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Characterising seasonal influenza epidemiology using primary care surveillance data.

ABSTRACT: Understanding the epidemiology of seasonal influenza is critical for healthcare resource allocation and early detection of anomalous seasons. It can be challenging to obtain high-quality data of influenza cases specifically, as clinical presentations with influenza-like symptoms may instead be cases of one of a number of alternate respiratory viruses. We use a new dataset of confirmed influenza virological data from 2011-2016, along with high-quality denominators informing a hierarchical observation process, to model seasonal influenza dynamics in New South Wales, Australia. We use approximate Bayesian computation to estimate parameters in a climate-driven stochastic epidemic model, including the basic reproduction number R0, the proportion of the population susceptible to the circulating strain at the beginning of the season, and the probability an infected individual seeks treatment. We conclude that R0 and initial population susceptibility were strongly related, emphasising the challenges of identifying these parameters. Relatively high R0 values alongside low initial population susceptibility were among the results most consistent with these data. Our results reinforce the importance of distinguishing between R0 and the effective reproduction number (Re) in modelling studies.

SUBMITTER: Cope RC

PROVIDER: S-EPMC6112683 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Characterising seasonal influenza epidemiology using primary care surveillance data.

Cope Robert C RC Ross Joshua V JV Chilver Monique M Stocks Nigel P NP Mitchell Lewis L

PLoS computational biology 20180816 8

Understanding the epidemiology of seasonal influenza is critical for healthcare resource allocation and early detection of anomalous seasons. It can be challenging to obtain high-quality data of influenza cases specifically, as clinical presentations with influenza-like symptoms may instead be cases of one of a number of alternate respiratory viruses. We use a new dataset of confirmed influenza virological data from 2011-2016, along with high-quality denominators informing a hierarchical observa ...[more]

PMID: 30114215

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Project description:IntroductionThe European Medicines Agency (EMA) requires vaccine manufacturers to conduct enhanced safety surveillance (ESS) of seasonal influenza vaccines including a near real-time evaluation of collected data. The objective was to identify whether the use of passive surveillance or active surveillance provides different results of reported adverse events of interest (AEIs) by specified age strata and AEI type. We report the weekly incidence rates of AEIs within 7 days following seasonal influenza vaccination using passive and active surveillance.MethodsAEIs were collected within 7 days of vaccination from ten general practices predominantly administering inactivated quadrivalent influenza vaccine (IIV4, Fluarix Tetra, GSK). Vaccinees completed an adverse drug reaction (ADR) card. ADR card and medically attended AEIs data were recorded in practice electronic health records. We report the outcome of the first 5 weeks of safety surveillance (September 12, 2016-October 16, 2016); in an exploratory analysis, rates of AEI for IIV4 are compared to those passively reported through a sentinel network.ResultsPractices vaccinated 13.1% (12,864/98,091) of their registered population; 5.6% (95% CI 5.20-6.00) of them reported AEIs, none serious. The most frequent were respiratory 2.60% (95% CI 2.33-2.88), musculoskeletal 1.82% (95% CI 1.59-2.05) and neurological 1.05% (95% CI 0.88-1.23). AEIs were more frequently reported for adults than for children; 5.91% (95% CI 5.49-6.34) compared to 1.49% (95% CI 0.69-2.29); 47.18% of the adults reported AEI using the ADR card, none were returned for subjects < 18 years old. The frequency of AEIs reporting was higher, 6.88% (95% CI 6.35-7.42) vs. 3.30% (95% CI 2.68-3.96, 100/3028, p < 0.000), through ESS than passive surveillance.ConclusionThe ESS did not reveal any safety signal and we demonstrated the feasibility of conducting ESS following EMA recommendations. The use of a customised ADR card led to a doubling of AEIs reports over passive surveillance in adults.FundingGlaxoSmithKline Biologicals SA, Wavre, Belgium.

Project description:BackgroundUptake of medicinal drugs (preventive or treatment) is among the approaches used to control disease outbreaks, and therefore, it is of vital importance to be aware of the counts or frequencies of most commonly used drugs and trending topics about these drugs from consumers for successful implementation of control measures. Traditional survey methods would have accomplished this study, but they are too costly in terms of resources needed, and they are subject to social desirability bias for topics discovery. Hence, there is a need to use alternative efficient means such as Twitter data and machine learning (ML) techniques.ObjectiveUsing Twitter data, the aim of the study was to (1) provide a methodological extension for efficiently extracting widely consumed drugs during seasonal influenza and (2) extract topics from the tweets of these drugs and to infer how the insights provided by these topics can enhance seasonal influenza surveillance.MethodsFrom tweets collected during the 2012-13 flu season, we first identified tweets with mentions of drugs and then constructed an ML classifier using dependency words as features. The classifier was used to extract tweets that evidenced consumption of drugs, out of which we identified the mostly consumed drugs. Finally, we extracted trending topics from each of these widely used drugs' tweets using latent Dirichlet allocation (LDA).ResultsOur proposed classifier obtained an F1 score of 0.82, which significantly outperformed the two benchmark classifiers (ie, P<.001 with the lexicon-based and P=.048 with the 1-gram term frequency [TF]). The classifier extracted 40,428 tweets that evidenced consumption of drugs out of 50,828 tweets with mentions of drugs. The most widely consumed drugs were influenza virus vaccines that had around 76.95% (31,111/40,428) share of the total; other notable drugs were Theraflu, DayQuil, NyQuil, vitamins, acetaminophen, and oseltamivir. The topics of each of these drugs exhibited common themes or experiences from people who have consumed these drugs. Among these were the enabling and deterrent factors to influenza drugs uptake, which are keys to mitigating the severity of seasonal influenza outbreaks.ConclusionsThe study results showed the feasibility of using tweets of widely consumed drugs to enhance seasonal influenza surveillance in lieu of the traditional or conventional surveillance approaches. Public health officials and other stakeholders can benefit from the findings of this study, especially in enhancing strategies for mitigating the severity of seasonal influenza outbreaks. The proposed methods can be extended to the outbreaks of other diseases.

Project description:IntroductionThe increased availability of influenza surveillance data in recent years justifies an actual and more complete overview of influenza epidemiology in Latin America. We compared the influenza surveillance systems and assessed the epidemiology of influenza A and B, including the spatio-temporal patterns of influenza epidemics, in ten countries and sub-national regions in Latin America.MethodsWe aggregated the data by year and country and characteristics of eighty-two years were analysed. We calculated the median proportion of laboratory-confirmed influenza cases caused by each virus strain, and compared the timing and amplitude of the primary and secondary peaks between countries.Results37,087 influenza cases were reported during 2004-2012. Influenza A and B accounted for a median of 79% and, respectively, 21% of cases in a year. The percentage of influenza A cases that were subtyped was 82.5%; for influenza B, 15.6% of cases were characterized. Influenza A and B were dominant in seventy-five (91%) and seven (9%) years, respectively. In half (51%) of the influenza A years, influenza A(H3N2) was dominant, followed by influenza A(H1N1)pdm2009 (41%) and pre-pandemic A(H1N1) (8%). The primary peak of influenza activity was in June-September in temperate climate countries, with little or no secondary peak. Tropical climate countries had smaller primary peaks taking place in different months and frequently detectable secondary peaks.ConclusionsWe found that good influenza surveillance data exists in Latin America, although improvements can still be made (e.g. a better characterization of influenza B specimens); that influenza B plays a considerable role in the seasonal influenza burden; and that there is substantial heterogeneity of spatio-temporal patterns of influenza epidemics. To improve the effectiveness of influenza control measures in Latin America, tropical climate countries may need to develop innovative prevention strategies specifically tailored to the spatio-temporal patterns of influenza in this region.

Project description:BACKGROUND:School absenteeism data have been collected daily by the public health unit in Wellington-Dufferin-Guelph, Ontario since 2008. To date, a threshold-based approach has been implemented to raise alerts for community-wide and within-school illness outbreaks. We investigate several statistical modelling approaches to using school absenteeism for influenza surveillance at the regional level, and compare their performances using two metrics. METHODS:Daily absenteeism percentages from elementary and secondary schools, and report dates for influenza cases, were obtained from Wellington-Dufferin-Guelph Public Health. Several absenteeism data aggregations were explored, including using the average across all schools or only using schools of one type. A 10% absence threshold, exponentially weighted moving average model, logistic regression with and without seasonality terms, day of week indicators, and random intercepts for school year, and generalized estimating equations were used as epidemic detection methods for seasonal influenza. In the regression models, absenteeism data with various lags were used as predictor variables, and missing values in the datasets used for parameter estimation were handled either by deletion or linear interpolation. The epidemic detection methods were compared using a false alarm rate (FAR) as well as a metric for alarm timeliness. RESULTS:All model-based epidemic detection methods were found to decrease the FAR when compared to the 10% absence threshold. Regression models outperformed the exponentially weighted moving average model and including seasonality terms and a random intercept for school year generally resulted in fewer false alarms. The best-performing model, a seasonal logistic regression model with random intercept for school year and a day of week indicator where parameters were estimated using absenteeism data that had missing values linearly interpolated, produced a FAR of 0.299, compared to the pre-existing threshold method which at best gave a FAR of 0.827. CONCLUSIONS:School absenteeism can be a useful tool for alerting public health to upcoming influenza epidemics in Wellington-Dufferin-Guelph. Logistic regression with seasonality terms and a random intercept for school year was effective at maximizing true alarms while minimizing false alarms on historical data from this region.

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Characterising seasonal influenza epidemiology using primary care surveillance data.

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Characterising seasonal influenza epidemiology using primary care surveillance data.

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