Harmonizing influenza primary-care surveillance in the United Kingdom: piloting two methods to assess the timing and intensity of the seasonal epidemic across several general practice-based surveillance schemes

Green, H. K.; Charlett, André; Moran-Gilad, Jacob; Fleming, Douglas; Durnall, H.; Thomas, Daniel; Cottrell, Simon; Smyth, B.; Kearns, Cathriona; Reynolds, Arlene; Smith, Gillian; Elliot, Alex J; Ellis, Joanna; Zambon, Maria; Watson, John M.; McMenamin, Jim; Pebody, Richard

doi:10.1017/s0950268814001757

Cited by 40 publications

(42 citation statements)

References 11 publications

(17 reference statements)

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“…These systems are well‐characterised and have previously been analysed for spatiotemporal biases by our group 40. We are in the process of developing observation models for these and other systems, which will allow us to compare the forecasting quality obtained from each system.…”

Section: Discussionmentioning

confidence: 99%

Forecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data

Moss

Zarebski

Dawson

et al. 2016

Influenza Resp Viruses

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BackgroundAccurate forecasting of seasonal influenza epidemics is of great concern to healthcare providers in temperate climates, as these epidemics vary substantially in their size, timing and duration from year to year, making it a challenge to deliver timely and proportionate responses. Previous studies have shown that Bayesian estimation techniques can accurately predict when an influenza epidemic will peak many weeks in advance, using existing surveillance data, but these methods must be tailored both to the target population and to the surveillance system.ObjectivesOur aim was to evaluate whether forecasts of similar accuracy could be obtained for metropolitan Melbourne (Australia).MethodsWe used the bootstrap particle filter and a mechanistic infection model to generate epidemic forecasts for metropolitan Melbourne (Australia) from weekly Internet search query surveillance data reported by Google Flu Trends for 2006–14.Results and ConclusionsOptimal observation models were selected from hundreds of candidates using a novel approach that treats forecasts akin to receiver operating characteristic (ROC) curves. We show that the timing of the epidemic peak can be accurately predicted 4–6 weeks in advance, but that the magnitude of the epidemic peak and the overall burden are much harder to predict. We then discuss how the infection and observation models and the filtering process may be refined to improve forecast robustness, thereby improving the utility of these methods for healthcare decision support.

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Section: Discussionmentioning

confidence: 99%

Forecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data

Moss

Zarebski

Dawson

et al. 2016

Influenza Resp Viruses

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“…Primary care is the setting of choice for influenza surveillance in temperate countries, and this study demonstrates the feasibility of conducting primary care surveillance in a low middle income country across public and private providers. Recruitment at the hospital clinics was limited to daytime hours (8 am and 4 pm ), whereas private clinics were able to recruit patients in the evening.…”

Section: Discussionmentioning

confidence: 86%

Primary care influenza‐like illness surveillance in Ho Chi Minh City, Vietnam 2013‐2015

Todd

Hường

Thanh

et al. 2018

Influenza Resp Viruses

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BackgroundYear‐round transmission of influenza has been detected in Vietnam through both national surveillance and other epidemiological studies. Understanding the demographic and clinical features of influenza‐like illness (ILI) presenting to primary care in urban Vietnam is vital to understand these transmission dynamics.MethodsAn observational study of patients with ILI in Ho Chi Minh City, Vietnam, was conducted between August 2013 and November 2015 in a mix of public and private primary care settings. Molecular testing for influenza A and influenza B and 12 other respiratory viruses was performed.ResultsA total of 1152 ILI patients were recruited. 322 and 136 subjects tested positive for influenza A and influenza B, respectively. 193 subjects tested positive for another respiratory virus; most commonly rhinovirus and parainfluenza virus 3. Influenza was detected in 81% of the 116 study weeks. Three peaks of influenza activity were detected; an H3N2 peak April‐June 2014, an influenza B peak July‐December 2014, and a mixed H3N2 and H1N1 peak March‐September 2015. Subjects recruited from private clinics were more likely to have higher income and to have reported previous influenza vaccination. Antibiotic use was common (50.3%) despite limited evidence of bacterial infection.ConclusionInfluenza in southern Vietnam has complex transmission dynamics including periods of intense influenza activity of alternating types and subtypes. Broadening surveillance from hospital to the community in tropical settings is feasible and a valuable for improving our understanding of the global spread and evolution of the virus.

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“…For this purpose, a simple method proposed by the WHO was used (21)(22)(23). WHO is now recommending MEM, which is a more sophisticated method of reporting influenza activity adopted by the European Centre for Disease Prevention and Control (40)(41)(42)(43) and adopted by several countries from other regions (34,44). The analysis using the MEM application witheleven seasons of syndromic surveillance data showed clear seasonality toILI activity and visual inspection of graphed data revealed a single seasonal peak per year.…”

Section: Discussionmentioning

confidence: 99%

Establishing seasonal and alert influenza thresholds in Morocco

Rguig

Cherkaoui²,

McCarron³

et al. 2020

Preprint

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Abstract Background: Several statistical methods of variable complexity have been developed to establish thresholds for influenza activity to inform public health guidance. We compared the results of two methods and explored how they worked to characterize the 2017-2018 season.Methods: Historical data from the 2005/2006 to 2016/2017 seasons were provided by a network of 412 primary health centers in charge of influenza like illness (ILI) sentinel surveillance. We used the WHO averages and the moving epidemic method (MEM) to evaluate the proportion of ILI visits among all outpatient consultations (ILI%) as a proxy for influenza activity. We also used the MEM method to evaluate three seasons of composite data (ILI% multiplied by percent of ILI with laboratory-confirmed influenza) as recommended by WHO.Results: The WHO method estimated the seasonal ILI% threshold at 0.9%. The annual epidemic period begins on average at week 46 and lasts an average of 18 weeks. The MEM model estimated the epidemic threshold (corresponding to the WHO seasonal threshold) at 1.5% of ILI visits among all consultations. The annual epidemic period began on week 49 and lasted on average 14 weeks. Intensity thresholds were similar using both methods. When using the composite measure, the MEM method showed a clearer estimate of the beginning of the influenza epidemic, which was coincident with a sharp increase in confirmed ILI cases.Conclusions: We found that the threshold methodology presented in the WHO manual is simple to implement and easy to adopt for use by the Moroccan influenza surveillance system. The MEM method is more statistically sophisticated and may allow a better detection of the start of seasonal epidemics. Incorporation of virologic data into the composite parameter as recommended by WHO has the potential to increase the accuracy of seasonal threshold estimation.

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Harmonizing influenza primary-care surveillance in the United Kingdom: piloting two methods to assess the timing and intensity of the seasonal epidemic across several general practice-based surveillance schemes

Cited by 40 publications

References 11 publications

Forecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data

Forecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data

Primary care influenza‐like illness surveillance in Ho Chi Minh City, Vietnam 2013‐2015

Establishing seasonal and alert influenza thresholds in Morocco

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