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Internet Search Effort on Covid-19 and the Underlying Public Interventions and Epidemiological Status

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Internet Search Effort on Covid-19 and the Underlying Public Interventions and Epidemiological Status Internet Search Effort on Covid-19 and the Underlying Public Interventions and Epidemiological Status Aristides Moustakas ( [email protected] ) University of Crete https://orcid.org/0000-0002-6334-747X Short Report Keywords: Data analytics, public interest, web search, epidemiology, Covid-19, public interventions Posted Date: June 14th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-583289/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/21 Abstract This study aimed to quantify the relative importance of indices of personal freedom, economy, and epidemiology on the public interest on Covid-19 expressed by internet searches on the topic. The relationship between the effective reproduction rate Rt, news media cover, and web search effort was also quantied. Data of online search in Greece on Covid-19 topic for one year were analyzed using indices of social distancing, nancial measures, and epidemiological variables using machine learning. Temporal autocorrelation of web search effort was quantied and control charts of web search, Rt, new cases and new deaths were employed. Results indicated that the trained model exhibited a t of R2 = 91% between the actual and predicted web search effort. The top ve variables for predicting web search effort were new deaths, the opening of international borders to non-Greek nationals, new cases, testing policy, and restrictions in internal movements. Web search had negligible temporal autocorrelation between weeks. Web search peaked during the same weeks that the Rt was peaking although new deaths or new cases were not peaking during those dates. The extent to which online searches may reect the actual epidemiological situation is discussed. Introduction Disease spread is a complex phenomenon and requires an interdisciplinary approach spanning from medicine to statistics and social sciences (Christakos et al. 2006). Covid-19 exhibited a global spatial spread in a relatively very short time frame resulting in being characterized as a pandemic by the World Health Organisation (WHO 2020). Quantifying human psychology or motivation to be informed about facts is admittedly a complex and potentially polarized issue (Kreps and Kriner 2020). Health-related issues are particularly dicult, as humans in an effort to protect their own or other peoples' lives may need to compromise their daily habits, personal and social freedom or nancial interests (Clinton et al. 2021). Public interest in a disease spread is a dynamical phenomenon, as different factors may dominate depending on the nature of a disease or the economic status, or the health system of the country they reside and these factors exhibit strong interaction effects (Green et al. 2020). In addition, intervention measures employed in order to control the disease are dynamically evaluated by the public (Bento et al. 2020). Quantifying Covid-19 web search interest into quantiable characteristics in a consistent and reproducible way, could facilitate explaining human preferences as well as divergences between intervention measures (Bento et al. 2020, Du et al. 2020, Zitting et al. 2021). In addition, it can provide a way to understand mass psychology during such events (Du et al. 2020, Zitting et al. 2021). To date there is no known anti-viral treatment, and vaccination against Covid-19 was implemented relatively recently (Molina et al. 2020). Therefore the available options against the virus were the immune system and health status of each individual, social distancing measures, and testing (Mack et al. 2007, Guo et al. 2020, Utsunomiya et al. 2020). Thus, there were relatively few options to be employed in order to diminish the spread of the disease (Haug et al. 2020). Page 2/21 Substantial analysis has been conducted regarding the ecacy of different intervention measures in the disease control (Haug et al. 2020). In addition, the impacts of social distancing and movement restrictions have been reported from a social and psychological perspective. However, it is hard to quantify what is the public risk perception and what is triggering the highest public attention around Covid-19 (Cori et al. 2020, Dryhurst et al. 2020). Public attention could be triggered by fear of getting sick or fear of death (Menzies and Menzies 2020, Pradhan et al. 2020). However, public interest could also be driven by fear of poverty, unemployment, or nancial insecurity, or reciprocally nancial aid or dept relief (Giordano 2020, Mamun and Ullah 2020, Esobi et al. 2021). Additional causes of public interest are disruption of individual and social freedom of movement and gathering, outgoing and entertainment, national and international travels, and school closures (Clinton et al. 2021). There clearly is a trade-off between freedom, safety, democracy, and economy (Landman and Splendore 2020, Simon 2020). Quantifying the public interest associated with the intervention measures comparable with the corresponding epidemiological parameters and ranking the relative contribution of each factor on the web search interest regarding Covid-19 may facilitate understanding elements of human nature. In this study online search effort was used as a proxy of societal interest in Covid-19 (Effenberger et al. 2020, Szmuda et al. 2020) in Greece. The study addresses two questions: (a) what are the economic, epidemiological, and social distancing characteristics that better explain online searches on Covid? (b) How is online search effort related to the Covid-19 situation in the country as expressed by the disease reproduction rate Rt? Web search effort and the corresponding epidemiological, social distancing, and economic data were analyzed using machine learning (Demertzis et al. 2020). Feature importance of each epidemiological, social distancing and economic explanatory variable on Covid-19 web search effort was quantied. The temporal autocorrelation of web search effort was quantied, and control charts of web search, Rt, new cases and new deaths were compared. Methods Web search data Data mining (Moustakas and Katsanevakis 2018) from Google Trends (https://trends.google.com/trends/) was performed in order to connect these data with the current status at the time of search - see e.g. (Effenberger et al. 2020, Szmuda et al. 2020). Google Trends is a public repository of information on online search patterns of individuals that use Google as their search engine. Data regarding search effort on the topic 'Coronavirus disease 2019' (thereby 'Covid-19') was retrieved from 26 February 2020 to 14 February 2021. Throughout the analysis week 1 (rst week) is the week 24 Feb 2020 - 1 March 2020, while week 52 (last week) is the week 8 Feb 2021 - 14 Feb 2021. The Covid-19 search topic includes searches in all major languages (covered by Google translator) conducted through a Greek IP address or internet provider. Data are normalized by Google Trends so as to represent search interest relative to the highest search term in Greece for that date. For example a value of 33% means that the term is one third as popular than the most popular searched topic on that date. The temporal resolution of the data is one week. Page 3/21 Epidemiological data New conrmed Covid-19 cases (New cases) per day in Greece were used a proxy of disease spread as a scale variable. Similarly, new conrmed Covid-19 deaths (New deaths) per day (scale variable) were used as a proxy of mortality. New cases and deaths are the epidemiological variables most commonly reported in the media. The original temporal resolution of the data was one day. As the temporal resolution of the web search effort data is one week, new cases and new deaths were averaged per week and weekly average values were employed throughout the analysis. Data regarding new Covid-19 cases and deaths per day were retrieved from the database 'Our world in data' (Roser et al. 2020) publicly available at: (Our_World_in_Data 2021b). The original data derive from the European Centre for Disease Prevention and Control (ECDC), and from John Hopkins University (Our_World_in_Data 2021b). New cases and new deaths were averaged per week to match the temporal resolution and dates of the web search effort data. Public intervention data All data were recorded at a daily temporal resolution as time series as ordinal factor variables with different factor levels (unless otherwise explicitly stated) and averaged per week to match the resolution and dates of web search effort weekly data. These are publicly available from the database 'Our world in data' (Roser et al. 2020) at: (Our_World_in_Data 2021a). A detailed description of the variables with different levels of restrictions is provided in the supplementary material. Containment and closure policies (C) The policies examined were: school closure, workplace closure, cancelation of public events, gathering restrictions, stay at home requirements, internal movement restrictions, and international travel restrictions. Economic interventions (E) The economic interventions examined included: income support, dept relief, and scal support measures (scale variable). Health system policies (H) The health system policies investigated included: testing policy, contact tracing, facial coverings, and vaccination policy. Covid-19 situation In an effort to compare the actual epidemiological situation in Greece against the web search effort, the daily score of the effective reproduction number Rt was used. The Rt is the average number of secondary infections produced when one infected individual is introduced into a susceptible host population Page 4/21 (Anderson and May 1992). It indicates the average number of people who will contract a contagious disease from one person with that disease (Pandit 2020). The Rt, is dened as: (t) where S is the number of susceptible individuals in a total population of N individuals, while R 0 is the average number of individuals infected by a single infected individual when everyone else is susceptible (Arroyo-Marioli et al. 2021). In practice Rt > 1 indicates a growing disease spread while Rt < 1 a disease spread that is diminishing, with larger values indicating higher spread.
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