Comprehensive surveillance of acute respiratory infections during the COVID-19 pandemic: a methodological approach using sentinel networks, Castilla y León, Spain, January 2020 to May 2022.

BackgroundSince 1996, epidemiological surveillance of acute respiratory infections (ARI) in Spain has been limited to seasonal influenza, respiratory syncytial virus (RSV) and potential pandemic viruses. The COVID-19 pandemic provides opportunities to adapt existing systems for extended surveillance to capture a broader range of ARI.AimTo describe how the Influenza Sentinel Surveillance System of Castilla y León, Spain was rapidly adapted in 2020 to comprehensive sentinel surveillance for ARI, including influenza and COVID-19.MethodsUsing principles and methods of the health sentinel network, we integrated electronic medical record data from 68 basic surveillance units, covering 2.6% of the regional population between January 2020 to May 2022. We tested sentinel and non-sentinel samples sent weekly to the laboratory network for SARS-CoV-2, influenza viruses and other respiratory pathogens. The moving epidemic method (MEM) was used to calculate epidemic thresholds.ResultsARI incidence was estimated at 18,942 cases per 100,000 in 2020/21 and 45,223 in 2021/22, with similar seasonal fold increases by type of respiratory disease. Incidence of influenza-like illness was negligible in 2020/21 but a 5-week epidemic was detected by MEM in 2021/22. Epidemic thresholds for ARI and COVID-19 were estimated at 459.4 and 191.3 cases per 100,000 population, respectively. More than 5,000 samples were tested against a panel of respiratory viruses in 2021/22.ConclusionExtracting data from electronic medical records reported by trained professionals, combined with a standardised microbiological information system, is a feasible and useful method to adapt influenza sentinel reports to comprehensive ARI surveillance in the post-COVID-19 era.

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.

COVID-19 impact on EuroTravNet infectious diseases sentinel surveillance in Europe.

BACKGROUND: The COVID-19 pandemic resulted in a sharp decline of post-travel patient encounters at the European sentinel surveillance network (EuroTravNet) of travellers' health. We report on the impact of COVID-19 on travel-related infectious diseases as recorded by EuroTravNet clinics. METHODS: Travelers who presented between January 1, 2019 and September 30, 2021 were included. Comparisons were made between the pre-pandemic period (14 months from January 1, 2019 to February 29, 2020); and the pandemic period (19 months from March 1, 2020 to September 30, 2021). RESULTS: Of the 15,124 visits to the network during the 33-month observation period, 10,941 (72%) were during the pre-pandemic period, and 4183 (28%) during the pandemic period. Average monthly visits declined from 782/month (pre-COVID-19 era) to 220/month (COVID-19 pandemic era). Among non-migrants, the top-10 countries of exposure changed after onset of the COVID-19 pandemic; destinations such as Italy and Austria, where COVID-19 exposure peaked in the first months, replaced typical travel destinations in Asia (Thailand, Indonesia, India). There was a small decline in migrant patients reported, with little change in the top countries of exposure (Bolivia, Mali). The three top diagnoses with the largest overall decreases in relative frequency were acute gastroenteritis (-5.3%), rabies post-exposure prophylaxis (-2.8%), and dengue (-2.6%). Apart from COVID-19 (which rose from 0.1% to 12.7%), the three top diagnoses with the largest overall relative frequency increase were schistosomiasis (+4.9%), strongyloidiasis (+2.7%), and latent tuberculosis (+2.4%). CONCLUSIONS: A marked COVID-19 pandemic-induced decline in global travel activities is reflected in reduced travel-related infectious diseases sentinel surveillance reporting.

Design of effective outpatient sentinel surveillance for COVID-19 decision-making: a modeling study.

BACKGROUND: Decision-makers impose COVID-19 mitigations based on public health indicators such as reported cases, which are sensitive to fluctuations in supply and demand for diagnostic testing, and hospital admissions, which lag infections by up to two weeks. Imposing mitigations too early has unnecessary economic costs while imposing too late leads to uncontrolled epidemics with unnecessary cases and deaths. Sentinel surveillance of recently-symptomatic individuals in outpatient testing sites may overcome biases and lags in conventional indicators, but the minimal outpatient sentinel surveillance system needed for reliable trend estimation remains unknown. METHODS: We used a stochastic, compartmental transmission model to evaluate the performance of various surveillance indicators at reliably triggering an alarm in response to, but not before, a step increase in transmission of SARS-CoV-2. The surveillance indicators included hospital admissions, hospital occupancy, and sentinel cases with varying levels of sampling effort capturing 5, 10, 20, 50, or 100% of incident mild cases. We tested 3 levels of transmission increase, 3 population sizes, and conditions of either simultaneous transmission increase or lagged increase in the older population. We compared the indicators' performance at triggering alarm soon after, but not prior, to the transmission increase. RESULTS: Compared to surveillance based on hospital admissions, outpatient sentinel surveillance that captured at least 20% of incident mild cases could trigger an alarm 2 to 5 days earlier for a mild increase in transmission and 6 days earlier for a moderate or strong increase. Sentinel surveillance triggered fewer false alarms and averted more deaths per day spent in mitigation. When transmission increase in older populations lagged the increase in younger populations by 14 days, sentinel surveillance extended its lead time over hospital admissions by an additional 2 days. CONCLUSIONS: Sentinel surveillance of mild symptomatic cases can provide more timely and reliable information on changes in transmission to inform decision-makers in an epidemic like COVID-19.

COVID-19 Surveillance After Expiration of the Public Health Emergency Declaration - United States, May 11, 2023.

On January 31, 2020, the U.S. Department of Health and Human Services (HHS) declared, under Section 319 of the Public Health Service Act, a U.S. public health emergency because of the emergence of a novel virus, SARS-CoV-2.* After 13 renewals, the public health emergency will expire on May 11, 2023. Authorizations to collect certain public health data will expire on that date as well. Monitoring the impact of COVID-19 and the effectiveness of prevention and control strategies remains a public health priority, and a number of surveillance indicators have been identified to facilitate ongoing monitoring. After expiration of the public health emergency, COVID-19-associated hospital admission levels will be the primary indicator of COVID-19 trends to help guide community and personal decisions related to risk and prevention behaviors; the percentage of COVID-19-associated deaths among all reported deaths, based on provisional death certificate data, will be the primary indicator used to monitor COVID-19 mortality. Emergency department (ED) visits with a COVID-19 diagnosis and the percentage of positive SARS-CoV-2 test results, derived from an established sentinel network, will help detect early changes in trends. National genomic surveillance will continue to be used to estimate SARS-CoV-2 variant proportions; wastewater surveillance and traveler-based genomic surveillance will also continue to be used to monitor SARS-CoV-2 variants. Disease severity and hospitalization-related outcomes are monitored via sentinel surveillance and large health care databases. Monitoring of COVID-19 vaccination coverage, vaccine effectiveness (VE), and vaccine safety will also continue. Integrated strategies for surveillance of COVID-19 and other respiratory viruses can further guide prevention efforts. COVID-19-associated hospitalizations and deaths are largely preventable through receipt of updated vaccines and timely administration of therapeutics (1-4).

Correlations and Timeliness of COVID-19 Surveillance Data Sources and Indicators - United States, October 1, 2020-March 22, 2023.

When the U.S. COVID-19 public health emergency declaration expires on May 11, 2023, national reporting of certain categories of COVID-19 public health surveillance data will be transitioned to other data sources or will be discontinued; COVID-19 hospitalization data will be the only data source available at the county level (1). In anticipation of the transition, national COVID-19 surveillance data sources and indicators were evaluated for purposes of ongoing monitoring. The timeliness and correlations among surveillance indicators were analyzed to assess the usefulness of COVID-19-associated hospital admission rates as a primary indicator for monitoring COVID-19 trends, as well as the suitability of other replacement data sources. During April 2022-March 2023, COVID-19 hospital admission rates from the National Healthcare Safety Network (NHSN)† lagged 1 day behind case rates and 4 days behind percentages of positive test results and COVID-19 emergency department (ED) visits from the National Syndromic Surveillance Program (NSSP). In the same analysis, National Vital Statistics System (NVSS) trends in the percentage of deaths that were COVID-19-associated, which is tracked by date of death rather than by report date, were observable 13 days earlier than those from aggregate death count data, which will be discontinued (1). During October 2020-March 2023, strong correlations were observed between NVSS and aggregate death data (0.78) and between the percentage of positive SARS-CoV-2 test results from the National Respiratory and Enteric Viruses Surveillance System (NREVSS) and COVID-19 electronic laboratory reporting (CELR) (0.79), which will also be discontinued (1). Weekly COVID-19 Community Levels (CCLs) will be replaced with levels of COVID-19 hospital admission rates (low, medium, or high) which demonstrated >99% concordance by county during February 2022-March 2023. COVID-19-associated hospital admission levels are a suitable primary metric for monitoring COVID-19 trends, the percentage of COVID-19 deaths is a timely disease severity indicator, and the percentages of positive SARS-CoV-2 test results from NREVSS and ED visits serve as early indicators for COVID-19 monitoring. Collectively, these surveillance data sources and indicators can support monitoring of the impact of COVID-19 and related prevention and control strategies as ongoing public health priorities.

The epidemiological pattern of seasonal influenza in four sentinel sites in Iraq.

Introduction: Influenza is an acute viral infection with significant morbidity and mortality. It occurs annually each winter, which is called seasonal influenza, and is preventable through safe vaccine. Aim: The aim of this work is to know the epidemiological pattern of patients with seasonal influenza in Iraqi sentinel sites. Methods: A cross-sectional study was carried out on records of patients who attended four sentinel sites and registered to have influenza-like illness (ILI) or severe acute respiratory infection (SARI), and laboratory investigated. Results: The total number of cases was 1124; 36.2% of them aged 19-39 years; 53.9% were female; 74.9% lived in urban areas; 64.3% diagnosed as ILI; and 35.7% as SARI; 15.9% had diabetes, 12.7% had heart disease, 4.8% had asthma, 3% had a chronic lung disease, and 2% had hematological disease; 94.6% did not get influenza vaccine. About COVID-19 vaccine, 69.4% were not vaccinated, 3.5% got only one dose, and 27.1% completed two doses. Only the SARI cases needed admission; among them, 95.7% were cured. 6.5% were diagnosed with influenza-A virus, 26.1% had COVID-19, and 67.5% were negative. Among those with influenza, 97.3% had H3N2 subtype and 2.7% had H1N1 pdm09. Conclusions: The percentage of influenza virus in Iraq is relatively small. The age, classification of case (ILI or SARI), having diabetes, heart disease, or immunological disease, and taking COVID-19 vaccine have a significant association with influenza. Recommendations: It is needed for similar sentinel sites in other health directorates and for rising health education about seasonal influenza and its vaccine.

An assessment of the current epidemiological and laboratory capacities for influenza-like illnesses and severe acute respiratory infection surveillance, Yemen 2022.

Background and Objectives: We aim to re-activate influenza sentinel surveillance system in Yemen after disruption related to repurposing for COVID-19 pandemic. WHO Country Office (CO) in collaboration with Yemen's Ministry of Public Health and Population (MOPH&P) jointly conducted an assessment mission to assess the current situation of the influenza sentinel surveillance system and assess its capacity to detect influenza epidemics and monitor trends in circulating influenza and other respiratory viruses of epidemic and pandemic potential. This study presents the results of the assessment for three sentinel sites located in Aden, Taiz, and Hadramout/Mukalla. Methodology: A mixed methods approach was used to guide the assessment process and to help achieve the objectives. Data were collected as follows: desk review of the sentinel sites records and data; interviews with stakeholders, including key informants and partners; and direct observation through field visits to the sentinel sites, MOPH&P and the Central Public Health Laboratory (CPHL). Two assessment checklists were used: assessment of sentinel sites for SARI surveillance, and checklist for assessment of availability of SARI sentinel surveillance. Results and Conclusion: COVID-19 has affected health systems and services, and this was demonstrated in this assessment. The influenza sentinel surveillance system in Yemen is not effectively functional; however, there is plenty of room for improvement if investment in the system's restructuring, training, building technical and laboratory capacities, and conducting continuous and regular supervision visits.

Clinical features and severe outcome predictors of COVID-19 vaccine breakthrough infection among hospitalized patients: results from Egypt severe acute respiratory infections sentinel surveillance, 2021-2022.

BACKGROUND: COVID-19 vaccines are effective against infections and outcomes; however, breakthrough infections (VBT) are increasingly reported, possibly due to waning of vaccine-induced immunity or emerging variants. Most studies have focused on determining VBT rate based on antibody levels. This study aims at describing clinical features, risks, time trends, and outcomes of COVID-19 VBT among hospitalized patients in Egypt. METHODS: Data of SARS-CoV-2 confirmed patients hospitalized in 16 hospitals was obtained from the severe acute respiratory infections surveillance database, September 2021-April 2022. Data includes patients' demographics, clinical picture, and outcomes. Descriptive analysis was performed and patients with VBT were compared to not fully vaccinated (UPV). Bivariate and multivariate analyses were performed using Epi Info7 with a significance level < 0.05 to identify VBT risk factors. RESULTS: Overall, 1,297 patients enrolled, their mean age 56.7 ± 17.0 years, 41.5% were males, 64.7% received inactivated, 25.% viral vector, and 7.7% mRNA vaccine. VBT was identified in 156(12.0%) patients with an increasing trend over time. VBT significantly was higher in (16-35 years) age, males, in those who received inactivated vaccine compared to corresponding groups of UPV (14.1 vs. 9.0%, p < 0.05 and 57.1 vs. 39.4%, p < 0.001 and 64.7 vs. 45.1, p < 0.01 respectively). Whereas receiving mRNA vaccine was significantly protective against VBT (7.7 vs. 21.6%, p < 001). VBT patients tend to have shorter hospital stays and lower case fatality (mean hospital days = 6.6 ± 5.5 vs. 7.9 ± 5.9, p < 0.01 and CFR = 28.2 vs. 33.1, p < 0.01 respectively). MVA identified younger ages, male gender, and inactivated vaccines as risks for VBT. CONCLUSION: The study indicated that COVID-19 vaccines significantly reduce hospital days and fatality. VBT trend is on the rise and males, young ages, and inactivated vaccine receivers are at higher risk. Caution regarding relaxation of personal preventive measures in areas with higher or increasing incidences of COVID-19, particularly for the at-risk group even if they are vaccinated. The vaccination strategy should be revised to reduce VBT rate and increase vaccine effectiveness.

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.

Trends in COVID-Related Activity in Sentinel Family Medicine Practices: An Observational Study.

Objectives: During the COVID pandemic, data collected in family medicine were scarce. The COVID-FM project aimed to monitor trends of COVID-related activity in family medicine practices of the canton of Vaud, Switzerland, during the year 2021. Methods: Practitioners were invited to join an ad hoc sentinel surveillance system. Online data collection was based on daily activity reports and monthly questionnaires. Participants categorized daily counts of consultations and phone calls into predefined categories. Data were reported and discussed on a weekly basis with public health authorities. Results: On the target of 50 physicians, 37 general physicians from 32 practices finally constituted the COVID-FM sentinel network, contributing to 901 practice-weeks of surveillance in family medicine and 604 in paediatrics. In paediatrics, COVID-related activity corresponded mostly to COVID-19 diagnostic consultations (2911/25990 face-to-face consultations = 11.2%) while in family medicine, other COVID-related topics-such as questions on vaccination-predominated (4143/42221 = 9.8%). Conclusion: COVID-related consultations constituted an important part of primary care practices' activity in 2021. Monitoring COVID-related activity in primary care provided health authorities with valuable information to guide public health action.

Syndromic Surveillance Using Structured Telehealth Data: Case Study of the First Wave of COVID-19 in Brazil.

BACKGROUND: Telehealth has been widely used for new case detection and telemonitoring during the COVID-19 pandemic. It safely provides access to health care services and expands assistance to remote, rural areas and underserved communities in situations of shortage of specialized health professionals. Qualified data are systematically collected by health care workers containing information on suspected cases and can be used as a proxy of disease spread for surveillance purposes. However, the use of this approach for syndromic surveillance has yet to be explored. Besides, the mathematical modeling of epidemics is a well-established field that has been successfully used for tracking the spread of SARS-CoV-2 infection, supporting the decision-making process on diverse aspects of public health response to the COVID-19 pandemic. The response of the current models depends on the quality of input data, particularly the transmission rate, initial conditions, and other parameters present in compartmental models. Telehealth systems may feed numerical models developed to model virus spread in a specific region. OBJECTIVE: Herein, we evaluated whether a high-quality data set obtained from a state-based telehealth service could be used to forecast the geographical spread of new cases of COVID-19 and to feed computational models of disease spread. METHODS: We analyzed structured data obtained from a statewide toll-free telehealth service during 4 months following the first notification of COVID-19 in the Bahia state, Brazil. Structured data were collected during teletriage by a health team of medical students supervised by physicians. Data were registered in a responsive web application for planning and surveillance purposes. The data set was designed to quickly identify users, city, residence neighborhood, date, sex, age, and COVID-19-like symptoms. We performed a temporal-spatial comparison of calls reporting COVID-19-like symptoms and notification of COVID-19 cases. The number of calls was used as a proxy of exposed individuals to feed a mathematical model called "susceptible, exposed, infected, recovered, deceased." RESULTS: For 181 (43%) out of 417 municipalities of Bahia, the first call to the telehealth service reporting COVID-19-like symptoms preceded the first notification of the disease. The calls preceded, on average, 30 days of the notification of COVID-19 in the municipalities of the state of Bahia, Brazil. Additionally, data obtained by the telehealth service were used to effectively reproduce the spread of COVID-19 in Salvador, the capital of the state, using the "susceptible, exposed, infected, recovered, deceased" model to simulate the spatiotemporal spread of the disease. CONCLUSIONS: Data from telehealth services confer high effectiveness in anticipating new waves of COVID-19 and may help understand the epidemic dynamics.

Effectiveness of early warning systems in the detection of infectious diseases outbreaks: a systematic review.

BACKGROUND: Global pandemics have occurred with increasing frequency over the past decade reflecting the sub-optimum operationalization of surveillance systems handling human health data. Despite the wide array of current surveillance methods, their effectiveness varies with multiple factors. Here, we perform a systematic review of the effectiveness of alternative infectious diseases Early Warning Systems (EWSs) with a focus on the surveillance data collection methods, and taking into consideration feasibility in different settings. METHODS: We searched PubMed and Scopus databases on 21 October 2022. Articles were included if they covered the implementation of an early warning system and evaluated infectious diseases outbreaks that had potential to become pandemics. Of 1669 studies screened, 68 were included in the final sample. We performed quality assessment using an adapted CASP Checklist. RESULTS: Of the 68 articles included, 42 articles found EWSs successfully functioned independently as surveillance systems for pandemic-wide infectious diseases outbreaks, and 16 studies reported EWSs to have contributing surveillance features through complementary roles. Chief complaints from emergency departments' data is an effective EWS but it requires standardized formats across hospitals. Centralized Public Health records-based EWSs facilitate information sharing; however, they rely on clinicians' reporting of cases. Facilitated reporting by remote health settings and rapid alarm transmission are key advantages of Web-based EWSs. Pharmaceutical sales and laboratory results did not prove solo effectiveness. The EWS design combining surveillance data from both health records and staff was very successful. Also, daily surveillance data notification was the most successful and accepted enhancement strategy especially during mass gathering events. Eventually, in Low Middle Income Countries, working to improve and enhance existing systems was more critical than implementing new Syndromic Surveillance approaches. CONCLUSIONS: Our study was able to evaluate the effectiveness of Early Warning Systems in different contexts and resource settings based on the EWSs' method of data collection. There is consistent evidence that EWSs compiling pre-diagnosis data are more proactive to detect outbreaks. However, the fact that Syndromic Surveillance Systems (SSS) are more proactive than diagnostic disease surveillance should not be taken as an effective clue for outbreaks detection.

Sentinel Surveillance System Implementation and Evaluation for SARS-CoV-2 Genomic Data, Washington, USA, 2020-2021.

Genomic data provides useful information for public health practice, particularly when combined with epidemiologic data. However, sampling bias is a concern because inferences from nonrandom data can be misleading. In March 2021, the Washington State Department of Health, USA, partnered with submitting and sequencing laboratories to establish sentinel surveillance for SARS-CoV-2 genomic data. We analyzed available genomic and epidemiologic data during presentinel and sentinel periods to assess representativeness and timeliness of availability. Genomic data during the presentinel period was largely unrepresentative of all COVID-19 cases. Data available during the sentinel period improved representativeness for age, death from COVID-19, outbreak association, long-term care facility-affiliated status, and geographic coverage; timeliness of data availability and captured viral diversity also improved. Hospitalized cases were underrepresented, indicating a need to increase inpatient sampling. Our analysis emphasizes the need to understand and quantify sampling bias in phylogenetic studies and continue evaluation and improvement of public health surveillance systems.

Coinfection With SARS-CoV-2 and Influenza A(H1N1) in a Patient Seen at an Influenza-like Illness Surveillance Site in Egypt: Case Report.

BACKGROUND: Sentinel surveillance of influenza-like illness (ILI) in Egypt started in 2000 at 8 sentinel sites geographically distributed all over the country. In response to the COVID-19 pandemic, SARS-CoV-2 was added to the panel of viral testing by polymerase chain reaction for the first 2 patients with ILI seen at one of the sentinel sites. We report the first SARS-CoV-2 and influenza A(H1N1) virus co-infection with mild symptoms detected through routine ILI surveillance in Egypt. OBJECTIVE: This report aims to describe how the case was identified and the demographic and clinical characteristics and outcomes of the patient. METHODS: The case was identified by Central Public Health Laboratory staff, who contacted the ILI sentinel surveillance officer at the Ministry of Health. The case patient was contacted through a telephone call. Detailed information about the patient's clinical picture, course of disease, and outcome was obtained. The contacts of the patient were investigated for acute respiratory symptoms, disease confirmation, and outcomes. RESULTS: Among 510 specimens collected from patients with ILI symptoms from October 2019 to August 2020, 61 (12.0%) were COVID-19-positive and 29 (5.7%) tested positive for influenza, including 15 (51.7%) A(H1N1), 11 (38.0%) A(H3N2), and 3 (10.3%) influenza B specimens. A 21-year-old woman was confirmed to have SARS-CoV-2 and influenza A(H1N1) virus coinfection. She had a high fever of 40.2 °C and mild respiratory symptoms that resolved within 2 days with symptomatic treatment. All five of her family contacts had mild respiratory symptoms 2-3 days after exposure to the confirmed case, and their symptoms resolved without treatment or investigation. CONCLUSIONS: This case highlights the possible occurrence of SARS-CoV-2/influenza A(H1N1) coinfection in younger and healthy people, who may resolve the infection rapidly. We emphasize the usefulness of the surveillance system for detection of viral causative agents of ILI and recommend broadening of the testing panel, especially if it can guide case management.

Protocol for a winter sentinel surveillance program of notifiable respiratory viruses in Queensland.

BACKGROUND: With the reduction in access to polymerase chain reaction (PCR) testing and changes in testing guidelines in Australia, a reduced number of people are seeking testing for coronavirus disease (COVID-19), limiting the opportunity to monitor disease transmission. Knowledge of community transmission of COVID-19 and other respiratory viruses is essential to better predict subsequent surges in cases during the pandemic to alert health services, protect vulnerable populations and enhance public health measures. We describe a methodology for a testing-based sentinel surveillance program to monitor disease in the community for early signal detection of SARS-CoV-2 and other respiratory viruses. METHODS/DESIGN: A longitudinal active testing-based sentinel surveillance program for respiratory viruses (including SARS-CoV-2, influenza A, influenza B and Respiratory Syncytial Virus) will be implemented in some regions of Queensland. Adults will be eligible for enrolment if they are part of specific community groups at increased risk of exposure and have not had a COVID-19 infection in the last 13 weeks. Recruitment via workplaces will occur in-person, via email and through online advertisement. Asymptomatic participants will be tested via PCR for SARS-CoV-2 infection by weekly self-collected nasal swabs. In addition, symptomatic participants will be asked to seek SARS-CoV-2 and additional respiratory virus PCR testing at nominated COVID-19 testing sites. SARS-CoV-2 and respiratory virus prevalence data will be analysed weekly and at the end of the study period. DISCUSSION: Once implemented, this surveillance program will determine the weekly prevalence of COVID-19 and other respiratory viruses in the broader community by testing a representative sample of adults, with an aim to detect early changes in the baseline positivity rate. This information is essential to define the epidemiology of SARS-CoV-2 in the community in near-real time to inform public health control measures and prepare health services and other stakeholders for a rise in service demand.

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.

Tracking changes in SARS-CoV-2 transmission with a novel outpatient sentinel surveillance system in Chicago, USA.

Public health indicators typically used for COVID-19 surveillance can be biased or lag changing community transmission patterns. In this study, we investigate whether sentinel surveillance of recently symptomatic individuals receiving outpatient diagnostic testing for SARS-CoV-2 could accurately assess the instantaneous reproductive number R(t) and provide early warning of changes in transmission. We use data from community-based diagnostic testing sites in the United States city of Chicago. Patients tested at community-based diagnostic testing sites between September 2020 and June 2021, and reporting symptom onset within four days preceding their test, formed the sentinel population. R(t) calculated from sentinel cases agreed well with R(t) from other indicators. Retrospectively, trends in sentinel cases did not precede trends in COVID-19 hospital admissions by any identifiable lead time. In deployment, sentinel surveillance held an operational recency advantage of nine days over hospital admissions. The promising performance of opportunistic sentinel surveillance suggests that deliberately designed outpatient sentinel surveillance would provide robust early warning of increasing transmission.

Interdisciplinary Informatics to Support Primary Care Sentinel Surveillance of Respiratory Infectious Disease Including COVID-19: Donabedian Evaluation.

The Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) is one of Europe's oldest sentinel systems, providing sentinel surveillance since 1967. We report the interdisciplinary informatics required to run such a system. We used the Donabedian framework to describe the interdisciplinary informatics roles that support the structures, processes and outcomes of the RSC. Over the course of the COVID-19 pandemic University, RCGP, information technology specialists, SQL developers, analysts, practice liaison team, network member primary care providers, and their registered patients have nearly quadrupled the size of the RSC from working with 5 million to 19 million peoples pseudonymised health data. We have produced outputs used by the UK Health Security Agency to describe the epidemiology of COVID-19 and report vaccine effectiveness. We have also supported a trial of community-based therapies for COVID-19 and other observational studies. The home of the primary care sentinel surveillance network is with a clinical informatics research group. Interdisciplinary informatics teamwork was required to support primary care sentinel surveillance; such teams can accelerate the scale, scope and digital maturity of surveillance systems as demonstrated by the RSC across the COVID-19 pandemic.