Accurate estimation of influenza epidemics using Google search data via ARGO

Yang, Shihao; Santillana, Mauricio; Kou, S. C.

doi:10.1073/pnas.1515373112

Cited by 362 publications

(472 citation statements)

References 42 publications

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“…Many studies have assessed the use of internet-user activity data because they can produce real-time indicators [10][11][12][13][14][15][16][17][18]. Several data sources have been explored, including Wikipedia, Twitter or Google search-engine data.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…As a result, GFT is currently closed to the public. GFT appeared to be sensitive to uncommon flu epidemics, to media coverage, to changes in the internet users' habits and to modifications of the algorithm in the Google search engine [11,20]. Consequently, other studies proposed to combine traditional surveillance systems and web data, to benefit from the advantages of both systems.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…CDC ILI activity data with a dynamic statistical model (least absolute shrinkage and selection operator, LASSO) to weekly redefine the best predictors for the current week and readjust their coefficients [11]. This model seems very promising because it can produce near real-time flu activity indexes that are very accurate compared with those produced by the CDC, with a correlation coefficient of predicted values for the flu seasons of the 2010-2014 period ranging from 0.928 to 0.993.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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Leveraging hospital big data to monitor flu epidemics

Bouzillé

Poirier

Campillo‐Gimenez

et al. 2018

Computer Methods and Programs in Biomedicine

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Background and ObjectiveInfluenza epidemics are a major public health concern and require a costly and time-consuming surveillance system at different geographical scales. The main challenge is being able to predict epidemics. Besides traditional surveillance systems, such as the French Sentinel network, several studies proposed prediction models based on internet-user activity. Here, we assessed the potential of hospital big data to monitor influenza epidemics. MethodsWe used the clinical data warehouse of the Academic Hospital of Rennes (France) and then built different queries to retrieve relevant information from electronic health records to gather weekly influenza-like illness activity. ACCEPTED MANUSCRIPT A C C E P T E D M A N U S C R I P T ResultsWe found that the query most highly correlated with Sentinel network estimates was based on emergency reports concerning discharged patients with a final diagnosis of influenza (Pearson's correlation coefficient (PCC) of 0.931). The other tested queries were based on structured data (ICD-10 codes of influenza in Diagnosis-related Groups, and influenza PCR tests) and performed best (PCC of 0.981 and 0.953, respectively) during the flu season 2014-15. This suggests that both ICD-10 codes and PCR results are associated with severe epidemics. Finally, our approach allowed us to obtain additional patients' characteristics, such as the sex ratio or age groups, comparable with those from the Sentinel network. ConclusionsHospital big data seem to have a great potential for monitoring influenza epidemics in near realtime. Such a method could constitute a complementary tool to standard surveillance systems by providing additional characteristics on the concerned population or by providing information earlier. This system could also be easily extended to other diseases with possible activity changes.Additional work is needed to assess the real efficacy of predictive models based on hospital big data to predict flu epidemics.

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Section: Accepted Manuscriptmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

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Leveraging hospital big data to monitor flu epidemics

Bouzillé

Poirier

Campillo‐Gimenez

et al. 2018

Computer Methods and Programs in Biomedicine

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“…Outbreaks cause up to 5 million severe cases and 500,000 deaths per year worldwide. [1][2][3][4][5] During influenza peaks, the large increase of visits to general practitioners and to emergency departments causes healthcare system disruption. To reduce its impact and to help organizing adapted sanitary responses, it is necessary to monitor influenza-like illness (ILI; any acute respiratory infection with fever ≥ 38 °C, cough and onset within the last 10 days) activity.…”

Section: Introduction Backgroundmentioning

confidence: 99%

“…This time lag is an issue for public health decision-making. [2,7] Therefore, there is a growing interest in finding ways to avoid this information gap. Nsoesie et al reviewed methods for influenza forecasting, including temporal series and compartmental methods.…”

Section: Introduction Backgroundmentioning

confidence: 99%

Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study (Preprint)

Poirier¹,

Lavenu²,

Bertaud³

et al. 2018

Preprint

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Background Traditional surveillance systems produce estimates of influenza-like illness (ILI) incidence rates, but with one-to three-week delay. Accurate real-time monitoring systems of influenza outbreaks could be useful for public health decisions. Several works have investigated the possibility to use internet-users' activity data and different statistical models to predict influenza epidemics in near real-time. However, very few studies have investigated hospital big data. Objective Here, we compared internet and electronic health records (EHR) data and different statistical models to identify the best approach (data type and statistical model) for ILI estimates in real time. Methods We used Google Data for internet data and the clinical data warehouse eHOP, that includes all EHR from Rennes University Hospital (France), for hospital data. We compared three statistical models, Random Forest (RF), Elastic Net, and Support Vector Machine (SVM).

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Estimating Weekly National Opioid Overdose Deaths in Near Real Time Using Multiple Proxy Data Sources

et al. 2022

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IMPORTANCEOpioid overdose is a leading public health problem in the United States; however, national data on overdose deaths are delayed by several months or more. OBJECTIVES To build and validate a statistical model for estimating national opioid overdose deaths in near real time. DESIGN, SETTING, AND PARTICIPANTS In this cross-sectional study, signals from 5 overdoserelated, proxy data sources encompassing health, law enforcement, and online data from 2014 to 2019 in the US were combined using a LASSO (least absolute shrinkage and selection operator) regression model, and weekly predictions of opioid overdose deaths were made for 2018 and 2019 to validate model performance. Results were also compared with those from a baseline SARIMA (seasonal autoregressive integrated moving average) model, one of the most used approaches to forecasting injury mortality. EXPOSURES Time series data from 2014 to 2019 on emergency department visits for opioid overdose from the National Syndromic Surveillance Program, data on the volume of heroin and synthetic opioids circulating in illicit markets via the National Forensic Laboratory Information System, data on the search volume for heroin and synthetic opioids on Google, and data on post volume on heroin and synthetic opioids on Twitter and Reddit were used to train and validate prediction models of opioid overdose deaths. MAIN OUTCOMES AND MEASURES Model-based predictions of weekly opioid overdose deaths in the United States were made for 2018 and 2019 and compared with actual observed opioid overdose deaths from the National Vital Statistics System. RESULTSStatistical models using the 5 real-time proxy data sources estimated the national opioid overdose death rate for 2018 and 2019 with an error of 1.01% and −1.05%, respectively. When considering the accuracy of weekly predictions, the machine learning-based approach possessed a mean error in its weekly estimates (root mean squared error) of 60.3 overdose deaths for 2018 (compared with 310.2 overdose deaths for the SARIMA model) and 67.2 overdose deaths for 2019 (compared with 83.3 overdose deaths for the SARIMA model). CONCLUSIONS AND RELEVANCEResults of this serial cross-sectional study suggest that proxy administrative data sources can be used to estimate national opioid overdose mortality trends to provide a more timely understanding of this public health problem.

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Accurate estimation of influenza epidemics using Google search data via ARGO

Abstract: Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression

Cited by 362 publications

References 42 publications

Leveraging hospital big data to monitor flu epidemics

Leveraging hospital big data to monitor flu epidemics

Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study (Preprint)

Estimating Weekly National Opioid Overdose Deaths in Near Real Time Using Multiple Proxy Data Sources

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