jurisdictional and data-sharing barriers. TECs have successfully leveraged syndromic surveillance to monitor critical health issues, including respiratory illnesses, substance misuse, behavioral health, and maternal care. Collaborative efforts have addressed race misclassification and data gaps, enabling targeted interventions such as air purifier distribution and improving health care delivery for tribal veterans. However, TECs can face restrictive data use agreements, jurisdictional misalignments, and limited access to granular data, hindering their ability to serve AIAN communities comprehensively. Syndromic surveillance offers transformative potential for improving public health in AIAN communities. To fully realize this potential, systemic changes are needed to streamline data-sharing agreements and improve data accuracy. These efforts, along with strong collaborations between TECs and state health departments, are critical to advancing health equity, respecting tribal sovereignty, and ensuring timely, actionable insights for AIAN populations.

The underreporting of laboratory-reported cases of community-based gastrointestinal (GI) infections poses a challenge for epidemiologists understanding the burden and seasonal patterns of GI pathogens. Syndromic surveillance has the potential to overcome the limitations of laboratory reporting through real-time data and more representative population coverage. This systematic review summarizes the utility of syndromic surveillance for early detection and surveillance of GI infections. Relevant articles were identified using the following keyword combinations: ‘early warning’, ‘detection’, ‘gastrointestinal activity’, ‘gastrointestinal infections’, ‘syndrome monitoring’, ‘real-time monitoring’, ‘syndromic surveillance’. In total, 1820 studies were identified, 126 duplicates were removed, and 1694 studies were reviewed. Data extraction focused on studies reporting the routine use and effectiveness of syndromic surveillance for GI infections using relevant GI symptoms. Eligible studies (n = 29) were included in the narrative synthesis. Syndromic surveillance for GI infections has been implemented and validated for routine use in ten countries, with emergency department attendances being the most common source. Evidence suggests that syndromic surveillance can be effective in the early detection and routine monitoring of GI infections; however, 24% of the included studies did not provide conclusive findings. Further investigation is necessary to comprehensively understand the strengths and limitations associated with each type of syndromic surveillance system.