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. 2021 Jul 24;18(15):7841.
doi: 10.3390/ijerph18157841.

The Global Interest in Vaccines and Its Prediction and Perspectives in the Era of COVID-19. Real-Time Surveillance Using Google Trends

Magdalena Sycinska-Dziarnowska  1 , Iwona Paradowska-Stankiewicz  2 , Krzysztof Woźniak  1
Affiliations

The Global Interest in Vaccines and Its Prediction and Perspectives in the Era of COVID-19. Real-Time Surveillance Using Google Trends

Magdalena Sycinska-Dziarnowska et al. Int J Environ Res Public Health. .

Abstract

Background: The COVID-19 pandemic has globally overwhelmed all sectors of life. The fast development of vaccines against COVID-19 has had a significant impact on the course of the pandemic.

Methods: Global data from Google Trends was analyzed for vaccines against flu, BCG, HPV, pneumococcal disease, polio, and COVID-19. The time frame includes the last five-year period starting from 17 April 2016. Multiple training of time series models with back testing, including Holt-Winters forecasting, Exponential Smoothing State Space, Linear model with trend and seasonal components (tlsm), and ARIMA was conducted. Forecasting according to the best fitting model was performed.

Results: Correlation analysis did not reveal a decrease in interest in vaccines during the analyzed period. The prediction models provided a short-term forecast of the dynamics of interest for flu, HPV, pneumococcal and polio vaccines with 5-10% growth in interest for the first quarter of 2022 when compared to the same quarter of 2021.

Conclusions: Despite the huge interest in the COVID-19 vaccine, there has not been a detectable decline in the overall interest in the five analyzed vaccines.

Keywords: BCG vaccine; COVID-19 vaccine; Google Trends; HPV vaccine; Polio vaccine; flu vaccine; pneumococcal vaccine; vaccination programs.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Pairwise time series plots of weekly number of requests (2016–2021) for: (a) “covid” and “flu”; (b) “covid” and “bcg”; (c) “covid” and “hpv”; (d) “covid” and “pneumo”; (e) “covid” and “polio”.
Figure 2
Figure 2
ACF and PACF plots of time series for (a) “covid”; (b) “flu”; (c) “bcg”; (d) “hpv”; (e) “pneumo”; (f) “polio”.
Figure 3
Figure 3
Cross-correlation plots for: (a) “covid”–“flu”; (b) “covid”–“bcg”; (c) “covid”–“hpv”; (d) “covid”–“pneumo”; (e) “covid”–“polio”.
Figure 4
Figure 4
Forecast of interest in “flu” vaccine: (a) using SARIMA model; (b) using tslm model.
Figure 5
Figure 5
Forecast using Holt–Winters model of interest in: (a) “hpv” vaccine; (b) “polio” vaccine.
Figure 6
Figure 6
Forecast of interest in “pneumo” vaccine: (a) by using SARIMA model; (b) using tslm model.
See this image and copyright information in PMC

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