A general method for estimating the prevalence of influenza-like-symptoms with Wikipedia data

Toni, Giovanni De; Consonni, Cristian; Montresor, Alberto

doi:10.1371/journal.pone.0256858

Cited by 3 publications

(7 citation statements)

References 38 publications

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“… 39 Caldwell et al evaluated internet traffic to influenza‐related pages on the US Centers for Disease Control (CDC) website against the percentage of visits for ILI across three influenza seasons. 37 They found strong correlations for some seasons, particularly at national and regional levels but no correlation in many other cases, particularly when looking at the state level. Depending on which pages were considered, the geographical unit and the season under study, optimal correlations were achieved either with no lag or using ILI data 1 week in advance of online search data.…”

Section: Resultsmentioning

confidence: 96%

“…Our search identified 10 articles addressing internet search queries or webpage views. 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 Four articles studied Baidu, a popular internet search engine in China. 38 , 39 , 41 , 44 Three of these provided data on both correlative value and timeliness.…”

Section: Resultsmentioning

confidence: 99%

“…De Toni et al found that page views for influenza‐related Wikipedia pages in four European countries correlated well with ILI incidence data for each country 39 . Caldwell et al evaluated internet traffic to influenza‐related pages on the US Centers for Disease Control (CDC) website against the percentage of visits for ILI across three influenza seasons 37 . They found strong correlations for some seasons, particularly at national and regional levels but no correlation in many other cases, particularly when looking at the state level.…”

Section: Resultsmentioning

confidence: 99%

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Influenza surveillance systems using traditional and alternative sources of data: A scoping review

Hammond

Kim

Sadler

et al. 2022

Influenza Resp Viruses

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Objective While the World Health Organization's recommendation of syndromic sentinel surveillance for influenza is an efficient method to collect high‐quality data, limitations exist. Aligned with the Research Recommendation 1.1.2 of the WHO Public Health Research Agenda for Influenza—to identify reliable complementary influenza surveillance systems which provide real‐time estimates of influenza activity—we performed a scoping review to map the extent and nature of published literature on the use of non‐traditional sources of syndromic surveillance data for influenza. Methods We searched three electronic databases (PubMed, Web of Science, and Scopus) for articles in English, French, and Spanish, published between January 1 2007 and January 28 2022. Studies were included if they directly compared at least one non‐traditional with a traditional influenza surveillance system in terms of correlation in activity or timeliness. Findings We retrieved 823 articles of which 57 were included for analysis. Fifteen articles considered electronic health records (EHR), 11 participatory surveillance, 10 online searches and webpage traffic, seven Twitter, five absenteeism, four telephone health lines, three medication sales, two media reporting, and five looked at other miscellaneous sources of data. Several articles considered more than one non‐traditional surveillance method. Conclusion We identified eight categories and a miscellaneous group of non‐traditional influenza surveillance systems with varying levels of evidence on timeliness and correlation to traditional surveillance systems. Analyses of EHR and participatory surveillance systems appeared to have the most agreement on timeliness and correlation to traditional systems. Studies suggested non‐traditional surveillance systems as complements rather than replacements to traditional systems.

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Influenza surveillance systems using traditional and alternative sources of data: A scoping review

Hammond

Kim

Sadler

et al. 2022

Influenza Resp Viruses

View full text Add to dashboard Cite

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“…The specific topics explored by the reviewed studies included Dementia (Brigo et al, 2015 ; Alibudbud, 2023b ), fencing response (Roe et al, 2023 ), tumors (e.g., pancreatic tumors, brain tumors, colorectal cancer) (Naik et al, 2021 ; Mondia et al, 2022 ; Gianfredi et al, 2023 ), substance use disorder (Alibudbud and Cleofas, 2022 ), epilepsy (Brigo et al, 2015 ; Okumura et al, 2016 ), schizophrenia (Adams et al, 2020 ), diabetes mellitus (Potapov et al, 2021 ), pain (e.g., migraine, low back pain, inflammation, sciatica) (Brigo et al, 2015 ; Szmuda et al, 2020 ; Ciaffi et al, 2021 ; Potapov et al, 2021 ), cardiovascular diseases (Potapov et al, 2021 ), gastrointestinal conditions (Potapov et al, 2021 ), dermatological agents (Potapov et al, 2021 ), viral infections (e.g., coronavirus, COVID-19, influenza, Chikungunya) (Laurent and Vickers, 2009 ; Mahroum et al, 2018 ; Provenzano et al, 2019 ; Qiu et al, 2019 ; Gozzi et al, 2020 ; O'Leary and Storey, 2020 ; De Toni et al, 2021 ; Gianfredi et al, 2021 ; Rutovic et al, 2021 ; Storey and O'Leary, 2022 ), autoimmune conditions (e.g., Systemic Lupus Erythematosus) (Sciascia and Radin, 2017 ) various medications (e.g., Abacavir, Zidovudine) (Sciascia and Radin, 2017 ; Apollonio et al, 2018 ; Darrow and Borisova, 2022 ), different diets (e.g., vegetarian) (Nucci et al, 2021 ), frostbite (Laurent and Vickers, 2009 ), hypothermia (Laurent and Vickers, 2009 ), carbon monoxide poisoning (Laurent and Vickers, 2009 ), hyperthermia (Laurent and Vickers, 2009 ), sunburn (Laurent and Vickers, 2009 ), insect bites (Laurent and Vickers, 2009 ), and women's health-related topic (e.g., discrimination) (Wang and Zhang, 2020 ). Therefore, the reviewed studies have been predominantly used in understanding health information utilization for various communicable and non-communicable diseases.…”

Section: Resultsmentioning

confidence: 99%

Wikipedia page views for health research: a review

Alibudbud

2023

Front. Big Data

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Wikipedia is an open-source online encyclopedia and one of the most-read sources of online health information. Likewise, Wikipedia page views have also been analyzed to inform public health services and policies. The present review analyzed 29 studies utilizing Wikipedia page views for health research. Most reviewed studies were published in recent years and emanated from high-income countries. Together with Wikipedia page views, most studies also used data from other internet sources, such as Google, Twitter, YouTube, and Reddit. The reviewed studies also explored various non-communicable diseases, infectious diseases, and health interventions to describe changes in the utilization of online health information from Wikipedia, to examine the effect of public events on public interest and information usage about health-related Wikipedia pages, to estimate and predict the incidence and prevalence of diseases, to predict data from other internet data sources, to evaluate the effectiveness of health education activities, and to explore the evolution of a health topic. Given some of the limitations in replicating some of the reviewed studies, future research can specify the specific Wikipedia page or pages analyzed, the language of the Wikipedia pages examined, dates of data collection, dates explored, type of data, and whether page views were limited to Internet users and whether web crawlers and redirects to the Wikipedia page were included. Future research can also explore public interest in other commonly read health topics available in Wikipedia, develop Wikipedia-based models that can be used to predict disease incidence and improve Wikipedia-based health education activities.

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“…To further improve prediction accuracy, numerous methods often combine various data sources, including infuenza surveillance data [14], web search data [26,27], temperature data [16], air pollutant data [28,29], Wikipedia access data [30], Twitter posts [31], and electronic medical records [32]. For instance, the ARGO method combines Google search term data and the LASSO model to capture people's dynamic search behavior over time-it has been noted to exhibit excellent infuenza prediction performance.…”

Section: Introductionmentioning

confidence: 99%

A LASSO-Based Prediction Model for Child Influenza Epidemics: A Case Study of Shanghai, China

Zhu

et al. 2022

Mathematical Problems in Engineering

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Child influenza is an acute infectious disease that places substantial burden on children and their families. Real-time accurate prediction of child influenza epidemics can aid scientific and timely decision-making that may reduce the harm done to children infected with influenza. Several models have been proposed to predict influenza epidemics. However, most existing studies focus on adult influenza prediction. This study demonstrates the feasibility of using the LASSO (least absolute shrinkage and selection operator) model to predict influenza-like illness (ILI) levels in children between 2017 and 2020 in Shanghai, China. The performance of the LASSO model was compared with that of other statistical influenza-prediction techniques, including autoregressive integrated moving average (ARIMA), random forest (RF), ordinary least squares (OLS), and long short-term memory (LSTM). The LASSO model was observed to exhibit superior performance compared to the other candidate models. Owing to the variable shrinkage and low-variance properties of LASSO, it eliminated unimportant features and avoided overfitting. The experimental results suggest that the LASSO model can provide useful guidance for short-term child influenza prevention and control for schools, hospitals, and governments.

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A general method for estimating the prevalence of influenza-like-symptoms with Wikipedia data

Cited by 3 publications

References 38 publications

Influenza surveillance systems using traditional and alternative sources of data: A scoping review

Influenza surveillance systems using traditional and alternative sources of data: A scoping review

Wikipedia page views for health research: a review

A LASSO-Based Prediction Model for Child Influenza Epidemics: A Case Study of Shanghai, China

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