ArticleScientific reports2017
Monitoring Pertussis Infections Using Internet Search Queries.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it, 45 citations in OpenAlex.
- Using internet-based query and climate data to predict climate-sensitive infectious disease risks: a systematic review of epidemiological evidence.International journal of biometeorology · 2021Pooled it
- The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review.Journal of medical Internet research · 2020Pooled it
- Methods for Detecting Suspicious Information From Individual Transactions of Pharmaceutical Products via Twitter (now X): Retrospective Observational Study.Journal of medical Internet research · 2026Observational
- Temporal Association Between ChatGPT-Generated Diarrhea Synonyms in Internet Search Queries and Emergency Department Visits for Diarrhea-Related Symptoms in South Korea: Exploratory Study.Journal of medical Internet research · 2025Article
- Combined short-term exposure to meteorological, pollution factors and pertussis in different groups from Jining, China.Journal of global health · 2024Article
- Infodemiology and Infoveillance of the Four Most Widespread Arbovirus Diseases in Italy.Epidemiologia (Basel, Switzerland) · 2024Article
- Japanese encephalitis transmission trends in Gansu, China: A time series predictive model based on spatial dispersion.One health (Amsterdam, Netherlands) · 2023Article
- Article
- Internet search data with spatiotemporal analysis in infectious disease surveillance: Challenges and perspectives.Frontiers in public health · 2022Article
- A potential tool for predicting epidemic trends and outbreaks of scrub typhus based on Internet search big data analysis in Yunnan Province, China.Frontiers in public health · 2022Article
- Article
- Risk-adjusted zero-inflated Poisson CUSUM charts for monitoring influenza surveillance data.BMC medical informatics and decision making · 2021Article
- Estimation of Asthma Symptom Onset Using Internet Search Queries: Lag-Time Series Analysis.JMIR public health and surveillance · 2021Article
- Associations between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States.International journal of environmental research and public health · 2021Article
- The long-term effects of meteorological parameters on pertussis infections in Chongqing, China, 2004-2018.Scientific reports · 2020Article
- Application of Big Data Technology for COVID-19 Prevention and Control in China: Lessons and Recommendations.Journal of medical Internet research · 2020Article
- Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database.International journal of environmental research and public health · 2020Article
- Forecasting tuberculosis using diabetes-related google trends data.Pathogens and global health · 2020Article
- Global dynamic spatiotemporal pattern of seasonal influenza since 2009 influenza pandemic.Infectious diseases of poverty · 2020Article
- Using big data to predict pertussis infections in Jinan city, China: a time series analysis.International journal of biometeorology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims to assess the utility of internet search query analysis in pertussis surveillance. This study uses an empirical time series model based on internet search metrics to detect the pertussis incidence in Australia. Our research demonstrates a clear seasonal pattern of both pertussis infections and Google Trends (GT) with specific search terms in time series seasonal decomposition analysis. The cross-correlation function showed significant correlations between GT and pertussis incidences in Australia and each state at the lag of 0 and 1 months, with the variation of correlations between 0.17 and 0.76 (p < 0.05). A multivariate seasonal autoregressive integrated moving average (SARIMA) model was developed to track pertussis epidemics pattern using GT data. Reflected values for this model were generally consistent with the observed values. The inclusion of GT metrics improved detective performance of the model (β = 0.058, p < 0.001). The validation analysis indicated that the overall agreement was 81% (sensitivity: 77% and specificity: 83%). This study demonstrates the feasibility of using internet search metrics for the detection of pertussis epidemics in real-time, which can be considered as a pre-requisite for constructing early warning systems for pertussis surveillance using internet search metrics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.