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

Harcourt, Sally; Morbey, Roger; Smith, Gillian; Loveridge, Paul; Green, H K; Pebody, Richard; Rutter, Judith; Yeates, F A; Stuttard, Gareth; Elliot, Alex J

doi:10.1017/s0950268819000542

Cited by 10 publications

(10 citation statements)

References 33 publications

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“…In addition, RSV infection in children can place a burden on health services each winter (15). Therefore, a study has evaluated the effectiveness of various measures to decrease the positive rate of RSV (16). Our study indicated that the positive rate of RSV could be decreased with strict quarantine, face masks, and limitation of interactive and school activities.…”

Section: Discussionmentioning

confidence: 85%

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

Wang

Xiao

et al. 2021

Front. Pediatr.

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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.

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Section: Discussionmentioning

confidence: 85%

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

Wang

Xiao

et al. 2021

Front. Pediatr.

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“…The immediate and obvious challenges will be those that we will face during winter 2020/2021. Syndromic surveillance data contribute to the annual programme of influenza and other respiratory pathogens [18], with indicators that are sensitive to, and successfully monitor community-based influenza activity [9, 19, 20]. Indeed, these indicators can also be used to identify and monitor trends in other specific respiratory pathogens e.g.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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Abstract 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.

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“…The identification of patients with ILI is based on the standard case definition of the WHO, that is, body temperature ≥38°C with either cough or sore throat, in the absence of an alternative diagnosis ( 17 ). Besides, the ILI% was widely used in influenza surveillance to reflect the local intensity of the influenza epidemic, where ILI% = (the number of ILI cases in outpatient and emergency departments)/(the total number of outpatient and emergency cases) ×100% ( 18 , 19 ). Taking n places as an example, x 1 ( t ), x 2 ( t ), ⋯ , x n ( t ) denoted the ILI% values of the place n at the t -th week.…”

Section: Methodsmentioning

confidence: 99%

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

Wang

Qiu

et al. 2022

Front. Public Health

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ObjectiveTimely 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.MethodsThe 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.ResultsThe 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.ConclusionThis 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.

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

Cited by 10 publications

References 33 publications

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

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

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

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

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