Using Web Search Query Data to Monitor Dengue Epidemics: A New Model for Neglected Tropical Disease Surveillance

Chan, Emily H.; Sahai, Vikram; Conrad, Corrie; Brownstein, John S.

doi:10.1371/journal.pntd.0001206

Cited by 245 publications

(268 citation statements)

References 18 publications

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“…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).…”

Section: Strength Of Argomentioning

confidence: 99%

Accurate estimation of influenza epidemics using Google search data via ARGO

Yang

Santillana

Kou

2015

Proc. Natl. Acad. Sci. U.S.A.

341

443

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Section: Strength Of Argomentioning

confidence: 99%

Accurate estimation of influenza epidemics using Google search data via ARGO

Yang

Santillana

Kou

2015

Proc. Natl. Acad. Sci. U.S.A.

341

443

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“…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]<...>…”

Section: Author Summarymentioning

confidence: 99%

“…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.…”

Section: Author Summarymentioning

confidence: 99%

Global Disease Monitoring and Forecasting with Wikipedia

Generous

Fairchild²,

Deshpande³

et al. 2016

OJPHI

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Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with r 2 up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without retraining is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

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“…was used in [12] to predict the number of dengue cases with Google query fraction Q. Thus, models have used both raw, logit-transformed and fractions as the dependent or predicted variable while the independent variable has always been a query fraction, with or without lags.…”

mentioning

confidence: 99%

Nowcasting influenza outbreaks using open-source media report.

Ray¹,

Brownstein

2013

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We construct and verify a statistical method to nowcast influenza activity from a time-series of the frequency of reports concerning influenza related topics. Such reports are published electronically by both public health organizations as well as newspapers/media sources, and thus can be harvested easily via web crawlers. Since media reports are timely, whereas reports from public health organization are delayed by at least two weeks, using timely, open-source data to compensate for the lag in "official" reports can be useful. We use morbidity data from networks of sentinel physicians (both the Center of Disease Control's ILINet and France's Sentinelles network) as the gold standard of influenza-like illness (ILI) activity. The time-series of media reports is obtained from HealthMap (http://healthmap.org). We find that the time-series of media reports shows some correlation (≈ 0.5) with ILI activity; further, this can be leveraged into an autoregressive moving average model with exogenous inputs (ARMAX model) to nowcast ILI activity. We find that the ARMAX models have more predictive skill compared to autoregressive (AR) models fitted to ILI data i.e., it is possible to exploit the information content in the open-source data. We also find that when the open-source data are non-informative, the ARMAX models reproduce the performance of AR models. The statistical models are tested on data from the 2009 swine-flu outbreak as well as the mild 2011-2012 influenza season in the U.S.A.

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Using Web Search Query Data to Monitor Dengue Epidemics: A New Model for Neglected Tropical Disease Surveillance

Cited by 245 publications

References 18 publications

Accurate estimation of influenza epidemics using Google search data via ARGO

Accurate estimation of influenza epidemics using Google search data via ARGO

Global Disease Monitoring and Forecasting with Wikipedia

Nowcasting influenza outbreaks using open-source media report.

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