Developing influenza and respiratory syncytial virus activity thresholds for syndromic surveillance in England

Determining the timing of respiratory syncytial virus (RSV) epidemics: a systematic review, 2016 to 2021; method categorisation and identification of influencing factors

BackgroundThere is currently no standardised approach to estimate respiratory syncytial virus (RSV) epidemics' timing (or seasonality), a critical information for their effective prevention and control.AimWe aimed to provide an overview of methods to define RSV seasonality and identify factors supporting method choice or interpretation/comparison of seasonal estimates.MethodsWe systematically searched PubMed and Embase (2016-2021) for studies using quantitative approaches to determine the start and end of RSV epidemics. Studies' features (data-collection purpose, location, regional/(sub)national scope), methods, and assessment characteristics (case definitions, sampled population's age, in/outpatient status, setting, diagnostics) were extracted. Methods were categorised by their need of a denominator (i.e. numbers of specimens tested) and their retrospective vs real-time application. Factors worth considering when choosing methods and assessing seasonal estimates were sought by analysing studies.ResultsWe included 32 articles presenting 49 seasonality estimates (18 thereof through the 10% positivity threshold method). Methods were classified into eight categories, two requiring a denominator (1 retrospective; 1 real-time) and six not (3 retrospective; 3 real-time). A wide range of assessment characteristics was observed. Several studies showed that seasonality estimates varied when methods differed, or data with dissimilar assessment characteristics were employed. Five factors (comprising study purpose, application time, assessment characteristics, healthcare system and policies, and context) were identified that could support method choice and result interpretation.ConclusionMethods and assessment characteristics used to define RSV seasonality are heterogeneous. Our categorisation of methods and proposed framework of factors may assist in choosing RSV seasonality methods and interpretating results.

Epidemiology of respiratory syncytial virus in children younger than 5 years in England during the COVID-19 pandemic, measured by laboratory, clinical, and syndromic surveillance: a retrospective observational study

BACKGROUND

Seasonal epidemics of respiratory syncytial virus (RSV) cause a clinically significant burden of disease among young children. Non-pharmaceutical interventions targeted at SARS-CoV-2 have affected the activity of other respiratory pathogens. We describe changes in the epidemiology of RSV among children younger than 5 years in England since 2020.

METHODS

Surveillance data on RSV infections, comprising laboratory-confirmed cases, proportion of positive tests, hospital admissions for RSV-attributable illness, and syndromic indicators for RSV-associated disease (emergency department attendances for acute bronchitis or bronchiolitis, non-emergency health advice telephone service [NHS 111] calls for cough, general practitioner [GP] in-hours consultations for respiratory tract infections, and GP out-of-hours contacts for acute bronchitis or bronchiolitis) were analysed from Dec 29, 2014 to March 13, 2022, for children younger than 5 years. Data were extracted from national laboratory, clinical, and syndromic surveillance systems. Time-series analyses using generalised linear models were used to estimate the effect of non-pharmaceutical interventions targeting SARS-CoV-2 on RSV indicators, with absolute and relative changes calculated by comparing observed and predicted values.

FINDINGS

RSV-associated activity was reduced for all RSV indicators during winter 2020-21 in England, with 10 280 (relative change -99·5% [95% prediction interval -100·0 to -99·1]) fewer laboratory-confirmed cases, 22·2 (-99·6%) percentage points lower test positivity, 92 530 (-80·8% [-80·9 to -80·8]) fewer hospital admissions, 96 672 (-73·7% [-73·7 to -73·7]) fewer NHS 111 calls, 2924 (-88·8% [-90·4 to -87·2]) fewer out-of-hours GP contacts, 91 304 (-89·9% [-90·0 to -89·9]) in-hours GP consultations, and 27 486 (-85·3% [-85·4 to -85·2]) fewer emergency department attendances for children younger than 5 years compared with predicted values based on winter seasons before the COVID-19 pandemic. An unprecedented summer surge of RSV activity occurred in 2021, including 11 255 (1258·3% [1178·3 to 1345·8]) extra laboratory-confirmed cases, 11·6 percentage points (527·3%) higher test positivity, 7604 (10·7% [10·7 to 10·8]) additional hospital admissions, 84 425 (124·8% [124·7 to 124·9]) more calls to NHS 111, 409 (39·0% [36·6 to 41·8]) more out-of-hours GP contacts, and 9789 (84·9% [84·5 to 85·4]) more emergency department attendances compared with the predicted values, although there were 21 805 (-34·1% [-34·1 to -34·0]) fewer in-hours GP consultations than expected. Most indicators were also lower than expected in winter 2021-22, although to a lesser extent than in winter 2020-21.

INTERPRETATION

The extraordinary absence of RSV during winter 2020-21 probably resulted in a cohort of young children without natural immunity to RSV, thereby raising the potential for increased RSV incidence, out-of-season activity, and health-service pressures when measures to restrict SARS-CoV-2 transmission were relaxed.

FUNDING

None.

Applying the Spatial Transmission Network to the Forecast of Infectious Diseases Across Multiple Regions

Objective

Timely and accurate forecast of infectious diseases is essential for achieving precise prevention and control. A good forecasting method of infectious diseases should have the advantages of interpretability, feasibility, and forecasting performance. Since previous research had illustrated that the spatial transmission network (STN) showed good interpretability and feasibility, this study further explored its forecasting performance for infectious diseases across multiple regions. Meanwhile, this study also showed whether the STN could overcome the challenges of model rationality and practical needs.

Methods

The construction of the STN framework involved three major steps: the spatial kluster analysis by tree edge removal (SKATER) algorithm, structure learning by dynamic Bayesian network (DBN), and parameter learning by the vector autoregressive moving average (VARMA) model. Then, we evaluated the forecasting performance of STN by comparing its accuracy with that of the mechanism models like susceptible-exposed-infectious-recovered-susceptible (SEIRS) and machine-learning algorithm like long-short-term memory (LSTM). At the same time, we assessed the robustness of forecasting performance of STN in high and low incidence seasons. The influenza-like illness (ILI) data in the Sichuan Province of China from 2010 to 2017 were used as an example for illustration.

Results

The STN model revealed that ILI was likely to spread among multiple cities in Sichuan during the study period. During the whole study period, the forecasting accuracy of the STN (mean absolute percentage error [MAPE] = 31.134) was significantly better than that of the LSTM (MAPE = 41.657) and the SEIRS (MAPE = 62.039). In addition, the forecasting performance of STN was also superior to those of the other two methods in either the high incidence season (MAPE = 24.742) or the low incidence season (MAPE = 26.209), and the superiority was more obvious in the high incidence season.

Conclusion

This study applied the STN to the forecast of infectious diseases across multiple regions. The results illustrated that the STN not only had good accuracy in forecasting performance but also indicated the spreading directions of infectious diseases among multiple regions to a certain extent. Therefore, the STN is a promising candidate to improve the surveillance work.

Development and assessment of a hospital admissions-based syndromic surveillance system for COVID-19 in Ontario, Canada: ACES Pandemic Tracker

BACKGROUND

The COVID-19 pandemic has continued to pose a major global public health risk. The importance of public health surveillance systems to monitor the spread and impact of COVID-19 has been well demonstrated. The purpose of this study was to describe the development and effectiveness of a real-time public health syndromic surveillance system (ACES Pandemic Tracker) as an early warning system and to provide situational awareness in response to the COVID-19 pandemic in Ontario, Canada.

METHODS

We used hospital admissions data from the Acute Care Enhanced Surveillance (ACES) system to collect data on pre-defined groupings of symptoms (syndromes of interest; SOI) that may be related to COVID-19 from 131 hospitals across Ontario. To evaluate which SOI for suspected COVID-19 admissions were best correlated with laboratory confirmed admissions, laboratory confirmed COVID-19 hospital admissions data were collected from the Ontario Ministry of Health. Correlations and time-series lag analysis between suspected and confirmed COVID-19 hospital admissions were calculated. Data used for analyses covered the period between March 1, 2020 and September 21, 2020.

RESULTS

Between March 1, 2020 and September 21, 2020, ACES Pandemic Tracker identified 22,075 suspected COVID-19 hospital admissions (150 per 100,000 population) in Ontario. After correlation analysis, we found laboratory-confirmed hospital admissions for COVID-19 were strongly and significantly correlated with suspected COVID-19 hospital admissions when SOI were included (Spearman's rho = 0.617) and suspected COVID-19 admissions when SOI were excluded (Spearman's rho = 0.867). Weak to moderate significant correlations were found among individual SOI. Laboratory confirmed COVID-19 hospital admissions lagged in reporting by 3 days compared with suspected COVID-19 admissions when SOI were excluded.

CONCLUSIONS

Our results demonstrate the utility of a hospital admissions syndromic surveillance system to monitor and identify potential surges in severe COVID-19 infection within the community in a timely manner and provide situational awareness to inform preventive and preparatory health interventions.

Burden of preschool wheeze and progression to asthma in the UK: Population-based cohort 2007 to 2017

BACKGROUND

Wheeze is one of the most common symptoms of preschool children (age 1-5 years), yet we have little understanding of the burden in the United Kingdom.

OBJECTIVES

We sought to determine prevalence and pattern of physician-confirmed preschool wheeze, related health care utilization, and factors associated with progression to school-age asthma.

METHODS

We used nationally representative primary and secondary care electronic medical records between 2007 and 2017 to identify preschool children with wheeze. Factors associated with asthma progression were identified in a nested cohort of children with follow-up from age 1 to 2 years, until at least age 8 years.

RESULTS

From 1,021,624 preschool children, 69,261 were identified with wheeze. Prevalence of preschool wheeze was 7.7% in 2017. Wheeze events were lowest in August and highest in late-autumn/early-winter. During median follow-up of 2 years (interquartile range, 1.2-4.0 years), 15.8% attended an emergency department, and 13.9% had a hospital admission, for a respiratory disorder. The nested cohort with prolonged follow-up identified 15,085 children; 35.5% progressed to asthma between age 5 and 8 years. Of children with preschool wheeze, without an asthma diagnosis, 34.9% were prescribed inhaled corticosteroids and 15.6% oral corticosteroids. The factors most strongly associated with progression to asthma were wheeze frequency and severity, atopy, prematurity, maternal asthma severity, and first reported wheeze event occurring in September.

CONCLUSIONS

Preschool wheeze causes considerable health care burden, and a large number of children are prescribed asthma medication and have unplanned secondary care visits. Multiple factors influence progression to asthma, including first wheeze event occurring in September.

Time Distributions of Common Respiratory Pathogens Under the Spread of SARS-CoV-2 Among Children in Xiamen, China

Objectives: The aim of this study was to observe the effect of COVID-19 prevention and control measures on the transmission of common respiratory viruses in a pediatric population. Methods: This was a retrospective observational study. The study population was selected from children with respiratory diseases who attended Xiamen Children's Hospital from January 1, 2018 to January 31, 2021. All children were screened for influenza virus, parainfluenza virus, respiratory syncytial virus (RSV), adenovirus, and Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The changes in respiratory virus detection rates before and after the SARS-CoV-2 intervention were analyzed using an interrupted time-series model. Polynomial curve fitting was also used to predict future short-term trends in respiratory virus detection. Results: A total of 56,859 children were seen at Xiamen Children's Hospital from January 1, 2018 to Jan 31, 2021, of which 32,120 were tested for respiratory viruses via pharyngeal swabs. The overall positive detection rates of the four respiratory viral infections decreased significantly (P = 0.0017) after the implementation of the quarantine and school suspension measures in January 2020. Among them, the detection rate of RSV decreased most significantly (P = 0.008), and although there was no statistically significant difference in the detection rates of the influenza virus, parainfluenza virus, and adenovirus, a downward trend in the graph was observed. The positive detection rates of RSV in the 0-1-, 1-3-, and 3-7-year-old groups all decreased significantly (P = 0.035, 0.016, and 0.038, respectively). The change in the positive detection rate of RSV was relatively stable in the 7-18-year-old group. A total of 10,496 samples were tested for SARS-CoV-2, and no positive cases were reported. Conclusions: The combination of preventive and control measures for COVID-19 reduced the detection rate of four common respiratory viruses, with the greatest impact on RSV. If prevention and control measures continue to be maintained, the overall detection rate or absolute number of detections for the four respiratory viruses will remain low in the short term. However, this trend is likely to vary with the changes in measures.

The COVID-19 pandemic: a new challenge for syndromic surveillance

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