Using search queries for malaria surveillance, Thailand

Abstract

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.

Accurate estimation of influenza epidemics using Google search data via ARGO

“…In this article, we fixed the search query terms after 2010 so as to directly compare our results with GFT, which has kept the same query terms since 2010; future application of ARGO may update search terms more frequently. ARGO can be easily generalized to any temporal and spatial scales for a variety of diseases or social events amenable to be tracked by Internet searches or services (3,4,8,9,29,30,38,39). Further improvements in influenza prediction may come from combining multiple predictors constructed from disparate data sources (40).…”

“…In recent years, methods that harness Internet-based information have also been proposed, such as Google (1), Yahoo (2), and Baidu (3) Internet searches, Twitter posts (4), Wikipedia article views (5), clinicians' queries (6), and crowdsourced selfreporting mobile apps such as Influenzanet (Europe) (26), Flutracking (Australia) (27), and Flu Near You (United States) (28). Among them, GFT has received the most attention and has inspired subsequent digital disease detection systems (3,8,(29)(30)(31)(32). Interestingly, Google has never made their raw data public, thus making it impossible to reproduce the exact results of GFT.…”

Research on the impact factors of new energy automobile sales under the background of big data Era

“…The Internet become one of the quickest way to get materials and information, and consumers preferred to search related data and information from the Internet .According to the statistics of the China Internet network information center, about 90% of Internet users use search engines to obtain relevant information by the end of 2015 [2].Due to Internet search data recorded hundreds of millions of attention and demand, it reflects the trend and rules of main market players and provides necessary data for the research of social and economic behavior. Before buying a car new energy automobile, consumers will obtain the data and information via the related information channels.…”

Global Disease Monitoring and Forecasting with Wikipedia

“…Openness. Deep access to search queries from Baidu, a Chinese-language search engine serving mostly the Chinese market (http://www.baidu.com) [64,74,76]; Google [36,[50][51][52][53][54][56][57][58][59][60][65][66][67][69][70][71][72][73]75]; Yahoo [55,68]; and Yandex, a search engine serving mostly Russia and Slavic countries in Russian (http://www.yandex.ru), English (http://www. yandex.com), and Turkish (http://www.yandex.com.tr) [75], as well as purpose-built health website search queries [61][62][63] and access logs [72,93] are available only to those within the organizations, upon payment of an often-substantial fee, or by some other special arrangement.…”

“…When appropriately trained, these methods can be quite accurate; for example, many of the cited models can produce near real-time estimates of case counts with correlations upwards of r = 0.95. The collection of disease surveillance work cited above has estimated incidence for a wide variety of infectious and noninfectious conditions: avian influenza [52], cancer [55], chicken pox [67], cholera [81], dengue [50,53,84], dysentery [76], gastroenteritis [56,61,67], gonorrhea [64], hand foot and mouth disease (HFMD) [72], HIV/AIDS [75,76], influenza [34,36,54,57,59,62,63,65,67,68,71,74,[77][78][79][80]82,83,[85][86][87][88][89][90][91][92][93], kidney stones [51], listeriosis [70], malaria [66], methicillin-resistant Staphylococcus aureus (MRSA) [58]<...>…”