Forecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data

Searching for information about individual entities such as persons, locations, events, is an important activity in Internet search today, and is in its core a very semantic-oriented task. Several ways for accessing such information exist, but for locating entity-specific information, search engines are the most commonly used approach. In this context, keyword queries are the primary means of retrieving information about a specific entity. We believe that an important first step of performing such a task is to understand what type of entity the user is looking for. We call this process Entity Type Disambiguation. In this paper, we present a Naive Bayesian Model for entity type disambiguation that explores our assumption that an entity type can be inferred from the attributes a user specifies in a search query. The model has been applied to queries provided by a large sample of participants in an experiment performing an entity search task. The beneficial impact of this approach for the development of new search systems is discussed.

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Internet search query analysis can be used to demonstrate the rapidly increasing public awareness of palliative care in the USA

BMJ Supportive & Palliative Care ◽

10.1136/bmjspcare-2016-001171 ◽

2017 ◽

Vol 9 (1) ◽

pp. 40-44 ◽

Cited By ~ 4

Author(s):

Sarah McLean ◽

Paul Lennon ◽

Paul Glare

Keyword(s):

Palliative Care ◽

Public Awareness ◽

Search Term ◽

Internet Search ◽

Search Query ◽

Health Seeking ◽

The Public ◽

Health Related ◽

The Usa ◽

The Uk

BackgroundA lack of public awareness of palliative care (PC) has been identified as one of the main barriers to appropriate PC access. Internet search query analysis is a novel methodology, which has been effectively used in surveillance of infectious diseases, and can be used to monitor public awareness of health-related topics.ObjectivesWe aimed to demonstrate the utility of internet search query analysis to evaluate changes in public awareness of PC in the USA between 2005 and 2015.MethodsGoogle Trends provides a referenced score for the popularity of a search term, for defined regions over defined time periods. The popularity of the search term ‘palliative care’ was measured monthly between 1/1/2005 and 31/12/2015 in the USA and in the UK.ResultsResults were analysed using independent t-tests and joinpoint analysis. The mean monthly popularity of the search term increased between 2008–2009 (p<0.001), 2011–2012 (p<0.001), 2013–2014 (p=0.004) and 2014–2015 (p=0.002) in the USA. Joinpoint analysis was used to evaluate the monthly percentage change (MPC) in the popularity of the search term. In the USA, the MPC increase was 0.6%/month (p<0.05); in the UK the MPC of 0.05% was non-significant.DiscussionAlthough internet search query surveillance is a novel methodology, it is freely accessible and has significant potential to monitor health-seeking behaviour among the public. PC is rapidly growing in the USA, and the rapidly increasing public awareness of PC as demonstrated in this study, in comparison with the UK, where PC is relatively well established is encouraging in increasingly ensuring appropriate PC access for all.

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Forecasting influenza epidemics by integrating internet search queries and traditional surveillance data with the support vector machine regression model in Liaoning, from 2011 to 2015

PeerJ ◽

10.7717/peerj.5134 ◽

2018 ◽

Vol 6 ◽

pp. e5134 ◽

Cited By ~ 10

Author(s):

Feng Liang ◽

Peng Guan ◽

Wei Wu ◽

Desheng Huang

Keyword(s):

Support Vector Machine ◽

Regression Model ◽

Search Engine ◽

Influenza Surveillance ◽

Percentage Error ◽

Support Vector ◽

Mean Square ◽

Internet Search ◽

Search Query ◽

Search Queries

Background Influenza epidemics pose significant social and economic challenges in China. Internet search query data have been identified as a valuable source for the detection of emerging influenza epidemics. However, the selection of the search queries and the adoption of prediction methods are crucial challenges when it comes to improving predictions. The purpose of this study was to explore the application of the Support Vector Machine (SVM) regression model in merging search engine query data and traditional influenza data. Methods The official monthly reported number of influenza cases in Liaoning province in China was acquired from the China National Scientific Data Center for Public Health from January 2011 to December 2015. Based on Baidu Index, a publicly available search engine database, search queries potentially related to influenza over the corresponding period were identified. An SVM regression model was built to be used for predictions, and the choice of three parameters (C, γ, ε) in the SVM regression model was determined by leave-one-out cross-validation (LOOCV) during the model construction process. The model’s performance was evaluated by the evaluation metrics including Root Mean Square Error, Root Mean Square Percentage Error and Mean Absolute Percentage Error. Results In total, 17 search queries related to influenza were generated through the initial query selection approach and were adopted to construct the SVM regression model, including nine queries in the same month, three queries at a lag of one month, one query at a lag of two months and four queries at a lag of three months. The SVM model performed well when with the parameters (C = 2, γ = 0.005, ɛ = 0.0001), based on the ensemble data integrating the influenza surveillance data and Baidu search query data. Conclusions The results demonstrated the feasibility of using internet search engine query data as the complementary data source for influenza surveillance and the efficiency of SVM regression model in tracking the influenza epidemics in Liaoning.

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