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.
Highlights
Internet search query trends have been shown to correlate with incidence trends for select infectious diseases and countries
Overview The goal of this study was to determine whether query data from malaria-related Google searches can be used to predict existing malaria surveillance trends for Thailand
Google search query time series All Google query data used in the analysis were made available by Google Correlate, an open-source online tool that enables the user to identify the top 100 search queries with trends most similar to either a time series data uploaded by the user or to one particular search term of interest [12]
Summary
Internet search query trends have been shown to correlate with incidence trends for select infectious diseases and countries. Traditional malaria surveillance systems harness data regarding hospitalizations, blood smear slide examination positivity rates and surveying. These systems suffer from delays associated with aggregation, information collection and bureaucracy resulting in a lack of timeliness for ensuing interventions. Relying on this traditional surveillance only captures a fraction of cases at it fails to capture malaria cases outside the hospital, such as home treatment or those seeking nonconventional treatments such as herbals. Mathematical models using climate and ecological data, another commonly explored method of surveillance, can predict malaria outbreaks; these models rely on continuous, detailed and rapid availability of certain data types [4]
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