Alex James Elliot

Background: A range of surveillance systems have been employed in England to monitor respiratory illness, including participatory, syndromic and laboratory datasets. Combined, they can provide powerful insights on the spectrum of respiratory illness across the population. We compared trends across these datasets over three successive winters. Methods: Participatory, syndromic and laboratory surveillance datasets from the UK Health Security Agency were integrated. Winter trends in influenza‐like illness (ILI), using a standardised definition, were described and compared across datasets, 2022/2023 to 2024/2025, using the cross‐correlation function (CCF). Multivariable (beta and negative binomial) regression models were used to explore the association between ILI and SARS‐CoV‐2, influenza and RSV positivity in each dataset. Results: Trends in ILI align well across participatory surveillance systems, with ILI peaking around Weeks 50–52 each season. Participatory surveillance system trends align well with syndromic healthcare use for ILI, with ILI symptoms peaking about a week before healthcare use. ILI in each dataset is driven by a different combination of respiratory viruses, with ILI in the community driven by a combination of SARS‐CoV‐2, influenza and RSV, whereas healthcare attendance was driven by influenza only. Conclusions: While each surveillance system differs in its purpose and population, trends in ILI align across datasets. Comparing a range of surveillance systems can provide increased confidence in each system. While syndromic healthcare use surveillance systems highlight the burden of severe illness, integration with participatory surveillance systems can demonstrate the wider burden across a spectrum of severity.

Background: Severe COVID-19 is associated with immune dysregulation, microbiome disruption, and increased vulnerability to secondary infections. However, the longer-term risk of acute gastroenteritis (AGE) following COVID-19 remains poorly studied. We compared AGE incidence among community-managed and hospitalised COVID-19 patients with test-negative controls and hospitalised sepsis patients. Methods: We conducted a retrospective cohort study using electronic healthcare data. Patients with a recorded SARS-CoV-2 PCR test between 01 March 2020 and 01 March 2022 were allocated to test-negative, community COVID-19, or hospitalised COVID-19 cohorts. A hospitalised sepsis cohort was assembled for an age-, sex- and time-matched analysis. AGE incidence rates were calculated for the two years before enrolment and during follow-up. Negative binomial regression and conditional Poisson models estimated incidence rate ratios (IRRs) adjusting for demographic and clinical covariates. Findings: Among 788 103 patients, AGE incidence in the pre-pandemic period was higher among individuals later hospitalised with COVID-19 or sepsis compared with community cases. During follow-up, AGE incidence was slightly lower in community COVID-19 cases than test-negative controls (adjusted IRR 0·88 [95% CI 0·85–0·92]), but substantially higher among hospitalised COVID-19 patients (IRR 5·80 [95% CI: 5·38–6·25]). Matched analyses confirmed increased AGE rates compared with community controls (IRR 4·63 [95% CI: 4·15–5·17]) and hospitalised sepsis controls (IRR 1·41 [95% CI 1·21–1·64]). Interpretation: COVID-19 hospitalisation is associated with a marked increase in AGE risk after discharge, exceeding that associated with sepsis hospitalisation. By contrast, lower AGE rates in community COVID-19 cases likely reflect baseline differences rather than protective effects of infection. Overall, these findings demonstrate heightened post-discharge vulnerability to gastrointestinal illness after COVID-19 hospitalisation and support the need for improved follow-up and surveillance strategies.

We evaluated aggregated, privacy-preserving smartphone-detected nocturnal cough activity from the Sleep Cycle application as a potential syndromic surveillance signal in England. Weekly cough metrics from January 2023 to January 2026 were compared with UK Health Security Agency indicators: NHS 111 acute respiratory infection (ARI) triage calls, influenza and COVID-19 PCR positivity, and hospital admission rates for influenza, COVID-19, and respiratory syncytial virus. We evaluated total cough counts alongside two population-normalised metrics, coughs per user and coughs per hour of sleep, and assessed temporal relationships nationally and regionally using cross-correlation with prewhitening. The strongest and most consistent associations were observed for NHS 111 ARI triage calls, where population-normalised cough metrics showed raw national correlations of approximately 0.95 and retained prewhitened correlations above 0.55 at lag 0. This indicates that nocturnal cough activity closely tracks short-term variation in an established syndromic surveillance indicator, beyond shared seasonality, long-term trends, and autocorrelation. Similar near-contemporaneous patterns were observed across regions. Population-normalised cough metrics also showed epidemiologically plausible leading associations with pathogen-specific indicators: coughs per hour of sleep peaked one week before influenza PCR positivity, while both coughs per user and coughs per hour of sleep peaked one week before COVID-19 PCR positivity. Hospital-based indicators showed weaker and more heterogeneous relationships, but the normalised cough metrics still showed plausible temporal alignment with influenza and COVID-19 admissions, including contemporaneous associations with influenza admissions and short leading associations with COVID-19 admissions. In contrast, unnormalised total cough counts produced less stable and often non-interpretable lag structures, consistent with sensitivity to variation in observation volume. These findings suggest that passive, near-real-time nocturnal cough monitoring can provide a population-level signal of respiratory symptom burden, with greatest utility as a broad syndromic indicator that complements surveillance sources affected by healthcare-seeking behaviour, laboratory turnaround times, backfilling, and reporting delays.

Background: Case definitions are essential for effectively communicating public health threats. However, the absence of a standardized, machine-readable format poses significant challenges to interoperability, epidemiological research, data sharing, and the application of computational methods, including artificial intelligence. These barriers complicate collaboration across regions and organizations and hinder technological progress in public health. Objective: This study aims to propose and release the first open, machine-readable format for representing case and syndrome definitions, together with tools and resources that enable their standardized and scalable use. Methods: We developed the Open Syndrome Definition, a structured, machine-readable schema for representing case and syndrome definitions. We compiled official public health case definitions from multiple institutions and converted them into standardized, machine-readable representations using open-source tools. These tools, available through GitHub under the Massachusetts Institute of Technology license, automate the translation of narrative definitions into structured data. We also created a platform for browsing, analyzing, and contributing new definitions on our initiative website. Results: The Open Syndrome Definition format enabled consistent, automated representation of case definitions across different diseases and jurisdictions. The conversion tools achieved high semantic fidelity, as assessed by qualitative expert review, between narrative and structured representations, supporting human verification and automated analysis. The dataset and accompanying tools demonstrated structural and semantic interoperability by standardizing definitions from various health systems into a unified format and integrating existing medical ontologies through JSON for Linked Data. To further illustrate practical applicability and downstream usage, we introduced a data filtering prototype that allows users to upload their own datasets and verify the results against the standardized definitions. Conclusions: The Open Syndrome Definition establishes a foundation for consistent and machine-readable public health definitions, facilitating reproducible research and interoperability at scale. By enabling systematic data exchange and artificial intelligence–driven analysis, it strengthens public health preparedness and supports more rapid, coordinated responses to emerging health threats.

Background: There is a significant hidden burden of infectious intestinal disease (IID) in the UK community, which has increased over time. In the late 2000s, the Second Study of Infectious Intestinal Disease (IID2 study) estimated 17 million IID cases annually in the United Kingdom. However, only a small proportion of cases present to health care, and even those are often not tested for causative organisms. Objective: The Third Study of Infectious Intestinal Disease (IID3 study) aims to determine the IID burden in the UK community, estimate the underreporting level in routine practice and the general population, and recalibrate UK national surveillance based on the new incidence rates. Methods: We will follow methods of previous studies, along with modern pathogen detection methods and digital platforms for recruitment and follow-up. Participants will be recruited to three population-based prospective cohorts: cohort 1 (the general population), cohort 2 (patients with IID presenting to general practices [GPs]), and cohort 3 (enumeration study of IID cases presenting to GPs). Microbiological analysis of stool samples in cohorts 1 and 2 will include testing for a wide range of causative organisms using molecular assays, including pathogen targets not routinely sought by National Health Service (NHS) laboratories. Additional characterization of pathogens will be conducted at national reference laboratories. The incidence rates of IID and organisms detected in cohorts 1-3 will be compared to national surveillance systems, both laboratory and syndromic. Descriptive statistics and analysis will allow comparison of IID rates within each cohort, estimate the overall burden of disease caused by different pathogens, and compare findings to earlier IID studies. Results: A favorable ethical opinion was obtained from the UK Health Research Authority on August 4, 2022. A pilot phase to test the sampling process was conducted from January to August 2023. Participant recruitment commenced on September 1, 2023, for cohort 2 and on March 16, 2024, for cohort 1; recruitment ceased on August 31, 2025. Data collection is complete, and data analysis is to begin. The study is expected to end in September 2026. Conclusions: Since the first and second IID studies, changes have occurred within national surveillance systems, the NHS structure, and public recommendations about when to consult a GP and where to seek health care advice, which may have altered the extent of IID reporting and the perceived burden in the community, creating greater uncertainty about the representativeness of IID rates. The IID3 study results will provide insight into trends in disease incidence over time and help quantify inequalities in IID in the UK community. Revised estimates can inform policy related to prevention, including food standards and disease management. Furthermore, advances in molecular diagnostics will significantly enhance pathogen detection, increasing our understanding of the causes of IID. International Registered Report Identifier (IRRID): DERR1-10.2196/88759

BACKGROUND There is a significant hidden burden of infectious intestinal disease (IID) in the UK community, which has increased over time. In the late 2000s, the Second Study of Infectious Intestinal Disease (IID2 study) estimated 17 million IID cases annually in the United Kingdom. However, only a small proportion of cases present to health care, and even those are often not tested for causative organisms. OBJECTIVE The Third Study of Infectious Intestinal Disease (IID3 study) aims to determine the IID burden in the UK community, estimate the underreporting level in routine practice and the general population, and recalibrate UK national surveillance based on the new incidence rates. METHODS We will follow methods of previous studies, along with modern pathogen detection methods and digital platforms for recruitment and follow-up. Participants will be recruited to three population-based prospective cohorts: cohort 1 (the general population), cohort 2 (patients with IID presenting to general practices [GPs]), and cohort 3 (enumeration study of IID cases presenting to GPs). Microbiological analysis of stool samples in cohorts 1 and 2 will include testing for a wide range of causative organisms using molecular assays, including pathogen targets not routinely sought by National Health Service (NHS) laboratories. Additional characterization of pathogens will be conducted at national reference laboratories. The incidence rates of IID and organisms detected in cohorts 1-3 will be compared to national surveillance systems, both laboratory and syndromic. Descriptive statistics and analysis will allow comparison of IID rates within each cohort, estimate the overall burden of disease caused by different pathogens, and compare findings to earlier IID studies. RESULTS A favorable ethical opinion was obtained from the UK Health Research Authority on August 4, 2022. A pilot phase to test the sampling process was conducted from January to August 2023. Participant recruitment commenced on September 1, 2023, for cohort 2 and on March 16, 2024, for cohort 1; recruitment ceased on August 31, 2025. Data collection is complete, and data analysis is to begin. The study is expected to end in September 2026. CONCLUSIONS Since the first and second IID studies, changes have occurred within national surveillance systems, the NHS structure, and public recommendations about when to consult a GP and where to seek health care advice, which may have altered the extent of IID reporting and the perceived burden in the community, creating greater uncertainty about the representativeness of IID rates. The IID3 study results will provide insight into trends in disease incidence over time and help quantify inequalities in IID in the UK community. Revised estimates can inform policy related to prevention, including food standards and disease management. Furthermore, advances in molecular diagnostics will significantly enhance pathogen detection, increasing our understanding of the causes of IID. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/88759

Asthma epidemics linked to thunderstorms (thunderstorm asthma; TSA) have been reported internationally for several decades. TSA can have severe impact on an individuals' health, resulting in severe outcomes, including death. TSA events can also put intense pressures on local health care services. To describe changes in healthcare seeking behaviour associated with a period of thunderstorm activity in England, June 2023, using routinely available health surveillance data. Asthma and difficulty breathing-type presentations were analysed from telehealth advice calls and emergency department attendances. This was the largest TSA event documented in England since the English syndromic surveillance programme was established over 25 years ago. The scale of the increase in asthma-type activity was above previously reported TSA events in England and activity coincided with forecasted storm alerts, both temporally and geographically. However, increases in asthma-type patient contacts were only observed around storm alerts occurring during periods of high pollen activity. Additionally, increases in asthma-type patient contacts during the TSA episode were not observed in young children (under 5 years) or older adults (65 years and over). This work supports the evidence of the health impact of TSA in England. It also particularly adds to the evidence base that pollen is a leading causal factor in TSA episodes in England. Further investigations into the spatiotemporal relationships between TSA, storm and pollen activity would help in the identification of drivers affecting different age groups, as well as to inform messaging to both healthcare professionals and the public regarding the timing of events and mitigation steps that may be taken.

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.

Background: During 2022, a new public health threat emerged when cases of paediatric acute hepatitis of unknown aetiology (HUA) were identified in children aged under 16 years old in the United Kingdom (UK). At the time, the epidemiology and extent of cases was based upon limited and non-standardised reporting from hospitals and liver units. We aimed to adapt existing real-time syndromic surveillance systems to support the epidemiological investigation of cases of HUA presenting to emergency departments (EDs) in England. Methods: Syndromic surveillance is generally based on the collection of patient symptoms or chief complaints, which are collected using automated routines in near real-time. Here, we used an existing ED syndromic surveillance system monitoring daily patient attendances across a network of approximately 150 EDs in England. Clinical diagnosis codes related to the potential symptoms associated with the HUA incident were selected and attendance data monitored retrospectively and prospectively during the incident. Results: From 2 April 2018 to 31 December 2021, there were small sporadic numbers of daily ED attendances for ‘liver conditions’ in children with no observed secular trends or seasonality across the 1 to 4 and 5 to 14 years age groups. The period 2 April to 29 July was compared across each year included in the analysis. Mean daily HUA attendances during 2018 to 2021 was 0.05 and 0.22 for 1 to 4 and 5 to 14 years respectively, however in 2022 there were 0.26 and 0.42 mean daily attendances. This represented an increase of 377% and 94% in the 1 to 4 and 5 to 14 years age groups, respectively. From June 2022, daily syndromic ‘liver condition’ attendances appeared to decrease and the rate of increase in cumulative attendances slowed. Conclusions: We demonstrate how syndromic surveillance provided support to the HUA outbreak using an existing syndromic surveillance framework to develop new indicators based on the newly emerging clinical symptoms. The outputs from the syndromic tool matched clinical and epidemiological findings with respect to trends in other HUA-related data, including clinical and laboratory reports, over time. This work demonstrates the potential for syndromic surveillance supporting the epidemiological surveillance of hepatitis and providing a valuable tool for the real-time management of future unknown health threats.