Syndromic surveillance of population-level COVID-19 burden with cough monitoring in a hospital emergency waiting room.

Syndromic surveillance is an effective tool for enabling the timely detection of infectious disease outbreaks and facilitating the implementation of effective mitigation strategies by public health authorities. While various information sources are currently utilized to collect syndromic signal data for analysis, the aggregated measurement of cough, an important symptom for many illnesses, is not widely employed as a syndromic signal. With recent advancements in ubiquitous sensing technologies, it becomes feasible to continuously measure population-level cough incidence in a contactless, unobtrusive, and automated manner. In this work, we demonstrate the utility of monitoring aggregated cough count as a syndromic indicator to estimate COVID-19 cases. In our study, we deployed a sensor-based platform (Syndromic Logger) in the emergency room of a large hospital. The platform captured syndromic signals from audio, thermal imaging, and radar, while the ground truth data were collected from the hospital's electronic health record. Our analysis revealed a significant correlation between the aggregated cough count and positive COVID-19 cases in the hospital (Pearson correlation of 0.40, p-value < 0.001). Notably, this correlation was higher than that observed with the number of individuals presenting with fever (ρ = 0.22, p = 0.04), a widely used syndromic signal and screening tool for such diseases. Furthermore, we demonstrate how the data obtained from our Syndromic Logger platform could be leveraged to estimate various COVID-19-related statistics using multiple modeling approaches. Aggregated cough counts and other data, such as people density collected from our platform, can be utilized to predict COVID-19 patient visits related metrics in a hospital waiting room, and SHAP and Gini feature importance-based metrics showed cough count as the important feature for these prediction models. Furthermore, we have shown that predictions based on cough counting outperform models based on fever detection (e.g., temperatures over 39°C), which require more intrusive engagement with the population. Our findings highlight that incorporating cough-counting based signals into syndromic surveillance systems can significantly enhance overall resilience against future public health challenges, such as emerging disease outbreaks or pandemics.

Postpandemic Sentinel Surveillance of Respiratory Diseases in the Context of the World Health Organization Mosaic Framework: Protocol for a Development and Evaluation Study Involving the English Primary Care Network 2023-2024.

BACKGROUND: Prepandemic sentinel surveillance focused on improved management of winter pressures, with influenza-like illness (ILI) being the key clinical indicator. The World Health Organization (WHO) global standards for influenza surveillance include monitoring acute respiratory infection (ARI) and ILI. The WHO's mosaic framework recommends that the surveillance strategies of countries include the virological monitoring of respiratory viruses with pandemic potential such as influenza. The Oxford-Royal College of General Practitioner Research and Surveillance Centre (RSC) in collaboration with the UK Health Security Agency (UKHSA) has provided sentinel surveillance since 1967, including virology since 1993. OBJECTIVE: We aim to describe the RSC's plans for sentinel surveillance in the 2023-2024 season and evaluate these plans against the WHO mosaic framework. METHODS: Our approach, which includes patient and public involvement, contributes to surveillance objectives across all 3 domains of the mosaic framework. We will generate an ARI phenotype to enable reporting of this indicator in addition to ILI. These data will support UKHSA's sentinel surveillance, including vaccine effectiveness and burden of disease studies. The panel of virology tests analyzed in UKHSA's reference laboratory will remain unchanged, with additional plans for point-of-care testing, pneumococcus testing, and asymptomatic screening. Our sampling framework for serological surveillance will provide greater representativeness and more samples from younger people. We will create a biomedical resource that enables linkage between clinical data held in the RSC and virology data, including sequencing data, held by the UKHSA. We describe the governance framework for the RSC. RESULTS: We are co-designing our communication about data sharing and sampling, contextualized by the mosaic framework, with national and general practice patient and public involvement groups. We present our ARI digital phenotype and the key data RSC network members are requested to include in computerized medical records. We will share data with the UKHSA to report vaccine effectiveness for COVID-19 and influenza, assess the disease burden of respiratory syncytial virus, and perform syndromic surveillance. Virological surveillance will include COVID-19, influenza, respiratory syncytial virus, and other common respiratory viruses. We plan to pilot point-of-care testing for group A streptococcus, urine tests for pneumococcus, and asymptomatic testing. We will integrate test requests and results with the laboratory-computerized medical record system. A biomedical resource will enable research linking clinical data to virology data. The legal basis for the RSC's pseudonymized data extract is The Health Service (Control of Patient Information) Regulations 2002, and all nonsurveillance uses require research ethics approval. CONCLUSIONS: The RSC extended its surveillance activities to meet more but not all of the mosaic framework's objectives. We have introduced an ARI indicator. We seek to expand our surveillance scope and could do more around transmissibility and the benefits and risks of nonvaccine therapies.

Utilizing Wearable Device Data for Syndromic Surveillance: A Fever Detection Approach.

Commercially available wearable devices (wearables) show promise for continuous physiological monitoring. Previous works have demonstrated that wearables can be used to detect the onset of acute infectious diseases, particularly those characterized by fever. We aimed to evaluate whether these devices could be used for the more general task of syndromic surveillance. We obtained wearable device data (Oura Ring) from 63,153 participants. We constructed a dataset using participants' wearable device data and participants' responses to daily online questionnaires. We included days from the participants if they (1) completed the questionnaire, (2) reported not experiencing fever and reported a self-collected body temperature below 38 °C (negative class), or reported experiencing fever and reported a self-collected body temperature at or above 38 °C (positive class), and (3) wore the wearable device the nights before and after that day. We used wearable device data (i.e., skin temperature, heart rate, and sleep) from the nights before and after participants' fever day to train a tree-based classifier to detect self-reported fevers. We evaluated the performance of our model using a five-fold cross-validation scheme. Sixteen thousand, seven hundred, and ninety-four participants provided at least one valid ground truth day; there were a total of 724 fever days (positive class examples) from 463 participants and 342,430 non-fever days (negative class examples) from 16,687 participants. Our model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.85 and an average precision (AP) of 0.25. At a sensitivity of 0.50, our calibrated model had a false positive rate of 0.8%. Our results suggest that it might be possible to leverage data from these devices at a public health level for live fever surveillance. Implementing these models could increase our ability to detect disease prevalence and spread in real-time during infectious disease outbreaks.

Data Sharing in a Decentralized Public Health System: Lessons From COVID-19 Syndromic Surveillance.

The COVID-19 pandemic revealed that data sharing challenges persist across public health information systems. We examine the specific challenges in sharing syndromic surveillance data between state, local, and federal partners. These challenges are complicated by US federalism, which decentralizes public health response and creates friction between different government units. The current policies restrict federal access to state and local syndromic surveillance data without each jurisdiction's consent. These policies frustrate legitimate federal governmental interests and are contrary to ethical guidelines for public health data sharing. Nevertheless, state and local public health agencies must continue to play a central role as there are important risks in interpreting syndromic surveillance data without understanding local contexts. Policies establishing a collaborative framework will be needed to support data sharing between federal, state, and local partners. A collaborative framework would be enhanced by a governance group with robust state and local involvement and policy guardrails to ensure the use of data is appropriate. These policy and relational challenges must be addressed to actualize a truly national public health information system.

Increased circulation of echovirus 11 in the general population and hospital patients as elicited by the non-polio enterovirus laboratory-based sentinel surveillance in northern Italy, 2023.

OBJECTIVES: Following the alert of echovirus 11 (E-11) infection in neonates in EU/EEA Member States, we conducted an investigation of E-11 circulation by gathering data from community and hospital surveillance of enterovirus (EV) in northern Italy from 01 August 2021 to 30 June 2023. METHODS: Virological results of EVs were obtained from the regional sentinel surveillance database for influenza-like illness (ILI) in outpatients, and from the laboratory database of ten hospitals for inpatients with either respiratory or neurological symptoms. Molecular characterization of EVs was performed by sequence analysis of the VP1 gene. RESULTS: In our ILI series, the rate of EV-positive specimens showed an upward trend from the end of May 2023, culminating at the end of June, coinciding with an increase in EV-positive hospital cases. The E-11 identified belonged to the D5 genogroup and the majority (83%) were closely associated with the novel E-11 variant, first identified in severe neonatal infections in France since 2022. E-11 was identified sporadically in community cases until February 2023, when it was also found in hospitalized cases with a range of clinical manifestations. All E-11 cases were children, with 14 out of 24 cases identified through hospital surveillance. Of these cases, 60% were neonates, and 71% had severe clinical manifestations. CONCLUSION: Baseline epidemiological data collected since 2021 through EV laboratory-based surveillance have rapidly tracked the E-11 variant since November 2022, alongside its transmission during the late spring of 2023.

Effectiveness of one and two doses of acellular pertussis vaccines against laboratory-confirmed pertussis requiring hospitalisation in infants: Results of the PERTINENT sentinel surveillance system in six EU/EEA countries, December 2015 - December 2019.

BACKGROUND: Monitoring effectiveness of pertussis vaccines is necessary to adapt vaccination strategies. PERTINENT, Pertussis in Infants European Network, is an active sentinel surveillance system implemented in 35 hospitals across six EU/EEA countries. We aim to measure pertussis vaccines effectiveness (VE) by dose against hospitalisation in infants aged <1 year. METHODS: From December 2015 to December 2019, participating hospitals recruited all infants with pertussis-like symptoms. Cases were vaccine-eligible infants testing positive for Bordetella pertussis by PCR or culture; controls were those testing negative to all Bordetella spp. For each vaccine dose, we defined an infant as vaccinated if she/he received the corresponding dose >14 days before symptoms. Unvaccinated were those who did not receive any dose. We calculated (one-stage model) pooled VE as 100*(1-odds ratio of vaccination) adjusted for country, onset date (in 3-month categories) and age-group (when sample allowed it). RESULTS: Of 1,393 infants eligible for vaccination, we included 259 cases and 746 controls. Median age was 16 weeks for cases and 19 weeks for controls (p < 0.001). Median birth weight and gestational age were 3,235 g and week 39 for cases, 3,113 g and week 39 for controls. Among cases, 119 (46 %) were vaccinated: 74 with one dose, 37 two doses, 8 three doses. Among controls, 469 (63 %) were vaccinated: 233 with one dose, 206 two doses, 30 three doses. Adjusted VE after at least one dose was 59 % (95 %CI: 36-73). Adjusted VE was 48 % (95 %CI: 5-71) for dose one (416 eligible infants) and 76 % (95 %CI: 43-90) for dose two (258 eligible infants). Only 42 infants were eligible for the third dose. CONCLUSIONS: Our results suggest moderate one-dose and two-dose VE in infants. Larger sample size would allow more precise estimates for dose one, two and three.

Use of Sentinel Surveillance Platforms for Monitoring SARS-CoV-2 Activity: Evidence From Analysis of Kenya Influenza Sentinel Surveillance Data.

BACKGROUND: Little is known about the cocirculation of influenza and SARS-CoV-2 viruses during the COVID-19 pandemic and the use of respiratory disease sentinel surveillance platforms for monitoring SARS-CoV-2 activity in sub-Saharan Africa. OBJECTIVE: We aimed to describe influenza and SARS-CoV-2 cocirculation in Kenya and how the SARS-CoV-2 data from influenza sentinel surveillance correlated with that of universal national surveillance. METHODS: From April 2020 to March 2022, we enrolled 7349 patients with severe acute respiratory illness or influenza-like illness at 8 sentinel influenza surveillance sites in Kenya and collected demographic, clinical, underlying medical condition, vaccination, and exposure information, as well as respiratory specimens, from them. Respiratory specimens were tested for influenza and SARS-CoV-2 by real-time reverse transcription polymerase chain reaction. The universal national-level SARS-CoV-2 data were also obtained from the Kenya Ministry of Health. The universal national-level SARS-CoV-2 data were collected from all health facilities nationally, border entry points, and contact tracing in Kenya. Epidemic curves and Pearson r were used to describe the correlation between SARS-CoV-2 positivity in data from the 8 influenza sentinel sites in Kenya and that of the universal national SARS-CoV-2 surveillance data. A logistic regression model was used to assess the association between influenza and SARS-CoV-2 coinfection with severe clinical illness. We defined severe clinical illness as any of oxygen saturation <90%, in-hospital death, admission to intensive care unit or high dependence unit, mechanical ventilation, or a report of any danger sign (ie, inability to drink or eat, severe vomiting, grunting, stridor, or unconsciousness in children younger than 5 years) among patients with severe acute respiratory illness. RESULTS: Of the 7349 patients from the influenza sentinel surveillance sites, 76.3% (n=5606) were younger than 5 years. We detected any influenza (A or B) in 8.7% (629/7224), SARS-CoV-2 in 10.7% (768/7199), and coinfection in 0.9% (63/7165) of samples tested. Although the number of samples tested for SARS-CoV-2 from the sentinel surveillance was only 0.2% (60 per week vs 36,000 per week) of the number tested in the universal national surveillance, SARS-CoV-2 positivity in the sentinel surveillance data significantly correlated with that of the universal national surveillance (Pearson r=0.58; P<.001). The adjusted odds ratios (aOR) of clinical severe illness among participants with coinfection were similar to those of patients with influenza only (aOR 0.91, 95% CI 0.47-1.79) and SARS-CoV-2 only (aOR 0.92, 95% CI 0.47-1.82). CONCLUSIONS: Influenza substantially cocirculated with SARS-CoV-2 in Kenya. We found a significant correlation of SARS-CoV-2 positivity in the data from 8 influenza sentinel surveillance sites with that of the universal national SARS-CoV-2 surveillance data. Our findings indicate that the influenza sentinel surveillance system can be used as a sustainable platform for monitoring respiratory pathogens of pandemic potential or public health importance.

Geospatial methodology for determining the regional prevalence of hospital-reported childhood intussusception in patients from India.

Both developed and developing countries carry a large burden of pediatric intussusception. Sentinel site surveillance-based studies have highlighted the difference in the regional incidence of intussusception. The objectives of this manuscript were to geospatially map the locations of hospital-confirmed childhood intussusception cases reported from sentinel hospitals, identify clustering and dispersion, and reveal the potential causes of the underlying pattern. Geospatial analysis revealed positive clustering patterns, i.e., a Moran's I of 0.071 at a statistically significant (p value < 0.0010) Z score of 16.14 for the intussusception cases across India (cases mapped n = 2221), with 14 hotspots in two states (Kerala = 6 and Tamil Nadu = 8) at the 95% CI. Granular analysis indicated that 67% of the reported cases resided < 50 km from the sentinel hospitals, and the average travel distance to the sentinel hospital from the patient residence was calculated as 47 km (CI 95% min 1 km-max 378 km). Easy access and facility referral preferences were identified as the main causes of the existing clustering pattern of the disease. We recommend designing community-based surveillance studies to improve the understanding of the prevalence and regional epidemiological burden of the disease.

[Pathogenic characteristics of viral diarrhea in children under five years of age in sentinel surveillance in Lulong County of Hebei Province, 2010-2020].

Objective: To analyze pathogenic characteristics of viral diarrhea in children aged <5 years in Hebei Province and provide reference for the prevention and control of viral diarrhea in children. Methods: Stool samples were collected from in-patients with diarrhea under five years old from sentinel hospitals in Lulong County of Hebei between 2010 and 2020. ELISA detected rotavirus antigen, and then positive samples were genotyped by semi nested reverse transcription PCR of two rounds. Calicivirus, genotyping astrovirus, and adenovirus were detected by real-time fluorescence quantification PCR. The data were analyzed by using software SPSS 20.0. Results: In 2 925 detected stool samples, 1 919 (65.61%) were positive. The positive rates of rotavirus, calicivirus, adenovirus, and astrovirus were 42.80% (1 252/2 925), 22.12% (647/2 925), 6.19% (181/2 925), 3.56% (104/2 925). Viral diarrhea was mainly caused by rotavirus infection, accounting for 59.30% (1 017/1 715) between 2010 and 2017, and by calicivirus infection accounting for 53.43% (109/204) between 2018 and 2020. The peak positive rate of rotavirus occurred in winter, with the highest rate in infants aged 12 to 17 months (52.96%,483/912). In the rotavirus positive samples, G9P[8] was mainly detected strains (58.31%,730/1 252), followed by G3P[8] (8.15%,102/1 252). The calicivirus-positive samples were mainly infected with norovirus GⅡ. Sequence analysis indicated that the main type was GⅡ.4 [P31] between 2011 and 2016 and GⅡ.3 [P12] in 2018. Conclusions: Rotavirus and calicivirus were the main pathogens causing infant diarrhea in children under five years old in Hebei from 2010 to 2020. Winter was the main epidemic season.

[Progress in research of rash and fever syndrome surveillance and early warning].

Objective: To introduce the progress in research of rash and fever syndrome (RFS) surveillance and early warning both at home and abroad, and provide reference for surveillance and prevention of RFS in China. Methods: The keywords "fever" "rash" and "surveillance" and others were used for a literature retrieval by using China National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform, PubMed and Web of Science. The languages of literatures were limited in Chinese and English. The key information of the literatures were collected and analyzed with Excel. Results: A total of 36 study papers (21 in Chinese and 15 in English) were included. The studies mainly focused on the pathogen surveillance of RFS (n=19). The pathogens included measles virus, varicella-zoster virus, rubella virus, enterovirus, human B19 virus, dengue virus, streptococcus group A, Salmonella typhi and Salmonella paratyphoid,human herpesvirus, mumps virus and adenovirus. Eight studies were about the surveillance in major events, such as sport game, World Expo and religious gathering, or sudden natural disasters, such as earthquake and tropical storm, during 2010-2015. Eight studies focused on case or epidemic surveillance, most of which were studies from other counties. The surveillance sites were medical institutions. RFS was diagnosed according to the International Classification of Diseases, 9th (ICD-9) and symptoms descripted in chief-complaint. Only one study in Mongolia conducted RFS epidemic prediction. The analysis methods of 36 papers included simple descriptive analysis, time-based early warning models (such as regression analysis, fixed threshold method, Hugh Hart control chart method and cumulative sum control chart method) and time series analysis method. Conclusions: In the future, RFS surveillance system should cover both known pathogens and emerging pathogens. Automatic surveillance using information capture and intelligent modelling can be applied to improve the sensitivity and specificity of RFS surveillance and early warning.

Cross-sectional study of influenza trends and costs in Malaysia between 2016 and 2018.

BACKGROUND AND OBJECTIVES: While influenza circulates year-round in Malaysia, research data on its incidence is scarce. Yet, this information is vital to the improvement of public health through evidence-based policies. In this cross-sectional study, we aimed to determine the trends and financial costs of influenza. METHODS: Data for the years 2016 through 2018 were gathered retrospectively from several sources. These were existing Ministry of Health (MOH) influenza sentinel sites data, two teaching hospitals, and two private medical institutions in the Klang Valley, Malaysia. Expert consensus determined the final estimates of burden for laboratory-confirmed influenza-like illness (ILI) and severe acute respiratory infection (SARI). Economic burden was estimated separately using secondary data supplemented by MOH casemix costing. RESULTS: Altogether, data for 11,652 cases of ILI and 5,764 cases of SARI were extracted. The influenza B subtype was found to be predominant in 2016, while influenza A was more prevalent in 2017 and 2018. The distribution timeline revealed that the highest frequency of cases occurred in March and April of all three years. The costs of influenza amounted to MYR 310.9 million over the full three-year period. CONCLUSIONS: The study provides valuable insights into the dynamic landscape of influenza in Malaysia. The findings reveal a consistent year-round presence of influenza with irregular seasonal peaks, including a notable influenza A epidemic in 2017 and consistent surges in influenza B incidence during March across three years. These findings underscore the significance of continuous monitoring influenza subtypes for informed healthcare strategies as well as advocate for the integration of influenza vaccination into Malaysia's national immunization program, enhancing overall pandemic preparedness.

The weekly P25 of the age of the influenza-like illness shows a higher correlation with COVID-19 mortality than rapid tests and could predict the evolution of COVID-19 pandemics in sentinel surveillance, Piura, Perú, 2021.

GOAL: To describe the dynamics of syndromic surveillance of ILI cases in seasonal and COVID-19 pandemic scenarios. METHODOLOGY: A descriptive study of the epidemiological behavior of ILI in the seasonal and COVID-19 pandemic scenarios. Of a sample of 16,231 cases of ILI from 2013 to 2021, the features of cases from 68 weeks before and during the pandemic were selected and compared; weekly endemic channels were built; data fluctuations on the trend of ILI cases were analyzed; and estimated weekly correlations between weekly P25 age, cases confirmed by rapid tests, and mortality from COVID-19. To analyze clinical-epidemiological and mortality data, Student's t test, Mann-Whitney U, Chi2, Spearman's Ro, polynomial, and multinomial regression with a 95% confidence interval were used. RESULTS: During the COVID-19 pandemic, those most affected with ILI were: adults and the elderly; higher median age; autochthonous cases predominated; a lower proportion of other syndromes; delays in seeking care; and a higher rate of pneumonia attack than in the seasonal period (p< 0.01). Rapid tests (serological and antigenic) confirmed 52.7% as COVID-19. Two ILI pandemic waves were seasonally consistent with confirmed COVID-19 cases and district mortality with robust correlation (p<0.01) before and during the pandemic, especially the ILI weekly P25 age, which has a more robust correlation with mortality than ILI and rapid tests (p<0.01) whose endemic channels describe and could predict the evolution of the pandemic (p<0.01). CONCLUSIONS: The pandemic changed the clinical and epidemiological behavior of ILI, and the weekly P25 of age is a more robust indicator to monitor the COVID-19 pandemic than a rapid test and could predict its evolution.

[Results of PCR Pool Testing In Primary and Special Needs Schools In Bavaria For The School Year 2021/2022: Sentinel Surveillance In Face-To-Face Teaching During The Sars-CoV-2 Pandemic]. / Ergebnisse von PCR-Pooltestungen an bayerischen Grund- und Förderschulen im Schuljahr 2021/2022 zur Surveillance im Präsenzunterricht während der SARS-CoV-2-Pandemie.

In the school years 2019/20 and 2020/21, children were physically, psychologically, and socially stressed by school closures caused by the SARS-CoV-2 pandemic. To ensure attendance with optimal infection protection, PCR pool testing was conducted during the 2021/22 school year at Bavarian elementary schools and schools for pupils with special needs for timely detection of SARS-CoV-2 infection. This study analyzes the results of PCR pool testing over time stratified by region, school type, and age of children. The data were obtained from classes in elementary and special needs schools, involving pupils aged 6 to 11 years, who participated in the Bavaria-wide PCR pool testing from 09/20/21 to 04/08/22. Samples were collected twice weekly, consisting of PCR pool samples and individual PCR samples, which were only evaluated in case of a positive pool test. A class was considered positive if at least one individual sample from that class was positive within a calendar week (CW). A school (class) was considered to be infection-prone if three or more classes in that school (students in that class) were positive within a CW. The data included 2,430 elementary schools (339 special needs schools) with 23,021 (2,711) classes and 456,478 (29,200) children. A total of 1,157,617 pools (of which 3.37% were positive) and 724,438 individual samples (6.76% positive) were analyzed. Larger schools exhibited higher PR compared to smaller schools. From January 2022, the Omicron variant led to a massive increase in PR across Bavaria. The incidence rates per 100,000 person-weeks within the individual school samples were significantly lower than the concurrently reported age-specific and general infection incidences in the overall Bavarian population. PCR pool testing revealed relatively few positive pools, with an average of four children per one hundred pools testing positive. Schools and classes were rarely considered infection-prone, even during periods of high incidences outside of schools. The combination of PCR pool testing and hygiene measures allowed for a largely safe in-person education for pupils in primary and special needs schools in the school year 2021/22.

Analysis of data from two influenza surveillance hospitals in Zhejiang province, China, for the period 2018-2022.

PURPOSE: To assess the epidemiology of seasonal influenza in Huzhou City, Zhejiang Province, China, during 2018-2022 and provide insights for influenza prevention. METHODS: Following the National Influenza Surveillance Program, we conducted pathogen surveillance by randomly sampling throat swabs from cases with influenza-like illness (ILI) at two sentinel hospitals. RESULTS: From 2018 to 2022, a total of 3,813,471 cases were treated at two hospitals in Huzhou, China. Among them, there were 112,385 cases of Influenza-Like Illness (ILI), accounting for 2.95% of the total number of cases. A total of 11,686 ILI throat swab samples were tested for influenza viruses, with 1,602 cases testing positive for influenza virus nucleic acid, resulting in a positivity rate of 13.71%. Among the positive strains, there were 677 strains of A(H3N2) virus, 301 strains of A(H1N1) virus, 570 strains of B/Victoria virus, and 54 strains of B/Yamagata virus. The ILI percentage (ILI%) and influenza nucleic acid positivity rate showed winter-spring peaks in the years 2018, 2019, 2021, and 2022, with the peaks concentrated in January and February. Additionally, a small peak was observed in August 2022 during the summer season. No peak was observed during the winter-spring season of 2020. The highest proportion of ILI cases was observed in children aged 0-4 years, followed by school-age children aged 5-14 years. There was a positive correlation between ILI% and influenza virus nucleic acid positivity rate (r = 0.60, p < 0.05). CONCLUSIONS: The influenza outbreak in Huzhou from 2020 to 2022 was to some extent influenced by the COVID-19 pandemic and public health measures. After the conclusion of the COVID-19 pandemic, the influenza outbreak in Huzhou may become more severe. Therefore, it is crucial to promptly assess the influenza outbreak trends based on the ILI% and the positivity rate of influenza virus nucleic acid tests.

2023/24 mid-season influenza and Omicron XBB.1.5 vaccine effectiveness estimates from the Canadian Sentinel Practitioner Surveillance Network (SPSN).

The Canadian Sentinel Practitioner Surveillance Network reports mid-season 2023/24 influenza vaccine effectiveness (VE) of 63% (95% CI: 51-72) against influenza A(H1N1)pdm09, lower for clade 5a.2a.1 (56%; 95% CI: 33-71) than clade 5a.2a (67%; 95% CI: 48-80), and lowest against influenza A(H3N2) (40%; 95% CI: 5-61). The Omicron XBB.1.5 vaccine protected comparably well, with VE of 47% (95% CI: 21-65) against medically attended COVID-19, higher among people reporting a prior confirmed SARS-CoV-2 infection at 67% (95% CI: 28-85).

Chlamydia retesting remains low among young women in Australia: an observational study using sentinel surveillance data, 2018-2022.

BACKGROUND: Chlamydia remains the most notified bacterial sexually transmissible infection in Australia with guidelines recommending testing for re-infection at 3months post treatment. This paper aimed to determine chlamydia retesting and repeat positivity rates within 2-4months among young women in Australia, and to evaluate what factors increase or decrease the likelihood of retesting. METHODS: Chlamydia retesting rates among 16-29-year-old women were analysed from Australian Collaboration for Coordinated Enhanced Sentinel Surveillance of sexually transmissible infection and bloodborne virus (ACCESS) sentinel surveillance data (n =62 sites). Among women with at least one positive test between 1 January 2018 and 31 August 2022, retesting counts and proportions within 2-4months were calculated. Logistic regression was performed to assess factors associated with retesting within 2-4months. RESULTS: Among 8758 women who were positive before 31 August 2022 to allow time for follow up, 1423 (16.2%) were retested within 2-4months, of whom 179 (12.6%) tested positive. The odds of retesting within 2-4months were 25% lower if tested in a coronavirus disease 2019 (COVID-9) pandemic year (2020-2022) (aOR=0.75; 95% CI 0.59-0.95). Among 9140 women with a positive test before 30 November 2022, 397 (4.3%) were retested too early (within 7days to 1month) and 81 (20.4%) of those were positive. CONCLUSIONS: Chlamydia retesting rates remain low with around a sixth of women retested within 2-4months in line with guidelines. Re-infection is common with around one in eight retesting positive. An increase in retesting is required to reduce the risk of reproductive complications and onward transmission.

Epidemiology of influenza in Nigeria: A secondary analysis of the sentinel surveillance data in Nigeria from 2010 - 2020.

BACKGROUND: Influenza is a leading cause of morbidity and mortality globally. Little is known of the true burden and epidemiology of influenza in Africa. Nigeria has a sentinel surveillance system for influenza virus (IFV). This study seeks to describe the epidemiological characteristics of influenza cases in Nigeria through secondary data analysis of the sentinel surveillance data from 2010 to 2020. METHODOLOGY: A retrospective secondary data analysis of data collected from patients with influenza-like illness (ILI) and severe acute respiratory infection (SARI) in the four Nigeria Influenza Sentinel Surveillance sites from January 2010 to December 2020. Data was cleaned and analyzed using Microsoft Excel and Epi info 7.2 for frequencies and proportions. The results of the analysis were summarized in tables and charts. RESULTS: A total of 13,828 suspected cases of influenza were recorded at the sentinel sites during the study period. About 10.3% (1421/13,828) of these tested positive for IFV of which 1243 (87.5%) were ILI patients, 175 (12.3%) SARI patients, and 3 (0.2%) novel H1N1 patients. Males accounted for 54.2% (770/1421) of the confirmed cases. The median age of confirmed cases was 3 years (range: <1month-97 years). Children 0-4 years accounted for 69.3% (985/1421) of all cases. The predominant subtypes were B lineage not determined (32.3%), A/H1N1 pdm09 (28.8%) and A/H3 (23.0%). There were periods of sustained transmission in most years with 2011 having the highest number of cases. Overall, there were more cases around January to March and August to November. Heart disease and chronic shortness of breath were the most common co-morbidities identified among confirmed cases. CONCLUSION: Influenza remains a significant cause of respiratory illness, especially among children aged less than 4 years. Influenza cases occur all year round with irregular seasonality in Nigeria. Children less than 4 years and those with co-morbidities should be prioritized for vaccination. Vaccine composition in the country should take cognizance of the prevailing strains which are type B (lineage not determined), A/H1N1 pdm09 and A/H3.