An Infodemiological study of non-clinical COVID-19 preventive measures using Google Trends
DOI:
https://doi.org/10.64497/jssci.258Keywords:
COVID-19, non-clinical preventive measures, google trends, regression analysis, public awarenessAbstract
Public awareness of non-clinical COVID-19 preventive measures is critical for effective health communication, strategic planning, and evidence-based decision-making. This study analyzes trends in public interest using Google Trends data on preventive measures such as face mask usage, hand sanitizer use, social distancing, and lockdowns, in relation to COVID-19 case trends. Statistical diagnostics, including correlation matrices, Variance Inflation Factor (VIF), and eigenvalue analysis, were employed to assess multicollinearity. At the same time, Breusch–Pagan, White, and Breusch–Godfrey tests were applied to detect and address heteroscedasticity. The results reveal significant positive associations between COVID-19 case trends and both face mask usage and lockdown measures, whereas hand sanitizer and social distancing trends exhibited negligible correlations. No evidence of multicollinearity was found, as all condition indices were below 10, indicating stable model estimates. These findings provide valuable insights into temporal changes in public behavior and awareness of preventive measures, informing policymakers and public health authorities in the design of targeted interventions to mitigate the spread of COVID-19.
Downloads
References
[1] B. S. Mohan and V. Nambiar, “COVID-19: An insight into SARS-CoV-2 pandemic originated at Wuhan City in Hubei Province of China,” J. Infect. Dis. Epidemiol., vol. 6, no. 4, Art. no. 146, 2020. DOI: https://doi.org/10.23937/2474-3658/1510146
[2] F. B. Hamzah, C. Lau, H. Nazri, D. V. Ligot, G. Lee, C. L. Tan, B. M. Lee, S. S. Abdullah, N. Chung, and M. H. Chung, “CoronaTracker: Worldwide COVID-19 outbreak data analysis and prediction,” Bull. World Health Organ., vol. 1, pp. 1–32, 2020. DOI: https://doi.org/10.2471/BLT.20.255695
[3] D. T. Halperin, N. Hearst, S. Hodgins, R. C. Bailey, J. D. Klausner, H. Jackson, and M. Gandhi, “Revisiting COVID-19 policies: 10 evidence-based recommendations for where to go from here,” BMC Public Health, vol. 21, no. 1, Art. no. 2084, 2021. DOI: https://doi.org/10.1186/s12889-021-12082-z
[4] R. Filip, R. Gheorghita Puscaselu, L. Anchidin-Norocel, M. Dimian, and W. K. Savage, “Global challenges to public health care systems during the COVID-19 pandemic: A review of pandemic measures and problems,” J. Pers. Med., vol. 12, no. 8, Art. no. 1295, 2022. DOI: https://doi.org/10.3390/jpm12081295
[5] S. Gupta, K. I. Simon, and C. Wing, “Mandated and voluntary social distancing during the COVID-19 epidemic: A review,” Brookings Papers Econ. Activity, vol. 2020, no. 2, pp. 269–326, 2020, doi: 10.3386/w28139. DOI: https://doi.org/10.1353/eca.2020.0011
[6] W. Deressa, A. Worku, W. Abebe, S. Getachew, and W. Amogne, “Social distancing and preventive practices of government employees in response to COVID-19 in Ethiopia,” PLoS One, vol. 16, no. 9, Art. no. e0257112, 2021. DOI: https://doi.org/10.1371/journal.pone.0257112
[7] S. P. Otto, T. Day, J. Arino, C. Colijn, J. Dushoff, M. Li, and N. H. Ogden, “The origins and potential future of SARS-CoV-2 variants of concern in the evolving COVID-19 pandemic,” Curr. Biol., vol. 31, no. 14, pp. R918–R929, 2021. DOI: https://doi.org/10.1016/j.cub.2021.06.049
[8] B. Afrough, S. Dowall, and R. Hewson, “Emerging viruses and current strategies for vaccine intervention,” Clin. Exp. Immunol., vol. 196, no. 2, pp. 157–166, 2019. DOI: https://doi.org/10.1111/cei.13295
[9] O. Nwankwo, N. E. Obande-Ogbuinya, B. N. Ohuruogu, U. C. Ibe, T. N. Nnaji, I. M. Okafor, and C. O. Aleke, “COVID-19 pandemic: A systematic review of Nigeria situation from the global perspective between February to June 2020 of the outbreak,” Int. J. Collaborative Res. Internal Med. Public Health, vol. 14, no. 9, pp. 1–6, 2022.
[10] B. C. Uzoh, “Socio-economic effects of COVID-19 pandemic on the Nigeria working class,” Social Scientia: J. Social Sci. Humanit., vol. 5, no. 4, pp. 1–15, 2020.
[11] M. M. Alvi, S. Sivasankaran, and M. Singh, “Pharmacological and nonpharmacological efforts at prevention, mitigation, and treatment for COVID-19,” J. Drug Target., vol. 28, nos. 7–8, pp. 742–754, 2020. DOI: https://doi.org/10.1080/1061186X.2020.1793990
[12] D. Liu, L. Clemente, C. Poirier, X. Ding, M. Chinazzi, J. Davis, and M. Santillana, “Real-time forecasting of the COVID-19 outbreak in Chinese provinces: Machine learning approach using novel digital data and estimates from mechanistic models,” J. Med. Internet Res., vol. 22, no. 8, Art. no. e20285, 2020. DOI: https://doi.org/10.2196/20285
[13] P. Liu, R. Niu, J. Chen, Y. Tang, W. Tang, L. Xu, and J. Feng, “Epidemiological and clinical features in patients with coronavirus disease 2019 outside of Wuhan, China: Special focus on asymptomatic patients,” PLoS Negl. Trop. Dis., vol. 15, no. 3, Art. no. e0009248, 2021. DOI: https://doi.org/10.1371/journal.pntd.0009248
[14] S. F. Ardabili, A. Mosavi, P. Ghamisi, F. Ferdinand, A. R. Várkonyi-Kóczy, U. Reuter, and P. M. Atkinson, “COVID-19 outbreak prediction with machine learning,” Algorithms, vol. 13, no. 10, Art. no. 249, 2020. DOI: https://doi.org/10.3390/a13100249
[15] S. M. Meystre, P. M. Heider, Y. Kim, M. Davis, J. Obeid, J. Madory, and A. V. Alekseyenko, “Natural language processing enabling COVID-19 predictive analytics to support data-driven patient advising and pooled testing,” J. Am. Med. Inform. Assoc., vol. 29, no. 1, pp. 12–21, 2022. DOI: https://doi.org/10.1093/jamia/ocab186
[16] T. W. Russell, J. Hellewell, C. I. Jarvis, K. van Zandvoort, S. Abbott, R. Ratnayake, CMMID COVID-19 Working Group, et al., “Estimating the infection and case fatality ratio for coronavirus disease (COVID-19) using age-adjusted data from the outbreak on the Diamond Princess cruise ship, February 2020,” Euro Surveill., vol. 25, no. 12, Art. no. 2000256, 2020. DOI: https://doi.org/10.2807/1560-7917.ES.2020.25.12.2000256
[17] M. Abbas, T. B. Morland, E. S. Hall, and Y. El-Manzalawy, “Associations between Google search trends for symptoms and COVID-19 confirmed and death cases in the United States,” Int. J. Environ. Res. Public Health, vol. 18, no. 9, Art. no. 4560, 2021. DOI: https://doi.org/10.3390/ijerph18094560
[18] J. Xiong, O. Lipsitz, F. Nasri, L. M. Lui, H. Gill, L. Phan, D. Chen-Li, M. Iacobucci, R. Ho, A. Majeed, and R. S. McIntyre, “Impact of COVID-19 pandemic on mental health in the general population: A systematic review,” J. Affect. Disord., vol. 277, pp. 55–64, 2020. DOI: https://doi.org/10.1016/j.jad.2020.08.001
[19] K. Y. Pan, A. A. Kok, M. Eikelenboom, M. Horsfall, F. Jörg, R. A. Luteijn, and B. W. J. H. Penninx, “The mental health impact of the COVID-19 pandemic on people with and without depressive, anxiety, or obsessive-compulsive disorders: A longitudinal study of three Dutch case-control cohorts,” Lancet Psychiatry, vol. 8, no. 2, pp. 121–129, 2021. DOI: https://doi.org/10.1016/S2215-0366(20)30491-0
[20] J. Liu, J. Zhou, J. Yao, X. Zhang, L. Li, X. Xu, X. He, B. Wang, S. Fu, T. Niu, J. Yan, Y. Shi, X. Ren, C. Niu, B. Zhu, S. Zhang, B. Luo, and K. Zhang, “Impact of meteorological factors on the COVID-19 transmission: A multi-city study in China,” Sci. Total Environ., vol. 726, Art. no. 138513, 2020. DOI: https://doi.org/10.1016/j.scitotenv.2020.138513
[21] A. Kumar, D. Sinwar, and M. Saini, “Study of several key parameters responsible for COVID-19 outbreak using multiple regression analysis and multi-layer feed forward neural network,” J. Interdiscip. Math., vol. 24, no. 1, pp. 53–75, 2021. DOI: https://doi.org/10.1080/09720502.2020.1833443
[22] M. Sycinska-Dziarnowska, I. Paradowska-Stankiewicz, and K. Wozniak, “The global interest in vaccines and its prediction and perspectives in the era of COVID-19: Real-time surveillance using Google Trends,” Int. J. Environ. Res. Public Health, vol. 18, no. 15, Art. no. 7841, 2021. DOI: https://doi.org/10.3390/ijerph18157841
[23] M. Howlett and B. Cashore, “The dependent variable problem in the study of policy change: Understanding policy change as a methodological problem,” in *Theory and Methods in Comparative Policy Analysis Studies*. London, U.K.: Routledge, 2020, pp. 154–167.
[24] D. V. Lindley, “Statistical inference,” J. Roy. Stat. Soc., Ser. B, vol. 15, no. 1, pp. 30–43, 1953. DOI: https://doi.org/10.1111/j.2517-6161.1953.tb00123.x
[25] D. E. Farrar and R. R. Glauber, “Multicollinearity in regression analysis: The problem revisited,” Rev. Econ. Stat., vol. 49, no. 1, pp. 92–107, 1967. DOI: https://doi.org/10.2307/1937887
[26] C. Robinson and R. E. Schumacker, “Interaction effects: Centering, variance inflation factor, and interpretation issues,” Multiple Linear Regression Viewpoints, vol. 35, no. 1, pp. 6–11, 2009.
[27] B. Abdul-Hameed and O. G. Matanmi, “A modified Breusch–Pagan test for detecting heteroscedasticity in the presence of outliers,” Pure Appl. Math. J., vol. 10, no. 6, pp. 139–149, 2021, doi: 10.11648/j.pamj.20211006.13.
[28] V. Berenguer-Rico and I. Wilms, “Heteroscedasticity testing after outlier removal,” Econometric Rev., vol. 40, no. 1, pp. 51–85, 2021. DOI: https://doi.org/10.1080/07474938.2020.1735749
[29] S. S. Uyanto, “Power comparisons of five most commonly used autocorrelation tests,” Pakistan J. Stat. Oper. Res., vol. 16, no. 1, pp. 119–130, 2020. DOI: https://doi.org/10.18187/pjsor.v16i1.2691
[30] G. Baiocchi and W. Distaso, “GRETL: Econometric software for the GNU generation,” J. Appl. Econom., vol. 18, no. 1, pp. 105–110, 2003, doi: 10.1002/jae.704. DOI: https://doi.org/10.1002/jae.704
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Babagana Modu, Haruna Aliyu
This work is licensed under a Creative Commons Attribution 4.0 International License.
- Abstract 642
- PDF 148
