OBJECTIVE: To assess Google Trends accuracy for epidemiological surveillance of dengue and yellow fever, and to compare the incidence of these diseases with the popularity of its terms in the state of São Paulo. METHODS: Retrospective cohort. Google Trends survey results were compared to the actual incidence of diseases, obtained from Centro de Vigilância Epidemiológica "Prof. Alexandre Vranjac", in São Paulo, Brazil, in periods between 2017 and 2019. The correlation was calculated by Pearson's coefficient and cross-correlation function. The accuracy was analyzed by sensitivity and specificity values. RESULTS: There was a statistically significant correlation between the variables studied for both diseases, Pearson coefficient of 0.91 for dengue and 0.86 for yellow fever. Correlation with up to 4 weeks of anticipation for time series was identified. Sensitivity was 87% and 90%, and specificity 69% and 78% for dengue and yellow fever, respectively. CONCLUSION: The incidence of dengue and yellow fever in the State of São Paulo showed a strong correlation with the popularity of its terms measured by Google Trends in weekly periods. Google Trends tool provided early warning, with high sensitivity, for the detection of outbreaks of these diseases.
OBJECTIVE: To assess Google Trends accuracy for epidemiological surveillance of dengue and yellow fever, and to compare the incidence of these diseases with the popularity of its terms in the state of São Paulo. METHODS: Retrospective cohort. Google Trends survey results were compared to the actual incidence of diseases, obtained from Centro de Vigilância Epidemiológica "Prof. Alexandre Vranjac", in São Paulo, Brazil, in periods between 2017 and 2019. The correlation was calculated by Pearson's coefficient and cross-correlation function. The accuracy was analyzed by sensitivity and specificity values. RESULTS: There was a statistically significant correlation between the variables studied for both diseases, Pearson coefficient of 0.91 for dengue and 0.86 for yellow fever. Correlation with up to 4 weeks of anticipation for time series was identified. Sensitivity was 87% and 90%, and specificity 69% and 78% for dengue and yellow fever, respectively. CONCLUSION: The incidence of dengue and yellow fever in the State of São Paulo showed a strong correlation with the popularity of its terms measured by Google Trends in weekly periods. Google Trends tool provided early warning, with high sensitivity, for the detection of outbreaks of these diseases.