Journal of public health research · 2026
Evaluation of Google search trends for COVID-19 vaccination administration efforts within an urban department of public health.
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Han Sae H, Soulakis Nicholas D
Аннотация
OBJECTIVE: To evaluate the utility of the Google COVID-19 Vaccination Search Insights (VSI) dataset for predicting and forecasting COVID-19 vaccine doses.
METHODS: This retrospective, ecological time-series analysis used Google VSI data and Chicago Data Portal COVID-19 Daily Vaccinations dataset to predict and forecast weekly COVID-19 vaccination doses administered in Chicago between January 4, 2021 and December 20, 2021. Model performance was evaluated using root mean squared error (RMSE) and mean percent differences between the predicted and forecasted vaccination values and actual doses administered.
RESULTS: SARIMAX models incorporating Google COVID-19 vaccination search frequencies, lagged by an average of 1.67 weeks, improved in-sample predictive accuracy relative to univariate SARIMA models, with gains most pronounced in high-vulnerability communities (High CCVI ZIP codes; mean RMSE reduction 84.73% across cross-validation iterations). Improvements in out-of-sample forecasting were similarly concentrated in High CCVI ZIP codes; in the remaining ZIP codes, SARIMAX forecasts were less accurate than SARIMA. SARIMAX forecasts produced narrower 95% confidence intervals across all strata, indicating greater model confidence.
CONCLUSIONS: These findings demonstrate the potential utility of the Google COVID-19 VSI dataset as a supplementary tool for informing COVID-19 vaccination distribution planning and resource allocation, particularly in socially vulnerable communities.
METHODS: This retrospective, ecological time-series analysis used Google VSI data and Chicago Data Portal COVID-19 Daily Vaccinations dataset to predict and forecast weekly COVID-19 vaccination doses administered in Chicago between January 4, 2021 and December 20, 2021. Model performance was evaluated using root mean squared error (RMSE) and mean percent differences between the predicted and forecasted vaccination values and actual doses administered.
RESULTS: SARIMAX models incorporating Google COVID-19 vaccination search frequencies, lagged by an average of 1.67 weeks, improved in-sample predictive accuracy relative to univariate SARIMA models, with gains most pronounced in high-vulnerability communities (High CCVI ZIP codes; mean RMSE reduction 84.73% across cross-validation iterations). Improvements in out-of-sample forecasting were similarly concentrated in High CCVI ZIP codes; in the remaining ZIP codes, SARIMAX forecasts were less accurate than SARIMA. SARIMAX forecasts produced narrower 95% confidence intervals across all strata, indicating greater model confidence.
CONCLUSIONS: These findings demonstrate the potential utility of the Google COVID-19 VSI dataset as a supplementary tool for informing COVID-19 vaccination distribution planning and resource allocation, particularly in socially vulnerable communities.