Uncontrolled outbreaks of emerging infectious diseases can pose threats to livelihoods and can undo years of progress made in developing regions, such as Sub-Saharan Africa. Therefore, the surveillance and early outbreak detection of infectious diseases, e.g., Dengue fever, is crucial. As a low-cost and timely source, Internet search queries data [e.g., Google Trends data (GTD)] are used and applied in epidemiological surveillance. This review aims to identify and evaluate relevant studies that used GTD in prediction models for epidemiological surveillance purposes regarding emerging infectious diseases. A comprehensive literature search in PubMed/MEDLINE was carried out, using relevant keywords identified from up-to-date literature and restricted to low- to middle-income countries. Eight studies were identified and included in the current review. Three focused on Dengue fever, three analyzed Zika virus infections, and two were about COVID-19. All studies investigated the correlation between GTD and the cases of the respective infectious disease; five studies used additional (time series) regression analyses to investigate the temporal relation. Overall, the reported positive correlations were high for Zika virus (0.75-0.99) or Dengue fever (0.87-0.94) with GTD, but not for COVID-19 (-0.81 to 0.003). Although the use of GTD appeared effective for infectious disease surveillance in low- to middle-income countries, further research is needed. The low costs and availability remain promising for future surveillance systems in low- to middle-income countries, but there is an urgent need for a standard methodological framework for the use and application of GTD.
Uncontrolled outbreaks of emerging infectious diseases can pose threats to livelihoods and can undo years of progress made in developing regions, such as Sub-Saharan Africa. Therefore, the surveillance and early outbreak detection of infectious diseases, e.g., Dengue fever, is crucial. As a low-cost and timely source, Internet search queries data [e.g., Google Trends data (GTD)] are used and applied in epidemiological surveillance. This review aims to identify and evaluate relevant studies that used GTD in prediction models for epidemiological surveillance purposes regarding emerging infectious diseases. A comprehensive literature search in PubMed/MEDLINE was carried out, using relevant keywords identified from up-to-date literature and restricted to low- to middle-income countries. Eight studies were identified and included in the current review. Three focused on Dengue fever, three analyzed Zika virus infections, and two were about COVID-19. All studies investigated the correlation between GTD and the cases of the respective infectious disease; five studies used additional (time series) regression analyses to investigate the temporal relation. Overall, the reported positive correlations were high for Zika virus (0.75-0.99) or Dengue fever (0.87-0.94) with GTD, but not for COVID-19 (-0.81 to 0.003). Although the use of GTD appeared effective for infectious disease surveillance in low- to middle-income countries, further research is needed. The low costs and availability remain promising for future surveillance systems in low- to middle-income countries, but there is an urgent need for a standard methodological framework for the use and application of GTD.