Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends.

Introduction: Large-scale diagnostic testing has been proven insufficient to promptly monitor the spread of the Coronavirus disease 2019. Electronic resources may provide better insight into the early detection of epidemics. We aimed to retrospectively explore whether the Google search volume has been useful in detecting Severe Acute Respiratory Syndrome Coronavirus outbreaks early compared to the swab-based surveillance system. Methods: The Google Trends website was used by applying the research to three Italian regions (Lombardy, Marche, and Sicily), covering 16 million Italian citizens. An autoregressive-moving-average model was fitted, and residual charts were plotted to detect outliers in weekly searches of five keywords. Signals that occurred during periods labelled as free from epidemics were used to measure Positive Predictive Values and False Negative Rates in anticipating the epidemic wave occurrence. Results: Signals from "fever," "cough," and "sore throat" showed better performance than those from "loss of smell" and "loss of taste." More than 80% of true epidemic waves were detected early by the occurrence of at least an outlier signal in Lombardy, although this implies a 20% false alarm signals. Performance was poorer for Sicily and Marche. Conclusion: Monitoring the volume of Google searches can be a valuable tool for early detection of respiratory infectious disease outbreaks, particularly in areas with high access to home internet. The inclusion of web-based syndromic keywords is promising as it could facilitate the containment of COVID-19 and perhaps other unknown infectious diseases in the future.

Establishing severe acute respiratory infection (SARI) surveillance in a sentinel hospital, Ireland, 2021 to 2022.

BackgroundIn 2020, due to the COVID-19 pandemic, the European Centre for Disease Prevention and Control (ECDC) accelerated development of European-level severe acute respiratory infection (SARI) surveillance.AimWe aimed to establish SARI surveillance in one Irish hospital as part of a European network E-SARI-NET.MethodsWe used routine emergency department records to identify cases in one adult acute hospital. The SARI case definition was adapted from the ECDC clinical criteria for a possible COVID-19 case. Clinical data were collected using an online questionnaire. Cases were tested for SARS-CoV-2, influenza and respiratory syncytial virus (RSV), including whole genome sequencing (WGS) on SARS-CoV-2 RNA-positive samples and viral characterisation/sequencing on influenza RNA-positive samples. Descriptive analysis was conducted for SARI cases hospitalised between July 2021 and April 2022.ResultsOverall, we identified 437 SARI cases, the incidence ranged from two to 28 cases per week (0.7-9.2/100,000 hospital catchment population). Of 431 cases tested for SARS-CoV-2 RNA, 226 (52%) were positive. Of 349 (80%) cases tested for influenza and RSV RNA, 15 (4.3%) were positive for influenza and eight (2.3%) for RSV. Using WGS, we identified Delta- and Omicron-dominant periods. The resource-intensive nature of manual clinical data collection, specimen management and laboratory supply shortages for influenza and RSV testing were challenging.ConclusionWe successfully established SARI surveillance as part of E-SARI-NET. Expansion to additional sentinel sites is planned following formal evaluation of the existing system. SARI surveillance requires multidisciplinary collaboration, automated data collection where possible, and dedicated personnel resources, including for specimen management.

Changes in emergency department utilisation in Germany before and during different phases of the COVID-19 pandemic, using data from a national surveillance system up to June 2021.

BACKGROUND: During the COVID-19 pandemic and associated public health and social measures, decreasing patient numbers have been described in various healthcare settings in Germany, including emergency care. This could be explained by changes in disease burden, e.g. due to contact restrictions, but could also be a result of changes in utilisation behaviour of the population. To better understand those dynamics, we analysed routine data from emergency departments to quantify changes in consultation numbers, age distribution, disease acuity and day and hour of the day during different phases of the COVID-19 pandemic. METHODS: We used interrupted time series analyses to estimate relative changes for consultation numbers of 20 emergency departments spread throughout Germany. For the pandemic period (16-03-2020 - 13-06-2021) four different phases of the COVID-19 pandemic were defined as interruption points, the pre-pandemic period (06-03-2017 - 09-03-2020) was used as the reference. RESULTS: The most pronounced decreases were visible in the first and second wave of the pandemic, with changes of - 30.0% (95%CI: - 32.2%; - 27.7%) and - 25.7% (95%CI: - 27.4%; - 23.9%) for overall consultations, respectively. The decrease was even stronger for the age group of 0-19 years, with - 39.4% in the first and - 35.0% in the second wave. Regarding acuity levels, consultations assessed as urgent, standard, and non-urgent showed the largest decrease, while the most severe cases showed the smallest decrease. CONCLUSIONS: The number of emergency department consultations decreased rapidly during the COVID-19 pandemic, without extensive variation in the distribution of patient characteristics. Smallest changes were observed for the most severe consultations and older age groups, which is especially reassuring regarding concerns of possible long-term complications due to patients avoiding urgent emergency care during the pandemic.

Views on increased federal access to state and local National Syndromic Surveillance Program data: a nominal group technique study with state and local epidemiologists.

BACKGROUND: US public health authorities use syndromic surveillance to monitor and detect public health threats, conditions, and trends in near real-time. Nearly all US jurisdictions that conduct syndromic surveillance send their data to the National Syndromic Surveillance Program (NSSP), operated by the US. Centers for Disease Control and Prevention. However, current data sharing agreements limit federal access to state and local NSSP data to only multi-state regional aggregations. This limitation was a significant challenge for the national response to COVID-19. This study seeks to understand state and local epidemiologists' views on increased federal access to state NSSP data and identify policy opportunities for public health data modernization. METHODS: In September 2021, we used a virtual, modified nominal group technique with twenty regionally diverse epidemiologists in leadership positions and three individuals representing national public health organizations. Participants individually generated ideas on benefits, concerns, and policy opportunities relating to increased federal access to state and local NSSP data. In small groups, participants clarified and grouped the ideas into broader themes with the assistance of the research team. An web-based survey was used to evaluate and rank the themes using five-point Likert importance questions, top-3 ranking questions, and open-ended response questions. RESULTS: Participants identified five benefit themes for increased federal access to jurisdictional NSSP data, with the most important being improved cross-jurisdiction collaboration (mean Likert = 4.53) and surveillance practice (4.07). Participants identified nine concern themes, with the most important concerns being federal actors using jurisdictional data without notice (4.60) and misinterpretation of data (4.53). Participants identified eleven policy opportunities, with the most important being involving state and local partners in analysis (4.93) and developing communication protocols (4.53). CONCLUSION: These findings identify barriers and opportunities to federal-state-local collaboration critical to current data modernization efforts. Syndromic surveillance considerations warrant data-sharing caution. However, identified policy opportunities share congruence with existing legal agreements, suggesting that syndromic partners are closer to agreement than they might realize. Moreover, several policy opportunities (i.e., including state and local partners in data analysis and developing communication protocols) received consensus support and provide a promising path forward.

Representativeness, Vaccination Uptake, and COVID-19 Clinical Outcomes 2020-2021 in the UK Oxford-Royal College of General Practitioners Research and Surveillance Network: Cohort Profile Summary.

BACKGROUND: The Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) is one of Europe's oldest sentinel systems, working with the UK Health Security Agency (UKHSA) and its predecessor bodies for 55 years. Its surveillance report now runs twice weekly, supplemented by online observatories. In addition to conducting sentinel surveillance from a nationally representative group of practices, the RSC is now also providing data for syndromic surveillance. OBJECTIVE: The aim of this study was to describe the cohort profile at the start of the 2021-2022 surveillance season and recent changes to our surveillance practice. METHODS: The RSC's pseudonymized primary care data, linked to hospital and other data, are held in the Oxford-RCGP Clinical Informatics Digital Hub, a Trusted Research Environment. We describe the RSC's cohort profile as of September 2021, divided into a Primary Care Sentinel Cohort (PCSC)-collecting virological and serological specimens-and a larger group of syndromic surveillance general practices (SSGPs). We report changes to our sampling strategy that brings the RSC into alignment with European Centre for Disease Control guidance and then compare our cohort's sociodemographic characteristics with Office for National Statistics data. We further describe influenza and COVID-19 vaccine coverage for the 2020-2021 season (week 40 of 2020 to week 39 of 2021), with the latter differentiated by vaccine brand. Finally, we report COVID-19-related outcomes in terms of hospitalization, intensive care unit (ICU) admission, and death. RESULTS: As a response to COVID-19, the RSC grew from just over 500 PCSC practices in 2019 to 1879 practices in 2021 (PCSC, n=938; SSGP, n=1203). This represents 28.6% of English general practices and 30.59% (17,299,780/56,550,136) of the population. In the reporting period, the PCSC collected >8000 virology and >23,000 serology samples. The RSC population was broadly representative of the national population in terms of age, gender, ethnicity, National Health Service Region, socioeconomic status, obesity, and smoking habit. The RSC captured vaccine coverage data for influenza (n=5.4 million) and COVID-19, reporting dose one (n=11.9 million), two (n=11 million), and three (n=0.4 million) for the latter as well as brand-specific uptake data (AstraZeneca vaccine, n=11.6 million; Pfizer, n=10.8 million; and Moderna, n=0.7 million). The median (IQR) number of COVID-19 hospitalizations and ICU admissions was 1181 (559-1559) and 115 (50-174) per week, respectively. CONCLUSIONS: The RSC is broadly representative of the national population; its PCSC is geographically representative and its SSGPs are newly supporting UKHSA syndromic surveillance efforts. The network captures vaccine coverage and has expanded from reporting primary care attendances to providing data on onward hospital outcomes and deaths. The challenge remains to increase virological and serological sampling to monitor the effectiveness and waning of all vaccines available in a timely manner.

Detecting early signals of COVID-19 outbreaks in 2020 in small areas by monitoring healthcare utilisation databases: first lessons learned from the Italian Alert_CoV project.

BackgroundDuring the COVID-19 pandemic, large-scale diagnostic testing and contact tracing have proven insufficient to promptly monitor the spread of infections.AimTo develop and retrospectively evaluate a system identifying aberrations in the use of selected healthcare services to timely detect COVID-19 outbreaks in small areas.MethodsData were retrieved from the healthcare utilisation (HCU) databases of the Lombardy Region, Italy. We identified eight services suggesting a respiratory infection (syndromic proxies). Count time series reporting the weekly occurrence of each proxy from 2015 to 2020 were generated considering small administrative areas (i.e. census units of Cremona and Mantua provinces). The ability to uncover aberrations during 2020 was tested for two algorithms: the improved Farrington algorithm and the generalised likelihood ratio-based procedure for negative binomial counts. To evaluate these algorithms' performance in detecting outbreaks earlier than the standard surveillance, confirmed outbreaks, defined according to the weekly number of confirmed COVID-19 cases, were used as reference. Performances were assessed separately for the first and second semester of the year. Proxies positively impacting performance were identified.ResultsWe estimated that 70% of outbreaks could be detected early using the proposed approach, with a corresponding false positive rate of ca 20%. Performance did not substantially differ either between algorithms or semesters. The best proxies included emergency calls for respiratory or infectious disease causes and emergency room visits.ConclusionImplementing HCU-based monitoring systems in small areas deserves further investigations as it could facilitate the containment of COVID-19 and other unknown infectious diseases in the future.

[Diagnostic protocol for febrile syndrome of respiratory origin in geographical areas of endemic risk of tropical infections]. / Protocolo diagnóstico del síndrome febril con focalidad respiratoria en áreas geográficas de riesgo endémico de infecciones tropicales.

The syndromic surveillance of a group of diseases that have similar signs and symptoms, a common pathophysiology, and diverse etiology is aimed at rapidly detecting the presence of outbreaks which could potentially harm public health. This includes not only known outbreaks of infectious origin but also those of unknown origin. In patients suspected of having SARS-CoV-2/COVID-19, it is recommended to consider other etiologies of tropical fever in the differential diagnosis when these patients live in or come from endemic areas, as is the case of dengue, malaria, leptospirosis, acute Chagas disease, and rickettsiosis, among other endemic diseases. The possibility of SARS-CoV-2/AH1 AH5N1 MERS-CoV coinfection with these pathogens should also be considered.

Participatory disease surveillance for a mass gathering - a prospective cohort study on COVID-19, Germany 2021.

BACKGROUND: Mass gatherings (MGs) such as music festivals and sports events have been associated with a high risk of SARS-CoV-2 transmission. On-site research can foster knowledge of risk factors for infections and improve risk assessments and precautionary measures at future MGs. We tested a web-based participatory disease surveillance tool to detect COVID-19 infections at and after an outdoor MG by collecting self-reported COVID-19 symptoms and tests. METHODS: We conducted a digital prospective observational cohort study among fully immunized attendees of a sports festival that took place from September 2 to 5, 2021 in Saxony-Anhalt, Germany. Participants used our study app to report demographic data, COVID-19 tests, symptoms, and their contact behavior. This self-reported data was used to define probable and confirmed COVID-19 cases for the full "study period" (08/12/2021 - 10/31/2021) and within the 14-day "surveillance period" during and after the MG, with the highest likelihood of an MG-related COVID-19 outbreak (09/04/2021 - 09/17/2021). RESULTS: A total of 2,808 of 9,242 (30.4%) event attendees participated in the study. Within the study period, 776 individual symptoms and 5,255 COVID-19 tests were reported. During the 14-day surveillance period around and after the MG, seven probable and seven PCR-confirmed COVID-19 cases were detected. The confirmed cases translated to an estimated seven-day incidence of 125 per 100,000 participants (95% CI [67.7/100,000, 223/100,000]), which was comparable to the average age-matched incidence in Germany during this time. Overall, weekly numbers of COVID-19 cases were fluctuating over the study period, with another increase at the end of the study period. CONCLUSION: COVID-19 cases attributable to the mass gathering were comparable to the Germany-wide age-matched incidence, implicating that our active participatory disease surveillance tool was able to detect MG-related infections. Further studies are needed to evaluate and apply our participatory disease surveillance tool in other mass gathering settings.

Protocol to implement a syndromic surveillance survey of COVID-19 in Malawi.

Introduction: Malawi experienced two waves of COVID-19 between April 2020 and February 2021. A High negative impact of COVID-19 was experienced in the second wave, with increased hospital admissions that overwhelmed the healthcare system. This paper describes a protocol to implement a telephone-based syndromic surveillance system to assist public health leaders in the guidance, implementation, and evaluation of programs and policies for COVID-19 prevention and control in Malawi. Study design: This is a serial cross-sectional telephonic-based national survey focusing on the general population and People living with HIV and AIDS. Methods: We will conduct a serial cross-sectional telephone survey to assess self-reported recent and current experience of influenza-like illness (ILI)/COVID-19-like-illness (CLI), household deaths, access to routine health services, and knowledge related to COVID-19. Structured questionnaires will be administered to two populations: 1) the general population and 2) people living with HIV (PLHIV) on antiretroviral therapy (ART) at EGPAF-supported health facilities. Electronic data collection forms using secure tablets will be used based on randomly selected mobile numbers from electronic medical records (EMR) for PLHIV. We will use random digit dialing (RDD) for the general population to generate phone numbers to dial respondents. The technique uses computer-generated random numbers, using the 10-digit basic structure of mobile phone numbers for the two existing mobile phone companies in Malawi. Interviews will be conducted only with respondents that will verbally consent. A near real-time online dashboard will be developed to help visualize the data and share results with key policymakers. Conclusion: The designed syndromic surveillance system is low-cost and feasible to implement under COVID-19 restrictions, with no physical contact with respondents and limited movement of the study teams and communities. The system will allow estimation proportions of those reporting ILI/CLI among the general population and PLHIV on ART and monitor trends over time to detect locations with possible COVID-19 transmission. Reported household deaths in Malawi, access to health services, and COVID-19 knowledge will be monitored to assess the burden and impact on communities in Malawi.

Efficient algorithms for real-time syndromic surveillance.

OBJECTIVE: Outbreaks of influenza-like diseases often cause spikes in the demand for hospital beds. Early detection of these outbreaks can enable improved management of hospital resources. The objective of this study was to test whether surveillance algorithms designed to be responsive to a wide range of anomalous decreases in the time between emergency department (ED) presentations with influenza-like illnesses provide efficient early detection of these outbreaks. METHODS: Our study used data on ED presentations to major public hospitals in Queensland, Australia across 2017-2020. We developed surveillance algorithms for each hospital that flag potential outbreaks when the average time between successive ED presentations with influenza-like illnesses becomes anomalously small. We designed one set of algorithms to be responsive to a wide range of anomalous decreases in the time between presentations. These algorithms concurrently monitor three exponentially weighted moving averages (EWMAs) of the time between presentations and flag an outbreak when at least one EWMA falls below its control limit. We designed another set of algorithms to be highly responsive to narrower ranges of anomalous decreases in the time between presentations. These algorithms monitor one EWMA of the time between presentations and flag an outbreak when the EWMA falls below its control limit. Our algorithms use dynamic control limits to reflect that the average time between presentations depends on the time of year, time of day, and day of the week. RESULTS: We compared the performance of the algorithms in detecting the start of two epidemic events at the hospital-level: the 2019 seasonal influenza outbreak and the early-2020 COVID-19 outbreak. The algorithm that concurrently monitors three EWMAs provided significantly earlier detection of these outbreaks than the algorithms that monitor one EWMA. CONCLUSION: Surveillance algorithms designed to be responsive to a wide range of anomalous decreases in the time between ED presentations are highly efficient at detecting outbreaks of influenza-like diseases at the hospital level.

Early Detection of SARS-CoV-2 Epidemic Waves: Lessons from the Syndromic Surveillance in Lombardy, Italy.

We evaluated the performance of the exponentially weighted moving average (EWMA) model for comparing two families of predictors (i.e., structured and unstructured data from visits to the emergency department (ED)) for the early detection of SARS-CoV-2 epidemic waves. The study included data from 1,282,100 ED visits between 1 January 2011 and 9 December 2021 to a local health unit in Lombardy, Italy. A regression model with an autoregressive integrated moving average (ARIMA) error term was fitted. EWMA residual charts were then plotted to detect outliers in the frequency of the daily ED visits made due to the presence of a respiratory syndrome (based on coded diagnoses) or respiratory symptoms (based on free text data). Alarm signals were compared with the number of confirmed SARS-CoV-2 infections. Overall, 150,300 ED visits were encoded as relating to respiratory syndromes and 87,696 to respiratory symptoms. Four strong alarm signals were detected in March and November 2020 and 2021, coinciding with the onset of the pandemic waves. Alarm signals generated for the respiratory symptoms preceded the occurrence of the first and last pandemic waves. We concluded that the EWMA model is a promising tool for predicting pandemic wave onset.

Citywide wastewater SARS-CoV-2 levels strongly correlated with multiple disease surveillance indicators and outcomes over three COVID-19 waves.

Public health surveillance systems for COVID-19 are multifaceted and include multiple indicators reflective of different aspects of the burden and spread of the disease in a community. With the emergence of wastewater disease surveillance as a powerful tool to track infection dynamics of SARS-CoV-2, there is a need to integrate and validate wastewater information with existing disease surveillance systems and demonstrate how it can be used as a routine surveillance tool. A first step toward integration is showing how it relates to other disease surveillance indicators and outcomes, such as case positivity rates, syndromic surveillance data, and hospital bed use rates. Here, we present an 86-week long surveillance study that covers three major COVID-19 surges. City-wide SARS-CoV-2 RNA viral loads in wastewater were measured across 39 wastewater treatment plants and compared to other disease metrics for the city of Houston, TX. We show that wastewater levels are strongly correlated with positivity rate, syndromic surveillance rates of COVID-19 visits, and COVID-19-related general bed use rates at hospitals. We show that the relative timing of wastewater relative to each indicator shifted across the pandemic, likely due to a multitude of factors including testing availability, health-seeking behavior, and changes in viral variants. Next, we show that individual WWTPs led city-wide changes in SARS-CoV-2 viral loads, indicating a distributed monitoring system could be used to enhance the early-warning capability of a wastewater monitoring system. Finally, we describe how the results were used in real-time to inform public health response and resource allocation.

Detection of Infectious Disease Outbreaks in Search Engine Time Series Using Non-Specific Syndromic Surveillance with Effect-Size Filtering

Novel infectious disease outbreaks, including most recently that of the COVID-19 pandemic, could be detected by non-specific syndromic surveillance systems. Such systems, utilizing a variety of data sources ranging from Electronic Health Records to internet data such as aggregated search engine queries, create alerts when unusually high rates of symptom reports occur. This is especially important for the detection of novel diseases, where their manifested symptoms are unknown. Here we improve upon a set of previously-proposed non-specific syndromic surveillance methods by taking into account both how unusual a preponderance of symptoms is and their effect size. We demonstrate that our method is as accurate as previously-proposed methods for low dimensional data and show its effectiveness for high-dimensional aggregated data by applying it to aggregated time-series health-related search engine queries. We find that in 2019 the method would have raised alerts related to several disease outbreaks earlier than health authorities did. During the COVID-19 pandemic the system identified the beginning of pandemic waves quickly, through combinations of symptoms which varied from wave to wave. Thus, the proposed method could be used as a practical tool for decision makers to detect new disease outbreaks using time series derived from search engine data even in the absence of specific information on the diseases of interest and their symptoms. © 2022 ACM.

Calculating incidence of Influenza-like and COVID-like symptoms from Flutracking participatory survey data.

This article describes a new method for estimating weekly incidence (new onset) of symptoms consistent with Influenza and COVID-19, using data from the Flutracking survey. The method mitigates some of the known self-selection and symptom-reporting biases present in existing approaches to this type of participatory longitudinal survey data. The key novel steps in the analysis are: 1) Identifying new onset of symptoms for three different Symptom Groupings: COVID-like illness (CLI1+, CLI2+), and Influenza-like illness (ILI), for responses reported in the Flutracking survey. 2) Adjusting for symptom reporting bias by restricting the analysis to a sub-set of responses from those participants who have consistently responded for a number of weeks prior to the analysis week. 3) Weighting responses by age to adjust for self-selection bias in order to account for the under- and over-representation of different age groups amongst the survey participants. This uses the survey package [22] in R [30]. 4) Constructing 95% point-wise confidence bands for incidence estimates using weighted logistic regression from the survey package [21] in R [28]. In addition to describing these steps, the article demonstrates an application of this method to Flutracking data for the 12 months from 27th April 2020 until 25th April 2021.

Mental Health Presentations Across Health Care Settings During the First 9 Months of the COVID-19 Pandemic in England: Retrospective Observational Study.

BACKGROUND: The COVID-19 pandemic has resulted in an unprecedented impact on the day-to-day lives of people, with several features potentially adversely affecting mental health. There is growing evidence of the size of the impact of COVID-19 on mental health, but much of this is from ongoing population surveys using validated mental health scores. OBJECTIVE: This study investigated the impact of the pandemic and control measures on mental health conditions presenting to a spectrum of national health care services monitored using real-time syndromic surveillance in England. METHODS: We conducted a retrospective observational descriptive study of mental health presentations (those calling the national medical helpline, National Health Service [NHS] 111; consulting general practitioners [GPs] in and out-of-hours; calling ambulance services; and attending emergency departments) from January 1, 2019, to September 30, 2020. Estimates for the impact of lockdown measures were provided using an interrupted time series analysis. RESULTS: Mental health presentations showed a marked decrease during the early stages of the pandemic. Postlockdown, attendances for mental health conditions reached higher than prepandemic levels across most systems-a rise of 10% compared to that expected for NHS 111 and 21% for GP out-of-hours service-while the number of consultations to GP in-hours service was 13% lower compared to the same time previous year. Increases were observed in calls to NHS 111 for sleep problems. CONCLUSIONS: These analyses showed marked changes in the health care attendances and prescribing for common mental health conditions across a spectrum of health care provision, with some of these changes persisting. The reasons for such changes are likely to be complex and multifactorial. The impact of the pandemic on mental health may not be fully understood for some time, and therefore, these syndromic indicators should continue to be monitored.

Syndromic surveillance of respiratory infections during protracted conflict: experiences from northern Syria 2016-2021.

OBJECTIVE: Northern Syria faces a large burden of influenza-like illness (ILI) and severe acute respiratory illness (SARI). This study aimed to investigate the trends of Early Warning and Response Network (EWARN) reported ILI and SARI in northern Syria between 2016 and 2021 and the potential impact of SARS-CoV-2. METHODS: We extracted weekly EWARN data on ILI/ SARI and aggregated cases and consultations into 4-week intervals to calculate case positivity. We conducted a seasonal-trend decomposition to assess case trends in the presence of seasonal fluctuations. RESULTS: It was observed that 4-week aggregates of ILI cases (n = 5,942,012), SARI cases (n = 114,939), ILI case positivity, and SARI case positivity exhibited seasonal fluctuations with peaks in the winter months. ILI and SARI cases in individuals aged ≥5 years surpassed those in individuals aged <5 years in late 2019. ILI cases clustered primarily in Aleppo and Idlib, whereas SARI cases clustered in Aleppo, Idlib, Deir Ezzor, and Hassakeh. SARI cases increased sharply in 2021, corresponding with a severe SARS-CoV-2 wave, compared with the steady increase in ILI cases over time. CONCLUSION: Respiratory infections cause widespread morbidity and mortality throughout northern Syria, particularly with the emergence of SARS-CoV-2. Strengthened surveillance and access to testing and treatment are critical to manage outbreaks among conflict-affected populations.

Syndromic Surveillance Systems for Mass Gatherings: A Scoping Review.

Syndromic surveillance involves the near-real-time collection of data from a potential multitude of sources to detect outbreaks of disease or adverse health events earlier than traditional forms of public health surveillance. The purpose of the present study is to elucidate the role of syndromic surveillance during mass gathering scenarios. In the present review, the use of syndromic surveillance for mass gathering scenarios is described, including characteristics such as methodologies of data collection and analysis, degree of preparation and collaboration, and the degree to which prior surveillance infrastructure is utilized. Nineteen publications were included for data extraction. The most common data source for the included syndromic surveillance systems was emergency departments, with first aid stations and event-based clinics also present. Data were often collected using custom reporting forms. While syndromic surveillance can potentially serve as a method of informing public health policy regarding specific mass gatherings based on the profile of syndromes ascertained, the present review does not indicate that this form of surveillance is a reliable method of detecting potentially critical public health events during mass gathering scenarios.

The impact of SARS-CoV-2 on respiratory syndromic and sentinel surveillance in Israel, 2020: a new perspective on established systems.

BackgroundThe COVID-19 pandemic presented new challenges for the existing respiratory surveillance systems, and adaptations were implemented. Systematic assessment of the syndromic and sentinel surveillance platforms during the pandemic is essential for understanding the value of each platform in the context of an emerging pathogen with rapid global spread.AimWe aimed to evaluate systematically the performance of various respiratory syndromic surveillance platforms and the sentinel surveillance system in Israel from 1 January to 31 December 2020.MethodsWe compared the 2020 syndromic surveillance trends to those of the previous 3 years, using Poisson regression adjusted for overdispersion. To assess the performance of the sentinel clinic system as compared with the national SARS-CoV-2 repository, a cubic spline with 7 knots and 95% confidence intervals were applied to the sentinel network's weekly percentage of positive SARS-CoV-2 cases.ResultsSyndromic surveillance trends changed substantially during 2020, with a statistically significant reduction in the rates of visits to physicians and emergency departments to below previous years' levels. Morbidity patterns of the syndromic surveillance platforms were inconsistent with the progress of the pandemic, while the sentinel surveillance platform was found to reflect the national circulation of SARS-CoV-2 in the population.ConclusionOur findings reveal the robustness of the sentinel clinics platform for the surveillance of the main respiratory viruses during the pandemic and possibly beyond. The robustness of the sentinel clinics platform during 2020 supports its use in locations with insufficient resources for widespread testing of respiratory viruses.

Machine Learning Identification of Self-reported COVID-19 Symptoms from Tweets in Canada

Surveillance of open-source media, such as social media, has become an essential complement to traditional surveillance data for quickly detecting changes in the occurrence of diseases in time and space. We present our method for classifying Tweets into narratives about COVID-19 symptoms to produce a dataset for downstream surveillance applications. A dataset of 10,405 tweets has been manually classified as relevant or not to self-reported symptoms of COVID-19. Five machine learning classification algorithms, with different tokenization methods, were trained on the dataset and tested. The Support vector machine (SVM) algorithm, with a term frequency-inverse document frequency (TF-IDF) 3-4 n-grams on character as the tokenization method, was the classification algorithm with the highest F1-score of 0.70. However, the training dataset showed an imbalanced classification problem. To reduce the bias of the imbalance classes, the crowdsourcing website Mechanical Turk was used to add 133 relevant tweets. This addition improved the F1-score from 0.70 to 0.77. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Putting digital epidemiology into practice: PIA- Prospective Monitoring and Management Application

Introduction Epidemiological data collection is often challenged by low response and, in the case of cohorts, poor long-term compliance, i.e. a high drop-out. For the correct recording of incident or recurring health events, that are subject to recall difficulties, gathering of data during the event and immediate response of the participants is crucial. This is especially true when biosampling that catches a transient biological situation like COVID-19 is involved. In addition, emerging research topics (e.g. pandemics like the current SARS-CoV-2) demand a flexible approach regarding content while allowing for complex and varying study designs. To meet these needs, we developed an eResearch system for prospective monitoring and management of incident health events (PIA). Methods Programming PIA focusses on IT security and data protection as well as aiming for a user-friendly and motivating design e.g. through feedback for study participants. The main building blocks of the infrastructure are identical functionalities in web-based, iOS and Android compatible application to strengthen the user acceptance of the participants. The backend consists of services and databases, which are all containerised using Docker containers. All programming is based on the JavaScript ecosystem as this is widely used and well supported. Results PIA offers complete management of observational epidemiological studies with six different roles: PIA administrator, researcher, participant manager, study nurse, consent manager and participant. Each role has a specific interface, so that different functions e.g. implementation of new questionnaires, administration of biosamples or management of participant contacts can be performed by different personae. PIA can be integrated in the IT system of ongoing studies like the German National Cohort but also used as stand-alone system. The software is open source (AGPL3.0): https://github.com/hzi-braunschweig/pia-system. Discussion Despite the abundance of existing Electronic Data Capture Systems (EDC systems), we developed our own generic tool that combines monitoring and management in order to use it for specific applications e.g. in certain pre-existing epidemiological studies or for syndromic surveillance in the current pandemic. Hence, PIA is continuously adapted to emerging requirements. Currently, systematic feedback from users is collected. We aim to improve the user experience of PIA as well as provide further feedback and additional elements like gamification in the future.