Exploring online search behavior for COVID-19 preventive measures: The Philippine case

Public and Research Interest in Telemedicine From 2017 to 2022: Infodemiology Study of Google Trends Data and Bibliometric Analysis of Scientific Literature

BACKGROUND

Telemedicine offers a multitude of potential advantages, such as enhanced health care accessibility, cost reduction, and improved patient outcomes. The significance of telemedicine has been underscored by the COVID-19 pandemic, as it plays a crucial role in maintaining uninterrupted care while minimizing the risk of viral exposure. However, the adoption and implementation of telemedicine have been relatively sluggish in certain areas. Assessing the level of interest in telemedicine can provide valuable insights into areas that require enhancement.

OBJECTIVE

The aim of this study is to provide a comprehensive analysis of the level of public and research interest in telemedicine from 2017 to 2022 and also consider any potential impact of the COVID-19 pandemic.

METHODS

Google Trends data were retrieved using the search topics "telemedicine" or "e-health" to assess public interest, geographic distribution, and trends through a joinpoint regression analysis. Bibliographic data from Scopus were used to chart publications referencing the terms "telemedicine" or "eHealth" (in the title, abstract, and keywords) in terms of scientific production, key countries, and prominent keywords, as well as collaboration and co-occurrence networks.

RESULTS

Worldwide, telemedicine generated higher mean public interest (relative search volume=26.3%) compared to eHealth (relative search volume=17.6%). Interest in telemedicine remained stable until January 2020, experienced a sudden surge (monthly percent change=95.7%) peaking in April 2020, followed by a decline (monthly percent change=-22.7%) until August 2020, and then returned to stability. A similar trend was noted in the public interest regarding eHealth. Chile, Australia, Canada, and the United States had the greatest public interest in telemedicine. In these countries, moderate to strong correlations were evident between Google Trends and COVID-19 data (ie, new cases, new deaths, and hospitalized patients). Examining 19,539 original medical articles in the Scopus database unveiled a substantial rise in telemedicine-related publications, showing a total increase of 201.5% from 2017 to 2022 and an average annual growth rate of 24.7%. The most significant surge occurred between 2019 and 2020. Notably, the majority of the publications originated from a single country, with 20.8% involving international coauthorships. As the most productive country, the United States led a cluster that included Canada and Australia as well. European, Asian, and Latin American countries made up the remaining 3 clusters. The co-occurrence network categorized prevalent keywords into 2 clusters, the first cluster primarily focused on applying eHealth, mobile health (mHealth), or digital health to noncommunicable or chronic diseases; the second cluster was centered around the application of telemedicine and telehealth within the context of the COVID-19 pandemic.

CONCLUSIONS

Our analysis of search and bibliographic data over time and across regions allows us to gauge the interest in this topic, offer evidence regarding potential applications, and pinpoint areas for additional research and awareness-raising initiatives.

How COVID-19 Pandemic Has Influenced Public Interest in Foods: A Google Trends Analysis of Italian Data

Controversy exists about the impact of the COVID-19 pandemic on dietary habits, with studies demonstrating both benefits and drawbacks of this period. We analyzed Google Trends data on specific terms and arguments related to different foods (i.e., fruits, vegetables, legumes, whole grains, nuts and seeds, milk, red meat, processed meat, and sugar-sweetened beverages) in order to evaluate the interest of Italian people before and during the COVID-19 pandemic. Joinpoint regression models were applied to identify the possible time points at which public interest in foods changed (i.e., joinpoints). Interestingly, public interest in specific food categories underwent substantial changes during the period under examination. While some changes did not seem to be related to the COVID-19 pandemic (i.e., legumes and red meat), public interest in fruit, vegetables, milk, and whole grains increased significantly, especially during the first lockdown. It should be noted, however, that the interest in food-related issues returned to prepandemic levels after the first lockdown period. Thus, more efforts and ad hoc designed studies should be encouraged to evaluate the duration and direction of the COVID-19 pandemic's influence.

Personal network protects, social media harms: Evidence from two surveys during the COVID-19 pandemic

Background

The classic debate regarding the complex relationships between personal network, social media use, and mental well-being requires renewed examination in the novel context of pandemic-related social isolation.

Data and method

We present two surveys conducted at (i) the earlier months of the pandemic and (ii) the end of large scale social-lockdown measures in the U.S. to explore the social and behavioral antecedents of mental health states relating to social media use. Study 1 tracked the longitudinal changes of personal network, social media use, and anxiety level of a group of individuals (N = 147) over a three-month period during the pandemic. Study 2 replicated and extended the theoretical model to a race-representative U.S. adult sample (N = 258).

Results

Both studies consistently show that (1) more time on social media worsens anxiety. It also mediates the relationship between personal network size and anxiety. That is, a small personal network predicts more social media use, which is in turn related to increased anxiety. (2) Moreover, the effect of social media use on anxiety is mainly explained by news consumption on social media, rather than non-news related usage. (3) This link's strength is moderated by one's perception of COVID-19 impact, such that news consumption on social media increases anxiety more when the perceived impact is higher.

Conclusion

These results demonstrate communication technologies' increasingly critical and multifaceted role in affecting mental health conditions.

Assessing the online search behavior for COVID-19 outbreak: Evidence from Iran

Introduction

Google Trends (GT) is an important free tool for online search behavior analysis, which provides access to Internet search patterns in Google. In recent decades, this database has been used for predicting the outbreak of epidemics and pandemics in different regions of the world. The present study aimed to evaluate Iranian users’ COVID-19-related online search behavior.

Methods

This longitudinal study was conducted in 2021. The data of Iranian users’ COVID-19-related online search behavior (trend) were collected from the GT website, and the epidemiological data of the COVID-19 outbreak in Iran from 16 February 2020 to 2 January 2021 were sourced from the Iranian ministry of health and medical education, as well as the World Health Organization. The data were analyzed in SPSS using descriptive and inferential statistics.

Results

All the COVID-19-related search terms in Iran gained their highest popularity value (relative search volume = 100) in the first 8 weeks of the pandemic, and then this value assumed a decreasing trend over time. Based on factor analysis, relative search volume (RSV) of factor 1 terms (related to corona [in Persian] and corona) have a low significance relationship with COVID-19 epidemiological data in one-, two-, and three-week time lags. Although, RSV of factor 2 terms (related to COVID [in Persian], COVID-19, and coronavirus) correlated with the total weekly number of COVID-19 cases in mentioned time lags.

Conclusion

COVID-19-related search terms were popular among Iranian users at the beginning of the pandemic. The online search queries and the key terms searched by Iranian users varied during the COVID-19 pandemic. This study provides evidence in favor of the adoption of GT as an epidemiological surveillance tool but, it is necessary to consider that mass media and other confounders can significantly influence RSVs.

How COVID-19 Has Influenced Public Interest in Antimicrobials, Antimicrobial Resistance and Related Preventive Measures: A Google Trends Analysis of Italian Data

Google Trends analytics is an innovative way to evaluate public interest in antimicrobial resistance (AMR) and related preventive measures. In the present study, we analyzed Google Trends data in Italy, from 2016 to 2021. A joinpoint analysis was performed to assess whether and how annual campaigns and the COVID-19 pandemic affected public interest in antimicrobials, AMR, hand hygiene, and the use of disinfectant. For the terms "antimicrobials" and "antimicrobial resistance", no joinpoints were detected around the time of the World Antimicrobial Awareness Week. Similarly, the COVID-19 pandemic seems to have had no effect on public interest in this term. For the term "handwashing", no joinpoints were detected around World Hand Hygiene Day or Global Handwashing Day. However, three joinpoints were detected around the peak of interest observed in March 2020, after the beginning of the COVID-19 pandemic. Comparable results were obtained for the term "disinfectant". These findings show that the influence of annual campaigns on public interest in AMR and preventive measures was modest and not long-term. The COVID-19 pandemic, meanwhile, had no effect on AMR but raised awareness on preventive measures. However, this was a temporary rather than long-term outcome. Thus, different policies, strategies, and measures should be designed to advocate prevention of AMR in the COVID-19 era.

Trends in online searching toward suicide pre-, during, and post the first wave of COVID-19 outbreak in China

COVID-19 may increase the risk of suicide, but the conclusion is still unclear. This study was designed to assess the impact of COVID-19 on suicide pre-, during, and post the first wave of COVID-19 in China. It was reported that online public searching was associated with their offline thoughts and behaviors. Therefore, this study was designed to explore the online search for suicide pre-, during, and post-COVID-19 in China. The keywords on suicide, COVID-19, unemployment, and depression were collected in 2019 and 2020 using the Baidu Search Index (BSI). A time-series analysis examined the dynamic correlations between BSI-COVID-19 and BSI-suicide. A generalized estimating equation model was used to calculate the coefficients of variables associated with the BSI-suicide. The BSI-suicide showed a significant increase (15.6%, p = 0.006) from the 5th to 9th week, which was also the point of the first wave of the COVID-19 outbreak. A time-series analysis between BSI-suicide and BSI-COVID-19 showed that the strongest correlation occurred at lag 1+ and lag 2+ week. In the pre-COVID-19 model, only BSI-depression was highly associated with BSI-suicide (β = 1.38, p = 0.008). During the COVID-19 model, BSI-depression (β = 1.77, p = 0.040) and BSI-COVID-19 (β = 0.03, p < 0.001) were significantly associated with BSI-suicide. In the post-COVID-19 model, BSI depression (β = 1.55, p = 0.010) was still highly associated with BSI-suicide. Meanwhile, BSI-unemployment (β = 1.67, p = 0.007) appeared to be linked to BSI-suicide for the first time. There was a surge in suicide-related online searching during the early stage of the first wave of the COVID-19 outbreak. Online suicide search volume peaked 1-2 weeks after the COVID-19 peak. The BSI of factors associated with suicide varied at different stages of the COVID-19 pandemic. The findings in this study are preliminary and further research is needed to arrive at evidence of causality.

Predicting New Daily COVID-19 Cases and Deaths Utilizing Search Engine Query Data in South Korea from 2020 to 2021: Infodemiology Study

Background

Given the ongoing COVID-19 pandemic situation, accurate predictions could greatly help in the health resource management for future waves. However, as a new entity, COVID-19’s disease dynamics seemed difficult to predict. External factors, such as internet search data, need to be included in the models to increase their accuracy. However, it remains unclear whether incorporating online search volumes into models leads to better predictive performances for long-term prediction.

Objective

The aim of this study was to analyze whether search engine query data are important variables that should be included in the models predicting new daily COVID-19 cases and deaths in short- and long-term periods.

Methods

We used country-level case-related data, NAVER search volumes, and mobility data obtained from Google and Apple for the period of January 20, 2020, to July 31, 2021, in South Korea. Data were aggregated into four subsets: 3, 6, 12, and 18 months after the first case was reported. The first 80% of the data in all subsets were used as the training set, and the remaining data served as the test set. Generalized linear models (GLMs) with normal, Poisson, and negative binomial distribution were developed, along with linear regression (LR) models with lasso, adaptive lasso, and elastic net regularization. Root mean square error values were defined as a loss function and were used to assess the performance of the models. All analyses and visualizations were conducted in SAS Studio, which is part of the SAS OnDemand for Academics.

Results

GLMs with different types of distribution functions may have been beneficial in predicting new daily COVID-19 cases and deaths in the early stages of the outbreak. Over longer periods, as the distribution of cases and deaths became more normally distributed, LR models with regularization may have outperformed the GLMs. This study also found that models performed better when predicting new daily deaths compared to new daily cases. In addition, an evaluation of feature effects in the models showed that NAVER search volumes were useful variables in predicting new daily COVID-19 cases, particularly in the first 6 months of the outbreak. Searches related to logistical needs, particularly for “thermometer” and “mask strap,” showed higher feature effects in that period. For longer prediction periods, NAVER search volumes were still found to constitute an important variable, although with a lower feature effect. This finding suggests that search term use should be considered to maintain the predictive performance of models.

Conclusions

NAVER search volumes were important variables in short- and long-term prediction, with higher feature effects for predicting new daily COVID-19 cases in the first 6 months of the outbreak. Similar results were also found for death predictions.