Using Google Health Trends to investigate COVID-19 incidence in Africa

Fulk, Alexander; Romero-Álvarez, Daniel; Abu-Saymeh, Qays; Jms, Onge; Peterson, A. Townsend; Agusto, Folashade B.

doi:10.1101/2021.03.26.21254369

Cited by 5 publications

(5 citation statements)

References 52 publications

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“…Little to no research evaluate the predictive performance of models based upon GT data for African contexts. Internet penetration in Africa, compared with other continents, remains low, and such a study, therefore, represents a significant contribution (Fulk et al, 2021). Our preliminary results of the Spearman rank correlation analysis found that many (9 out of 15) search terms had significant contemporaneous correlations with the COVID-19 case numbers.…”

Section: Discussionmentioning

confidence: 73%

Section: Discussionmentioning

confidence: 99%

“…These terms were grouped into five distinct categories (see Table 1 ) and compared within each group. The considered search terms have the potential to capture a broad spectrum of information related to COVID-19 (Fulk et al, 2021 ; Satpathy et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

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Modeling COVID-19 incidence with Google Trends

Amusa

Twinomurinzi

Okonkwo

2022

Front. Res. Metr. Anal.

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Infodemiologic methods could be used to enhance modeling infectious diseases. It is of interest to verify the utility of these methods using a Nigerian case study. We used Google Trends data to track COVID-19 incidences and assessed whether they could complement traditional data based solely on reported case numbers. Data on the Nigerian weekly COVID-19 cases spanning through March 1, 2020, to May 31, 2021, were matched with internet search data from Google Trends. The reported weekly incidence numbers and the GT data were split into training and testing sets. ARIMA models were fitted to describe reported weekly COVID cases using the training set. Several COVID-related search terms were theoretically and empirically assessed for initial screening. The utilized Google Trends (GT) variable was added to the ARIMA model as a regressor. Model forecasts, both with and without GTD, were compared with weekly cases in the test set over 13 weeks. Forecast accuracies were compared visually and using RMSE (root mean square error) and MAE (mean average error). Statistical significance of the difference in predictions was determined with the two-sided Diebold-Mariano test. Preliminary results of contemporaneous correlations between COVID-related search terms and weekly COVID cases reveal “loss of smell,” “loss of taste,” “fever” (in order of magnitude) as significantly associated with the official cases. Predictions of the ARIMA model using solely reported case numbers resulted in an RMSE (root mean squared error) of 411.4 and mean absolute error (MAE) of 354.9. The GT expanded model achieved better forecasting accuracy (RMSE: 388.7 and MAE = 340.1). Corrected Akaike Information Criteria also favored the GT expanded model (869.4 vs. 872.2). The difference in predictive performances was significant when using a two-sided Diebold-Mariano test (DM = 6.75, p < 0.001) for the 13 weeks. Google trends data enhanced the predictive ability of a traditionally based model and should be considered a suitable method to enhance infectious disease modeling.

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Section: Discussionmentioning

confidence: 73%

Section: Discussionmentioning

confidence: 99%

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Modeling COVID-19 incidence with Google Trends

Amusa

Twinomurinzi

Okonkwo

2022

Front. Res. Metr. Anal.

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“…Prevention and surveillance remain crucial activities that have helped curb the spread of COVID-19. Another recommendation would be to anticipate outbreaks using a free internet tool Google Health Trends, as demonstrated by Fulk et al (2022).…”

Section: Discussionmentioning

confidence: 99%

Effect of Health System Performance on Volatility During the Covid-19 Pandemic: A Neural Network Approach

Gheorghe,

Panazan

2024

Journal of Business Economics and Management

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The study proposes an assessment of the link between the performance of national health systems and volatility during the COVID-19 pandemic. Data from the World Health Organization was accessed regarding the Global Health Security Index of the states considered in the analysis as well as the categories based on which it is determined. To characterise volatility, a representative stock market index was considered for each of the 60 states analysed. Data processing was carried out using an artificial neural network. The main results show that: i) before the pandemic, the link between market volatility and the performance of national health systems was weak; ii) during the pandemic, the connection between the two variables is much stronger; iii) between the six categories that define the Global Health Security Index, norms, health, and prevention had the greatest influence on volatility.

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“…Correlation with Google search queries has been investigated for vastly different medical conditions and in various clinical or population scenarios and settings, related and unrelated to reproductive health issues. [21][22][23][24][25] However, the correlation estimates that we quantified between search on Google about IUDs and actual IUD insertion rates is a new contribution to the evidence in this area. Related literature includes Guendelman et al 26 who showed US state policies on abortion could predict search volumes for abortion.…”

Section: Discussionmentioning

confidence: 99%

Trends of intrauterine device insertion and ‘Googling’ about intrauterine devices before and during the COVID-19 pandemic in Australia

et al. 2022

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Objective The COVID-19 pandemic significantly disrupted access to primary care in Australia. This could have negatively impacted reproductive health services rates such as intrauterine device insertion rates, and interest in seeking information about intrauterine devices by searching on Google. We aimed to assess the trends of, and the association between, the actual Medicare service utilization rates for intrauterine device insertion and searching about intrauterine devices on Google, before and during the COVID-19 pandemic. Methods We conducted systematic analyses of secondary data from June 2017 to May 2022, using Medicare and Google Trends data sources. We visualized the rates of intrauterine device insertion, plus Google's search volumes about ‘Intrauterine device’ and ‘Progestin IUDs’ as topics. Then, we assessed the correlation between intrauterine device insertion rates and Google search, using Spearman correlation. Results The average yearly rates of intrauterine device insertion increased noticeably from 25.1–26.3 in 2018–2019 to 29.3–31.2 per 100,000 population in 2020–2021 (12–18% increase). The highest monthly intrauterine device insertion rate nationally (37 per 100,000 population) was seen in March 2021. By June 2020, search term use for the two intrauterine device-related topics returned to much higher levels (50% increase for ‘Progestin IUDs’, and 54% for ‘Intrauterine device’, respectively). A moderately strong correlation was seen between actual intrauterine device insertion rates and search on Google about intrauterine devices (Spearman rho = 0.61, p < 0.000). Conclusion We demonstrated a moderately strong correlation between trends of intrauterine device insertion rates and search on Google about intrauterine devices, before and during the COVID-19 pandemic in Australia. Googling about intrauterine devices could, therefore, be a useful indicator to gauge future interest in actual intrauterine device insertion for months thereafter.

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Using Google Health Trends to investigate COVID-19 incidence in Africa

Cited by 5 publications

References 52 publications

Modeling COVID-19 incidence with Google Trends

Modeling COVID-19 incidence with Google Trends

Effect of Health System Performance on Volatility During the Covid-19 Pandemic: A Neural Network Approach

Trends of intrauterine device insertion and ‘Googling’ about intrauterine devices before and during the COVID-19 pandemic in Australia

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