Access the full text.
Sign up today, get DeepDyve free for 14 days.
M. Rieger, Mei Wang (2021)
Trust in Government Actions During the COVID-19 CrisisSocial Indicators Research, 159
Alina Glaubitz, Feng Fu (2020)
Oscillatory dynamics in the dilemma of social distancingProceedings. Mathematical, Physical, and Engineering Sciences, 476
P. Fine, K. Eames, D. Heymann (2011)
"Herd immunity": a rough guide.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 52 7
Journal of NBC Protection Corps, 4
A. Mavragani, Gabriela Ochoa (2019)
Google Trends in Infodemiology and Infoveillance: Methodology FrameworkJMIR Public Health and Surveillance, 5
(2020)
COVID-19 Pandemic: Analysis of Possible Scenarios for the Development of the Epidemic in RussiaJournal of NBC Protection Corps
O. Santangelo, S. Provenzano, D. Piazza, D. Giordano, G. Calamusa, A. Firenze (2019)
Digital epidemiology: assessment of measles infection through Google Trends mechanism in Italy.Annali di igiene : medicina preventiva e di comunita, 31 4
Maria Nicola, Z. Alsafi, C. Sohrabi, Ahmed Kerwan, A. Al-Jabir, Christos Iosifidis, M. Agha, R. Agha (2020)
The socio-economic implications of the coronavirus pandemic (COVID-19): A reviewInternational Journal of Surgery (London, England), 78
Robert Glass, Laura Glass, W. Beyeler, H. Min (2006)
Targeted Social Distancing Designs for Pandemic InfluenzaEmerging Infectious Diseases, 12
Carly Ziegler, Vincent Miao, A. Owings, Andrew Navia, Ying Tang, Joshua Bromley, Peter Lotfy, M. Sloan, Hannah Laird, Haley Williams, Micayla George, Riley Drake, Taylor Christian, A. Parker, C. Sindel, Molly Burger, Yilianys Pride, M Hasan, G. Abraham, M. Senitko, Tanya Robinson, A. Shalek, S. Glover, B. Horwitz, J. Ordovas-Montañes (2021)
Impaired local intrinsic immunity to SARS-CoV-2 infection in severe COVID-19Cell, 184
X. Zou, Wenfei Zhu, Lei Yang, Y. Shu (2015)
[Google Flu Trends--the initial application of big data in public health].Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine], 49 6
Darshan Gandhi, Rohan Sukumaran, Priyanshi Katiyar, A. Radunsky, S. Anand, S. Advani, Jil Kothari, K. Jakimowicz, Sheshank Shankar, V. SethuramanT., Krutika Misra, Aishwarya Saxena, S. Landage, Richa Sonker, Parth Patwa, Aryan Mahindra, Mikhail Dmitrienko, Kanishka Vaish, Ashley Mehra, Srinidhi Murali, Rohan Iyer, Joseph Bae, Vivek Sharma, Abhishek Singh, Rachel Barbar, R. Raskar (2020)
Digital Landscape of COVID-19 Testing: Challenges and OpportunitiesArXiv, abs/2012.01772
Max Roser, Hannah Ritchie, Esteban Ortiz-Ospina, Joe Hasell (2020)
Coronavirus Pandemic (COVID-19)
Oyungerel Byambasuren, M. Cardona, K. Bell, J. Clark, M. McLaws, P. Glasziou (2020)
Estimating the extent of asymptomatic COVID-19 and its potential for community transmission: Systematic review and meta-analysis.Journal of the Association of Medical Microbiology and Infectious Disease Canada = Journal officiel de l'Association pour la microbiologie medicale et l'infectiologie Canada, 5 4
N. Wilson, K. Mason, M. Tobias, M. Peacey, Q. Huang, M. Baker (2009)
Interpreting Google flu trends data for pandemic H1N1 influenza: the New Zealand experience.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin, 14 44
Lori Ayre, Jim Craner (2017)
Open Data: What It Is and Why You Should CarePublic Library Quarterly, 36
S. Roberts (2020)
Flattening the coronavirus curve
J. Bousquet, I. Agache, J. Anto, K. Bergmann, C. Bachert, Isabella, Annesi-Maesano, P. Bousquet, G. D'Amato, P. Demoly, Govert Vries, E. Eller, W. Fokkens, J. Fonseca, T. Haahtela, P. Hellings, Jocelyne Just, Thomas Keil, L. Klimek, P. Kuna, K. Carlsen, R. Mösges, Ruth, Murray, Kristof Nekam, G. Onorato, Nikos Papadopoulos, B. Samoliński, P. Schmid‐Grendelmeier, M. Thibaudon, P. Tomazic, Massimo Triggiani, Arunas, Valiulis, E. Valovirta, Michiel Eerd, M. Wickman, Torsten Zuberbier, Aziz, Sheikh (2017)
Google Trends terms reporting rhinitis and related topics differ in European countriesAllergy, 72
The study aims to help epidemiologists identify new patterns and trends in spreading infections on the example of the current coronavirus disease 2019 (COVID-19) pandemic using data from search engines. The study identified the types of thematic search of Russian Internet users and queries that have a mathematically confirmed correlation with public health indicators: mortality and morbidity from COVID-19. The study aims to determine digital epidemiology search trends to the current COVID-19 pandemic. The study identified the types of thematic search of RuNet users and queries that have a mathematically confirmed correlation with public health indicators: mortality and morbidity from COVID-19.Design/methodology/approachThe authors explored two types of data: (1) the monthly datasets of keywords relevant to COVID-19 extracted from the Yandex search engine and (2) officially published statistics data. Alongside, the authors searched for associations between all variables in this dataset. The Benjamin–Hochberg correction for multiple hypothesis testing was applied to the obtained results to improve the reliability of the results. The authors built a unique website with opportunities to update datasets and designed dashboards to visualize the research outcomes using PHP and Python.FindingsThe research results show the number of significant relationships that the authors interpreted in epidemiology as a new instrument in Public Health research. There are 132 data combinations with a correlation higher than 75%, making it possible to determine a mathematically reliable relationship between search statistics trends and mortality/morbidity indicators. The most statistically significant effects identified in bundles “query” – “query”, “query” – “morbidity”, “query” – “mortality”.Originality/valueThe authors developed a new approach in analyzing outbreaks of infections and their consequences based on a comprehensive analysis of epidemiological and infodemic data. The research results are relevant to public health as other decision-making and situational analysis tools for citizens and specialists who want to receive additional confirmation for the indicators of the official statistics of the headquarters for control and monitoring of the situation with coronavirus and others infections.
International Journal of Health Governance – Emerald Publishing
Published: May 6, 2022
Keywords: Public health; Statistics
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.