Monitoring Pertussis Infections Using Internet Search Queries.
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ABSTRACT:
SUBMITTER: Zhang Y
PROVIDER: S-EPMC5585203 | biostudies-literature | 2017 Sep
REPOSITORIES: biostudies-literature
Publications
Monitoring Pertussis Infections Using Internet Search Queries.
Zhang Yuzhou Y Milinovich Gabriel G Xu Zhiwei Z Bambrick Hilary H Mengersen Kerrie K Tong Shilu S Hu Wenbiao W
Scientific reports 20170905 1
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 inciden ...[more]
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Project description:BackgroundInternet-based surveillance systems provide a novel approach to monitoring infectious diseases. Surveillance systems built on internet data are economically, logistically and epidemiologically appealing and have shown significant promise. The potential for these systems has increased with increased internet availability and shifts in health-related information seeking behaviour. This approach to monitoring infectious diseases has, however, only been applied to single or small groups of select diseases. This study aims to systematically investigate the potential for developing surveillance and early warning systems using internet search data, for a wide range of infectious diseases.MethodsOfficial notifications for 64 infectious diseases in Australia were downloaded and correlated with frequencies for 164 internet search terms for the period 2009-13 using Spearman's rank correlations. Time series cross correlations were performed to assess the potential for search terms to be used in construction of early warning systems.ResultsNotifications for 17 infectious diseases (26.6%) were found to be significantly correlated with a selected search term. The use of internet metrics as a means of surveillance has not previously been described for 12 (70.6%) of these diseases. The majority of diseases identified were vaccine-preventable, vector-borne or sexually transmissible; cross correlations, however, indicated that vector-borne and vaccine preventable diseases are best suited for development of early warning systems.ConclusionsThe findings of this study suggest that internet-based surveillance systems have broader applicability to monitoring infectious diseases than has previously been recognised. Furthermore, internet-based surveillance systems have a potential role in forecasting emerging infectious disease events, especially for vaccine-preventable and vector-borne diseases.
| S-EPMC4300155 | biostudies-literature
Project description:BackgroundThe internet is one of the most important sources for health-related information for the general population. Therefore, the analysis of internet search engines can help to capture the social interests and needs regarding diseases.ObjectivesThe aim of this study was to investigate the search queries related to atopic dermatitis (AD) regarding frequency, focus of interest and temporal occurrence in the German federal states in order to identify possible regional differences.Material and methodsUsing the Google Ads Keyword Planner, AD relevant keywords including the monthly search volume between January 2017 and December 2020 were identified, which were then assigned to different categories.ResultsOverall, 1419 keywords were found that had a search volume of 14,817,610 queries. The category 'general' had the highest search volume (n = 5,970,840), but most keywords were assigned to the category 'location' (n = 348). About 60% of the keywords assigned to 'location' were related to AD on the face. On a national level, Bremen and Hamburg had the highest search volume per 100,000 inhabitants. With more than 70%, an enormous increase in search volume could be observed, which was especially high in 2020.DiscussionWith this internet search analysis, it was possible to clarify which aspects of AD were of particular importance for the German population, which can help to adapt information campaigns to the target population. In addition, the study underlines the increasing relevance of the internet as a source of information regarding health-related topics.
| S-EPMC8925291 | biostudies-literature
Project description:BackgroundInternet search query trends have been shown to correlate with incidence trends for select infectious diseases and countries. Herein, the first use of Google search queries for malaria surveillance is investigated. The research focuses on Thailand where real-time malaria surveillance is crucial as malaria is re-emerging and developing resistance to pharmaceuticals in the region.MethodsOfficial Thai malaria case data was acquired from the World Health Organization (WHO) from 2005 to 2009. Using Google correlate, an openly available online tool, and by surveying Thai physicians, search queries potentially related to malaria prevalence were identified. Four linear regression models were built from different sub-sets of malaria-related queries to be used in future predictions. The models' accuracies were evaluated by their ability to predict the malaria outbreak in 2009, their correlation with the entire available malaria case data, and by Akaike information criterion (AIC).ResultsEach model captured the bulk of the variability in officially reported malaria incidence. Correlation in the validation set ranged from 0.75 to 0.92 and AIC values ranged from 808 to 586 for the models. While models using malaria-related and general health terms were successful, one model using only microscopy-related terms obtained equally high correlations to malaria case data trends. The model built strictly of queries provided by Thai physicians was the only one that consistently captured the well-documented second seasonal malaria peak in Thailand.ConclusionsModels built from Google search queries were able to adequately estimate malaria activity trends in Thailand, from 2005-2010, according to official malaria case counts reported by WHO. While presenting their own limitations, these search queries may be valid real-time indicators of malaria incidence in the population, as correlations were on par with those of related studies for other infectious diseases. Additionally, this methodology provides a cost-effective description of malaria prevalence that can act as a complement to traditional public health surveillance. This and future studies will continue to identify ways to leverage web-based data to improve public health.
| S-EPMC4228243 | biostudies-literature
Project description:Cities around the globe are embracing the Healthy Cities approach to address urban health challenges. Public awareness is vital for successfully deploying this approach but is rarely assessed. In this study, we used internet search queries to evaluate the public awareness of the Healthy Cities approach applied in Shenzhen, China. The overall situation at the city level and the intercity variations were both analyzed. Additionally, we explored the factors that might affect the internet search queries of the Healthy Cities approach. Our results showed that the public awareness of the approach in Shenzhen was low. There was a high intercity heterogeneity in terms of interest in the various components of the Healthy Cities approach. However, we did not find a significant effect of the selected demographic, environmental, and health factors on the search queries. Based on our findings, we recommend that the city raise public awareness of healthy cities and take actions tailored to health concerns in different city zones. Our study showed that internet search queries can be a valuable data source for assessing the public awareness of the Healthy Cities approach.
| S-EPMC8072553 | biostudies-literature
Project description:The use of cannabis for medicinal purposes has increased globally over the past decade since patient access to medicinal cannabis has been legislated across jurisdictions in Europe, the United Kingdom, the United States, Canada, and Australia. Yet, evidence relating to the effect of medical cannabis on the management of symptoms for a suite of conditions is only just emerging. Although there is considerable engagement from many stakeholders to add to the evidence base through randomized controlled trials, many gaps in the literature remain. Data from real-world and patient reported sources can provide opportunities to address this evidence deficit. This real-world data can be captured from a variety of sources such as found in routinely collected health care and health services records that include but are not limited to patient generated data from medical, administrative and claims data, patient reported data from surveys, wearable trackers, patient registries, and social media. In this systematic scoping review, we seek to understand the utility of online user generated text into the use of cannabis as a medicine. In this scoping review, we aimed to systematically search published literature to examine the extent, range, and nature of research that utilises user-generated content to examine to cannabis as a medicine. The objective of this methodological review is to synthesise primary research that uses social media discourse and internet search engine queries to answer the following questions: (i) In what way, is online user-generated text used as a data source in the investigation of cannabis as a medicine? (ii) What are the aims, data sources, methods, and research themes of studies using online user-generated text to discuss the medicinal use of cannabis. We conducted a manual search of primary research studies which used online user-generated text as a data source using the MEDLINE, Embase, Web of Science, and Scopus databases in October 2022. Editorials, letters, commentaries, surveys, protocols, and book chapters were excluded from the review. Forty-two studies were included in this review, twenty-two studies used manually labelled data, four studies used existing meta-data (Google trends/geo-location data), two studies used data that was manually coded using crowdsourcing services, and two used automated coding supplied by a social media analytics company, fifteen used computational methods for annotating data. Our review reflects a growing interest in the use of user-generated content for public health surveillance. It also demonstrates the need for the development of a systematic approach for evaluating the quality of social media studies and highlights the utility of automatic processing and computational methods (machine learning technologies) for large social media datasets. This systematic scoping review has shown that user-generated content as a data source for studying cannabis as a medicine provides another means to understand how cannabis is perceived and used in the community. As such, it provides another potential 'tool' with which to engage in pharmacovigilance of, not only cannabis as a medicine, but also other novel therapeutics as they enter the market.
| S-EPMC9858862 | biostudies-literature
Project description:BackgroundReal-time air pollution monitoring is a valuable tool for public health and environmental surveillance. In recent years, there has been a dramatic increase in air pollution forecasting and monitoring research using artificial neural networks. Most prior work relied on modeling pollutant concentrations collected from ground-based monitors and meteorological data for long-term forecasting of outdoor ozone (O3), oxides of nitrogen, and fine particulate matter (PM2.5). Given that traditional, highly sophisticated air quality monitors are expensive and not universally available, these models cannot adequately serve those not living near pollutant monitoring sites. Furthermore, because prior models were built based on physical measurement data collected from sensors, they may not be suitable for predicting the public health effects of pollution exposure.ObjectiveThis study aimed to develop and validate models to nowcast the observed pollution levels using web search data, which are publicly available in near real time from major search engines.MethodsWe developed novel machine learning-based models using both traditional supervised classification methods and state-of-the-art deep learning methods to detect elevated air pollution levels at the US city level by using generally available meteorological data and aggregate web-based search volume data derived from Google Trends. We validated the performance of these methods by predicting 3 critical air pollutants (O3, nitrogen dioxide, and PM2.5) across 10 major US metropolitan statistical areas in 2017 and 2018. We also explore different variations of the long short-term memory model and propose a novel search term dictionary learner-long short-term memory model to learn sequential patterns across multiple search terms for prediction.ResultsThe top-performing model was a deep neural sequence model long short-term memory, using meteorological and web search data, and reached an accuracy of 0.82 (F1-score 0.51) for O3, 0.74 (F1-score 0.41) for nitrogen dioxide, and 0.85 (F1-score 0.27) for PM2.5, when used for detecting elevated pollution levels. Compared with using only meteorological data, the proposed method achieved superior accuracy by incorporating web search data.ConclusionsThe results show that incorporating web search data with meteorological data improves the nowcasting performance for all 3 pollutants and suggest promising novel applications for tracking global physical phenomena using web search data.
| S-EPMC9808603 | biostudies-literature
Project description:Seasonal influenza epidemics cause serious public health problems in China. Search queries-based surveillance was recently proposed to complement traditional monitoring approaches of influenza epidemics. However, developing robust techniques of search query selection and enhancing predictability for influenza epidemics remains a challenge. This study aimed to develop a novel ensemble framework to improve penalized regression models for detecting influenza epidemics by using Baidu search engine query data from China. The ensemble framework applied a combination of bootstrap aggregating (bagging) and rank aggregation method to optimize penalized regression models. Different algorithms including lasso, ridge, elastic net and the algorithms in the proposed ensemble framework were compared by using Baidu search engine queries. Most of the selected search terms captured the peaks and troughs of the time series curves of influenza cases. The predictability of the conventional penalized regression models were improved by the proposed ensemble framework. The elastic net regression model outperformed the compared models, with the minimum prediction errors. We established a Baidu search engine queries-based surveillance model for monitoring influenza epidemics, and the proposed model provides a useful tool to support the public health response to influenza and other infectious diseases.
| S-EPMC5396076 | biostudies-literature
Project description:BACKGROUND:Oral contraceptives (OCs) are a unique chronic medication with which a memory slip may result in a threat that could change a person's life course. Subjective concerns of missed OC doses among women have been addressed infrequently. Anonymized queries to internet search engines provide unique access to concerns and information gaps faced by a large number of internet users. OBJECTIVE:We aimed to quantitate the frequency of queries by women seeking information in an internet search engine, after missing one or more doses of an OC; their further queries on emergency contraception, abortion, and miscarriage; and their rate of reporting a pregnancy timed to the cycle of missing an OC. METHODS:We extracted all English-language queries submitted to Bing in the United States during 2018, which mentioned a missed OC and subsequent queries of the same users on miscarriage, abortion, emergency contraceptives, and week of pregnancy. RESULTS:We identified 26,395 Bing users in the United States who queried about missing OC pills and the fraction that further queried about miscarriage, abortion, emergency contraceptive, and week of pregnancy. Users under the age of 30 years who asked about forgetting an OC dose were more likely to ask about abortion (1.5 times) and emergency contraception (1.7 times) (P<.001 for both), while users at ages of 30-34 years were more likely to query about pregnancy (2.1 times) and miscarriage (5.4 times) (P<.001 for both). CONCLUSIONS:Our data indicate that many women missing a dose of OC might not have received sufficient information from their health care providers or chose to obtain it online. Queries about abortion and miscarriage peaking in the subsequent days indicate a common worry of possible pregnancy. These results reinforce the importance of providing comprehensive written information on missed pills when prescribing an OC.
| S-EPMC7525397 | biostudies-literature
Project description:HFMD is a viral-mediated infectious illness of increasing public health importance. This study aimed to develop a forecasting tool utilizing climatic predictors and internet search queries for informing preventive strategies in Sabah, Malaysia. HFMD case data from the Sabah State Health Department, climatic predictors from the Malaysia Meteorological Department, and Google search trends from the Google trends platform between the years 2010-2018 were utilized. Cross-correlations were estimated in building a seasonal auto-regressive moving average (SARIMA) model with external regressors, directed by measuring the model fit. The selected variables were then validated using test data utilizing validation metrics such as the mean average percentage error (MAPE). Google search trends evinced moderate positive correlations to the HFMD cases (r0-6weeks: 0.47-0.56), with temperature revealing weaker positive correlations (r0-3weeks: 0.17-0.22), with the association being most intense at 0-1 weeks. The SARIMA model, with regressors of mean temperature at lag 0 and Google search trends at lag 1, was the best-performing model. It provided the most stable predictions across the four-week period and produced the most accurate predictions two weeks in advance (RMSE = 18.77, MAPE = 0.242). Trajectorial forecasting oscillations of the model are stable up to four weeks in advance, with accuracy being the highest two weeks prior, suggesting its possible usefulness in outbreak preparedness.
| S-EPMC9779090 | biostudies-literature
Project description:The Internet is a common source of medical information and has created novel surveillance opportunities. We assessed the potential for Internet-based surveillance of methicillin-resistant Staphylococcus aureus and examined the extent to which it reflects trends in hospitalizations and news coverage. Google queries were a useful predictor of hospitalizations for methicillin-resistant S. aureus infections.
| S-EPMC3358097 | biostudies-literature