The use and applicability of Internet search queries for infectious disease surveillance in low- to middle-income countries

Abstract

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