Dataset Information

Using web search queries to monitor influenza-like illness: an exploratory retrospective analysis, Netherlands, 2017/18 influenza season.

ABSTRACT: BackgroundDespite the early development of Google Flu Trends in 2009, standards for digital epidemiology methods have not been established and research from European countries is scarce.AimIn this article, we study the use of web search queries to monitor influenza-like illness (ILI) rates in the Netherlands in real time.MethodsIn this retrospective analysis, we simulated the weekly use of a prediction model for estimating the then-current ILI incidence across the 2017/18 influenza season solely based on Google search query data. We used weekly ILI data as reported to The European Surveillance System (TESSY) each week, and we removed the then-last 4 weeks from our dataset. We then fitted a prediction model based on the then-most-recent search query data from Google Trends to fill the 4-week gap ('Nowcasting'). Lasso regression, in combination with cross-validation, was applied to select predictors and to fit the 52 models, one for each week of the season.ResultsThe models provided accurate predictions with a mean and maximum absolute error of 1.40 (95% confidence interval: 1.09-1.75) and 6.36 per 10,000 population. The onset, peak and end of the epidemic were predicted with an error of 1, 3 and 2 weeks, respectively. The number of search terms retained as predictors ranged from three to five, with one keyword, 'griep' ('flu'), having the most weight in all models.DiscussionThis study demonstrates the feasibility of accurate, real-time ILI incidence predictions in the Netherlands using Google search query data.

SUBMITTER: Schneider PP

PROVIDER: S-EPMC7268271 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Using web search queries to monitor influenza-like illness: an exploratory retrospective analysis, Netherlands, 2017/18 influenza season.

Schneider Paul P PP van Gool Christel Jaw CJ Spreeuwenberg Peter P Hooiveld Mariëtte M Donker Gé A GA Barnett David J DJ Paget John J

Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin 20200501 21

BackgroundDespite the early development of Google Flu Trends in 2009, standards for digital epidemiology methods have not been established and research from European countries is scarce.AimIn this article, we study the use of web search queries to monitor influenza-like illness (ILI) rates in the Netherlands in real time.MethodsIn this retrospective analysis, we simulated the weekly use of a prediction model for estimating the then-current ILI incidence across the 2017/18 influenza season solely ...[more]

PMID: 32489174

Similar Datasets

Project description:BackgroundSeasonal influenza causes a considerable burden to healthcare services every year. To better measure the impact of severe influenza cases in Romania, we analyzed active surveillance data collected during the 2017-2018 season from patients admitted for influenza-like illness (ILI) at a tertiary care hospital in Bucharest.MethodsPatients admitted for acute ILI were included if they were resident in the Bucharest-Ilfov region, had been hospitalized for at least 24?h, and had onset of symptoms within 7?days before admission. Patient demographics, healthcare use, vaccination status, and outcome data were collected by questionnaire or by searching clinical records. Respiratory swabs were also obtained from each patient to confirm influenza A (A/H1 and A/H3 subtypes) or influenza B (Yamagata and Victoria lineages) infection by real-time reverse-transcription polymerase chain reaction assay.ResultsThe study included 502 patients, many (45.2%) of whom were aged <?5?years. Overall, 108 patients (21.5%) had one or more comorbidities. Seventeen adults aged 18-64?years (3.4%) had been vaccinated against influenza. Patients were hospitalized for a median of 5?days and most (90.4%) were prescribed antiviral treatment. More than one-half of the patients (n?=?259, 51.6%) were positive for influenza. Most influenza cases were caused by B viruses (172/259, 66.4%), which were mostly of the B/Yamagata lineage (85 of 94 characterized, 90.4%). Most of the subtyped A viruses were A/H1 (59/74, 79.7%). A/H1 viruses were frequently detected in influenza-positive admissions throughout the 2017-2018 season, whereas the predominant B/Yamagata viruses were detected around the middle of the season, with a peak in cases at week 7 of 2018. Eleven patients were admitted to an intensive care unit; of these, one patient with confirmed B/Yamagata infection died.ConclusionsThese results show that seasonal influenza results in considerable hospitalization in Bucharest-Ilfov, Romania and suggest vaccine coverage should be extended, especially to the youngest age groups. The data from this study should help inform and optimize national influenza healthcare policies.

Project description:BACKGROUND:Influenza generates a significant societal impact on morbidity, mortality, and associated costs. The study objective was to identify factors associated with influenza-like-illness (ILI) episodes during seasonal influenza epidemics among the general population. METHODS:A prospective study was conducted with the GrippeNet.fr crowdsourced cohort between 2012/13 and 2017/18. After having completed a yearly profile survey detailing socio-demographic, lifestyle and health characteristics, participants reported weekly data on symptoms. Factors associated with at least one ILI episode per influenza epidemic, using the European Centre for Disease Prevention and Control case definition, were analyzed through a conditional logistic regression model. RESULTS:From 2012/13 to 2017/18, 6992 individuals participated at least once, and 61% of them were women (n = 4258). From 11% (n = 469/4140 in 2013/14) to 29% (n = 866/2943 in 2012/13) of individuals experienced at least one ILI during an influenza epidemic. Factors associated with higher risk for ILI were: gender female (OR = 1.29, 95%CI [1.20; 1.40]), young age (< 5 years old: 3.12 [2.05; 4.68]); from 5 to 14 years old: 1.53 [1.17; 2.00]), respiratory allergies (1.27 [1.18; 1.37]), receiving a treatment for chronic disease (1.20 [1.09; 1.32]), being overweight (1.18 [1.08; 1.29]) or obese (1.28 [1.14; 1.44]), using public transport (1.17 [1.07; 1.29]) and having contact with pets (1.18 [1.09; 1.27]). Older age (≥ 75 years old: 0.70 [0.56; 0.87]) and being vaccinated against influenza (0.91 [0.84; 0.99]) were found to be protective factors for ILI. CONCLUSIONS:This ILI risk factors analysis confirms and further completes the list of factors observed through traditional surveillance systems. It indicates that crowdsourced cohorts are effective to study ILI determinants at the population level. These findings could be used to adapt influenza prevention messages at the population level to reduce the spread of the disease.

Project description:BackgroundThe objective of this study is to investigate predictive utility of online social media and web search queries, particularly, Google search data, to forecast new cases of influenza-like-illness (ILI) in general outpatient clinics (GOPC) in Hong Kong. To mitigate the impact of sensitivity to self-excitement (i.e., fickle media interest) and other artifacts of online social media data, in our approach we fuse multiple offline and online data sources.MethodsFour individual models: generalized linear model (GLM), least absolute shrinkage and selection operator (LASSO), autoregressive integrated moving average (ARIMA), and deep learning (DL) with Feedforward Neural Networks (FNN) are employed to forecast ILI-GOPC both one week and two weeks in advance. The covariates include Google search queries, meteorological data, and previously recorded offline ILI. To our knowledge, this is the first study that introduces deep learning methodology into surveillance of infectious diseases and investigates its predictive utility. Furthermore, to exploit the strength from each individual forecasting models, we use statistical model fusion, using Bayesian model averaging (BMA), which allows a systematic integration of multiple forecast scenarios. For each model, an adaptive approach is used to capture the recent relationship between ILI and covariates.ResultsDL with FNN appears to deliver the most competitive predictive performance among the four considered individual models. Combing all four models in a comprehensive BMA framework allows to further improve such predictive evaluation metrics as root mean squared error (RMSE) and mean absolute predictive error (MAPE). Nevertheless, DL with FNN remains the preferred method for predicting locations of influenza peaks.ConclusionsThe proposed approach can be viewed a feasible alternative to forecast ILI in Hong Kong or other countries where ILI has no constant seasonal trend and influenza data resources are limited. The proposed methodology is easily tractable and computationally efficient.

Dataset Information

Using web search queries to monitor influenza-like illness: an exploratory retrospective analysis, Netherlands, 2017/18 influenza season.

Publications

Using web search queries to monitor influenza-like illness: an exploratory retrospective analysis, Netherlands, 2017/18 influenza season.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets