Forecasting the 2013–2014 Influenza Season Using Wikipedia

Hickmann, Kyle S.; Fairchild, Geoffrey; Priedhorsky, Reid; Generous, Nicholas; Hyman, James M.; Deshpande, Alina; Valle, Sara Y. Del

doi:10.1371/journal.pcbi.1004239

Cited by 145 publications

(144 citation statements)

References 55 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%

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%

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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“…If a person can be tested during the incubation period, they can be quarantined and treated, which is the only effective response to infectious viral diseases, such as influenza. Differences between influenza infection and other viral diseases and intestinal diseases exist [24][25][26]. Diarrhea, nausea, and vomiting are often mistaken as influenza.…”

Section: Influenza Investigationmentioning

confidence: 99%

A Rapid Screening Test for the Diagnosis of Influenza Infection Incubation Period Using Coincidence Analysis of Pulse Waves

Chen¹,

Tu²,

Yu³

et al. 2017

Preprint

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Viral infections have long been the biggest threat to human survival, and from a medical perspective, the development of noninvasive high-throughput screening methods that target the incubation period to either treat diseases or limit viral spread would be strikingly effective. Using this technology to target viral incubation periods would also be inexpensive to perform. The current study proposes to transform pulse signals into a rapid diagnostic test using "coincidence analysis" in the hope of preventing or reducing the symptoms of viral infections.The heart plays a critical role in calculating and supplying the needs of all tissues of the body. Pulse waves are pressurized signals in response to heart's calculations and include all phases of the cardiac cycle, which maintains life and provides energy needed to perform tasks. Any small movement gives a corresponding signal to pulse waves. The current study investigated conclusive data on self-limiting infections, such as common cold. We used pulse wave, coincidence analysis technology to capture signals from individuals with common cold during the incubation period and investigated if particular characteristic signals could be applied to influenza during the incubation period. Preliminary work demonstrated that pulse waves could generate signals using this technology that would be worthwhile for future research.A small amount of analytical data from common cold existed previously. The data structure is based on the idea that a single pulse wave at differing physiological conditions would have slight modifications that would be amplified and presented by various geometrical shapes after extensive data are accumulated. These geometric shapes can then be sliced vertically or horizontally to extract data during different illness stages.The significance of these findings are impressive; from a personal or a public hygiene perspective, this analytical technology provides many benefits, such as rapid and precise decision-making that can be directly visualized or can be analyzed using software programs. Also, this technology also uses futuristic wearable technology that brings practical problem solving to physiology.

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“…Hickmann et al (6) assessed Wikipedia hits to five articles that referred to influenza to forecast the actual number of cases that were reported to the Centers for Disease Control and Prevention (CDC) during the 2013-2014 influenza season. They showed that the number of hits to the website predicted the official number of cases before these were published by CDC (6). An evaluation of the accuracy of Wikipedia articles regarding microbiology has not been published.…”

Section: Microbiology Search Engines and The General Publicmentioning

confidence: 99%

Microbiology Learning and Education Online

Guarner

Niño

2016

J Clin Microbiol

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The ubiquity of devices that connect to the Internet has exploded, allowing for easy dissemination of information. Many teachers from kindergarten to universities use the information obtained online or post material they want their students to access. Online media readily places articles, books, videos, and games at our fingertips. The public in general also gathers health information from the Internet. The following review will explore what has been published regarding microbiology education and learning online and the use of electronic media by microbiologists for scientific purposes.

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Forecasting the 2013–2014 Influenza Season Using Wikipedia

Cited by 145 publications

References 55 publications

Leveraging hospital big data to monitor flu epidemics

Leveraging hospital big data to monitor flu epidemics

A Rapid Screening Test for the Diagnosis of Influenza Infection Incubation Period Using Coincidence Analysis of Pulse Waves

Microbiology Learning and Education Online

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