Global Trends in Social Prescribing: Web-Based Crawling Approach.

BACKGROUND: Social loneliness is a prevalent issue in industrialized countries that can lead to adverse health outcomes, including a 26% increased risk of premature mortality, coronary heart disease, stroke, depression, cognitive impairment, and Alzheimer disease. The United Kingdom has implemented a strategy to address loneliness, including social prescribing-a health care model where physicians prescribe nonpharmacological interventions to tackle social loneliness. However, there is a need for evidence-based plans for global social prescribing dissemination. OBJECTIVE: This study aims to identify global trends in social prescribing from 2018. To this end, we intend to collect and analyze words related to social prescribing worldwide and evaluate various trends of related words by classifying the core areas of social prescribing. METHODS: Google's searchable data were collected to analyze web-based data related to social prescribing. With the help of web crawling, 3796 news items were collected for the 5-year period from 2018 to 2022. Key topics were selected to identify keywords for each major topic related to social prescribing. The topics were grouped into 4 categories, namely Healthy, Program, Governance, and Target, and keywords for each topic were selected thereafter. Text mining was used to determine the importance of words collected from new data. RESULTS: Word clouds were generated for words related to social prescribing, which collected 3796 words from Google News databases, including 128 in 2018, 432 in 2019, 566 in 2020, 748 in 2021, and 1922 in 2022, increasing nearly 15-fold between 2018 and 2022 (5 years). Words such as health, prescribing, and GPs (general practitioners) were the highest in terms of frequency in the list for all the years. Between 2020 and 2021, COVID, gardening, and UK were found to be highly related words. In 2022, NHS (National Health Service) and UK ranked high. This dissertation examines social prescribing-related term frequency and classification (2018-2022) in Healthy, Program, Governance, and Target categories. Key findings include increased "Healthy" terms from 2020, "gardening" prominence in "Program," "community" growth across categories, and "Target" term spikes in 2021. CONCLUSIONS: This study's discussion highlights four key aspects: (1) the "Healthy" category trends emphasize mental health, cancer, and sleep; (2) the "Program" category prioritizes gardening, community, home-schooling, and digital initiatives; (3) "Governance" underscores the significance of community resources in social prescribing implementation; and (4) "Target" focuses on 4 main groups: individuals with long-term conditions, low-level mental health issues, social isolation, or complex social needs impacting well-being. Social prescribing is gaining global acceptance and is becoming a global national policy, as the world is witnessing a sharp rise in the aging population, noncontagious diseases, and mental health problems. A successful and sustainable model of social prescribing can be achieved by introducing social prescribing schemes based on the understanding of roles and the impact of multisectoral partnerships.

Global Online Interest in HIV/AIDS care Services in the time of COVID-19: A Google Trends Analysis.

The Covid-19 pandemic has compounded the challenge of HIV/AIDS elimination, creating difficulties in accessing HIV care services such as early testing and treatment. This paper characterized the global online interest in HIV care services-related search terms before and during the pandemic. Global online search interest for HIV was measured using the Google Trends™ database. Spearman's rank-order correlation correlated country-specific characteristics and HIV prevalence data with the search volume index (SVI). We found a significant decrease in the global online search interest for HIV/AIDS care services-related search terms during the Covid-19 pandemic. The top countries with the highest online interest for "HIV/AIDS" search terms were Zambia, Eswatini, Malawi, Lesotho, and Zimbabwe. In addition, search volume indices for HIV correlated positively with HIV prevalence and negatively with GDP, GDP per capita, and the number of physicians. This result highlights that resource-poor countries with a high prevalence of HIV have a high online interest in HIV/AIDS. Therefore, there is a need to improve internet access, the quality of HIV-related health information, and online health literacy to improve health-seeking behavior, especially in areas with a high disease burden. Overall, our study shows that the infodemiologic approach through Google Trends™ can be used to assess the online interest of the public toward HIV infection and related healthcare services.

Medical waste generation during COVID-19 pandemic in selected member countries of Arabian Gulf region, Google trend analysis

The variant SARS-CoV-2 (COVID-19) has affected the entire globe;it has caused acute respiratory illnesses and has led to massive environmental problems associated with extra medical waste and single-use plastics. Herein the total quantity of waste generated in the selected GCC countries from different sources has been calculated. The popularity of the medical waste issue through using relative search volume was investigated to associate it with the quantities of Waste. Data was collected from the first COVID-19 case in the respective country until March 26, 2022, from the public domains, "Worldmeter”, "Our World in Data” and "Google Trends”. Autoregressive Integrated Moving Average (ARIMA) was used to predict the amount of CMW. The calculated values are 1107.980 kg/day from the facemask used by the public, 2,554,812 kg from vaccination, 2,595,772 kg from PCR tests, also the predicted daily total amount of COVID-19 related Medical Waste (CMW) by the end of 2022 (December 31, 2022) to be 3667.25 kg/day. The trend in the generation of CMW has been identified in selected countries. Generally, the waste is expected to get minimized, but it is a lesson for future planning. Especially With extensive vaccination, the cases are drastically decreased, and life is resuming in the regions. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the University of Bahrain.

COVID-19 Related Early Google Search Behavior and Health Communication in the United States: Panel Data Analysis on Health Measures.

The COVID-19 outbreak at the end of December 2019 spread rapidly all around the world. The objective of this study is to investigate and understand the relationship between public health measures and the development of the pandemic through Google search behaviors in the United States. Our collected data includes Google search queries related to COVID-19 from 1 January to 4 April 2020. After using unit root tests (ADF test and PP test) to examine the stationary and a Hausman test to choose a random effect model, a panel data analysis is conducted to investigate the key query terms with the newly added cases. In addition, a full sample regression and two sub-sample regressions are proposed to explain: (1) The changes in COVID-19 cases number are partly related to search variables related to treatments and medical resources, such as ventilators, hospitals, and masks, which correlate positively with the number of new cases. In contrast, regarding public health measures, social distancing, lockdown, stay-at-home, and self-isolation measures were negatively associated with the number of new cases in the US. (2) In mild states, which ranked one to twenty by the average daily new cases from least to most in 50 states, the query terms about public health measures (quarantine, lockdown, and self-isolation) have a significant negative correlation with the number of new cases. However, only the query terms about lockdown and self-isolation are also negatively associated with the number of new cases in serious states (states ranking 31 to 50). Furthermore, public health measures taken by the government during the COVID-19 outbreak are closely related to the situation of controlling the pandemic.

An Analysis of Google Trends During COVID-19: Determining Public Urological Cancer Concerns.

Background The COVID-19 pandemic put a massive strain on the healthcare system as patients avoided the hospital, elective cases were postponed, and general medical anxiety was increased. We aimed to capture public interest in urological cancers during this massive shock to the medical field. Methodology A total of 12 keywords related to the three most prevalent urological cancers (prostate, bladder, and kidney) were searched using Google Trends from 2018 to 2022. The search volume index of these 12 keywords was extracted to assess public interest before and after the pandemic. Results There was a reduction in search volume for "prostate, bladder, and kidney cancer" and "kidney cancer treatment" after the postponement of elective surgeries. However, there was an increase in search volume for "prostate, bladder, and kidney cancer survival rates" and "prostate cancer symptoms" after this period. There was no change in search volume for bladder cancer symptoms, bladder cancer treatment, or kidney cancer symptoms. Conclusions Public interest in urological cancers decreased after COVID, while interest in survival rates across all three cancers increased. Future research is needed to investigate the effects of changing priorities and delays in medical care on patients' experiences with urological cancers.

Exploration of Correlations between COVID-19 Vaccination Choice and Public Mental Health Using Google Trend Search.

Since the global COVID-19 pandemic has great impact on human health and life style, the vaccination is the most effective method for disease control and prevention. However, not all people are willing to be vaccinated because some critical factors affect vaccination aspiration and vaccine choice of the public population. Among these factors, public mental health belongs to a political issue. In this study, Google Trend Search was used to explore the correlation between COVID-19 vaccination choice and public mental health during the period from August/2020 to December/2021. The results suggested that the main public concerns of COVID-19-related mental illnesses are positively correlated with the new cases amount but are negatively correlated with total cases and vaccinated cases amount. Moreover, the results support that the public population took more interest in the Pfizer/BNT COVID vaccine and Moderna COVID vaccine than the AstraZeneca COVID vaccine. Our study shows that investigations of the public mental health should be set up and conducted widely. A complete vaccination program combined with a policy for the improvement of public mental health are very effective for the control and prevention of COVID-19.

Insomnia as a concerned mental health issue during COVID-19 pandemic: A google trend analysis

Introduction: Insomnia is one of the most common major health issues during the COVID-19 pandemic. There has been limited evidence that showed the correlation between insomnia and COVID-19 using Google trend. Objective(s): To investigate the impact of the COVID-19 pandemic on interest in insomnia, including national mental health by a Google trend analysis that implicitly represents the state of distress and concern for this pandemic. Method(s): We examined the Google trend search query data from these sleep-related keywords: insomnia, restless leg, and obstructive sleep apnea (OSA) from 1 Jan 2020 to 30 May 2020 and explored the correlation between the internet search volumes for insomnia and the cumulative number of new COVID-19 cases. In addition, we investigated the internet search pattern over time, before and during the COVID-19 pandemic. Result(s): During the early phase of the COVID-19 pandemic between January and May 2020, the Relative Search Volumes (RSV) curves showed that the cumulative number of new COVID-19 cases was significantly correlated with the rising search for these keywords linking to sleep-related conditions as follows: 'insomnia' (r = 0.41, p < 0.001), and 'restless leg' (r = 0.19, p = 0.009). However, it was not correlated with the keyword 'OSA' (r = -0.14, p = 0.07). Conclusion(s): These findings emphasize the impact of the COVID- 19 pandemic on insomnia and the crucial need for public mental health interventions to be offered and accessible. The Google trend could be used as a new tool for public mental health surveillance in a new normal lifestyle.

COVID-19 Infodemiology: Association Between Google Search and Vaccination in Malaysian Population.

Background In light of the ongoing coronavirus disease 2019 (COVID-19) pandemic, vaccination is one of the most important defensive strategies in combating the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Vaccine hesitancy or anti-vaccination attitude has become a barrier to the nationwide vaccination program, potentially sabotaging the effectiveness of vaccination. Thus far, Google Trends (GT) has been used extensively for monitoring information-seeking behavior during the pandemic. We aimed to investigate the association between Google search, the vaccination rate, and the number of vaccinated and infected cases among the Malaysian population. Material and method GT's customizable geographic and temporal filters were applied to include results for predetermined keywords from January 1, 2021, to December 31, 2021. Both Malay and English languages were used to reflect the multi-racial and multi-lingual community in Malaysia. The search volume index (SVI) derived was compared with the numbers of vaccinated and infected cases which were extracted from the open-access database (COVIDNOW in Malaysia) within the same period. Both analyses were performed independently by two authors to ensure accuracy of the data extraction process. A descriptive analysis was used to compare GT analyses and the number of daily vaccinations and positive COVID-19 cases. Results The information-seeking behavior in the public fluctuated from time to time. The interest surged during the initiation of vaccination program and upon the outbreak of COVID-19 in Malaysia. The surge in interest prior to the peak of vaccination rate also indicated that the public tended to get information online prior to getting the vaccines.  Conclusion This observational study illustrates the ability of GT to monitor the interest of vaccination among the Malaysian population during the pandemic. By monitoring the dynamic changes in Google Trends, healthcare authorities can get a glimpse of public perceptions such as attitude towards COVID-19 vaccine, hence potentially identify and stymie any dangerous online anti-vaccination rhetoric swiftly.

Forecast of Aviation Traffic in Indonesia Based on Google Trend and Macroeconomic Data using Long Short-Term Memory

The COVID-19 pandemic has impacted many sectors. For example, in the aviation sector, flight traffic went down drastically with no certainty of being recovered. This calls for a methodology to predict the flight traffic to provide strategic planning on flight schedules operational, route structuring, and flight navigation service cost determination. However, current developments mainly focus on flight traffic forecasting based on historical data without considering external factors. In this study, we propose the Long Short-Term Memory (LSTM) technique to forecast flight traffic in Indonesia involving external variables such as macroeconomic variables and Google Trends. LSTM is proposed because of its flexibility to model non-linear time series data and has a good reputation for predictive accuracy. We first select a few among Google Trends and macroeconomic variables using nonlinearity analysis and cross-correlation function (CCF). We then employ the selected variables to forecast the flight traffic and compare it to the one using only historical flight traffic data. Our results concluded, based on the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), that the model involving google trend outperforms the other three models, i.e., the model with only historical data, the model with macroeconomics, and the model with both macroeconomic and Google Trends. It is because, in this digital era, Google Trends can reflect population psychology in an up-to-date manner. © 2022 IEEE.

The sum of all SCARES COVID-19 sentiment and asset return.

In this study, I constitute a search based COVID-19 sentiment index using Google search volume. I develop an alternative Scared COVID-19 Attitude Revealed by Eager Search (SCARES) index using the household search volume i.e. "coronavirus pandemic", "coronavirus epidemic", and "coronavirus outbreak" of United States (US) during the COVID-19 pandemic. Using daily data from May 1, 2020 to July 30, 2021, I find that SCARES index negatively explains stock market return and subsequent return reversals, implying that households' increased pandemic sentiment negatively affects equity market return. Furthermore, decile regressions on characteristics-sorted portfolio returns show that SCARES index predicts the return reversals of firms that are small, less profitable, and with low investment. I also report that COVID-19 search shocks of households do not significantly predict any of the Fama-French five-factors except SMB (small-minus-big). Moreover, I use two state Markov switching model and find that structural breaks associated with pandemic phases make SCARES positively related to indices i.e. twitter based uncertainty, volatility index, economic policy uncertainty, and business condition in high volatility regime. Finally, sub-period analysis reports that, in stock market context, people start to react slowly and become relatively less responsive to the COVID-19 search keywords. The findings of this paper can assist key stakeholders in the market to carefully analyze the asset return pattern during pandemic regimes.

PREDICTION OF THE DEVELOPMENT OF COVID-19 CASE IN INDONESIA BASED ON GOOGLE TREND ANALYSIS

The global outbreak of the coronavirus disease (COVID-19) has recently hit many countries around the world. Indonesia is one of the 10 most affected countries. Search engines such as Google provide data on search activity in a population, and this data may be useful for analyzing epidemics. Leveraging data mining methods on electronic resource data can provide better insights into the COVID-19 outbreak to manage health crises in every country and around the world. This study aims to predict the incidence of COVID-19 by utilizing data from the Covid 19 Task Force and the Google Trends website. Linear regression and long-term memory (LSTM) models were used to estimate the number of positive COVID-19 cases. [ FROM AUTHOR] Copyright of Eduvest: Journal Of Universal Studies is the property of Green Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Google Searches and COVID-19 Cases in Saudi Arabia: A Correlation Study

The outbreak of the new coronavirus disease (COVID-19) has affected human life to a great extent on a worldwide scale. During the coronavirus pandemic, public health professionals at the early outbreak faced an extraordinary challenge to track and quantify the spread of disease. To investigate whether a digital surveillance model using google trends (GT) is feasible to monitor the outbreak of coronavirus in the Kingdom of Saudi Arabia. We retrieve GT data using ten common COVID-19 symptoms related keywords from March 2, 2020, to October 31, 2020. Spearman correlation were performed to determine the correlation between COVID-19 cases and the Google search terms. GT data related to Cough and Sore Throat were the most searched symptoms by the Internet users in Saudi Arabia. The highest daily correlation found with the Loss of Smell followed by Loss of Taste and Diarrhea. Strong correlation as well was found between the weekly confirmed cases and the same symptoms: Loss of Smell, Loss of Taste and Diarrhea. We conducted an investigation study utilizing Internet searches related to COVID-19 symptoms for surveillance of the pandemic spread. This study documents that google searches can be used as a supplementary surveillance tool in COVID-19 monitoring in Saudi Arabia. © 2021 IEEE.

Defining Causality in Covid-19 and Google Search Trends in Java, Indonesia Cases: A Retrospective Analysis

The Coronavirus disease 2019 (Covid-19) has led all countries around the world to the unpredicted situation. It is such a crucial to investigate novel approaches in predicting the future behaviour of the outbreak. In this paper, Google trend analysis will be employed to analyse the seek pattern of Covid-19 cases. The first method to investigate the seek information behaviour related to Covid-19 outbreak is using lag-correlation between two time series data per regional data. The second method is used to encounter the cause-effect relation between time series data. We apply statistical methods for causal inference in epidemics. Our focus is on predicting the causal-effect relationship between information-seeking patterns and Google search in the Covid-19 pandemic. We propose the using of Granger Causality method to analyse the causal relation between incidence data and Google Trend Data. © 2021, Indonesian Biomathematical Society. All rights reserved.

Online searching trend on Covid-19 using Google trend: infodemiological study in Malaysia.

Since January 2020, the emergence of Covid-19 has sparked a worldwide search for information about Covid-19. People frequently use the internet to search the information on the virus. However, the pandemic have triggered the information-seeking trends among public. As a result, large amount of information could lead to infodemic. It will create public concerned such as panic and paranoid because this information spread rapidly. The aim of this study is to analyze information about Covid-19 that has been searched in Malaysia. We investigated online search behavior related to the Covid-19 outbreak among public by using Google Trends to understand public searching behavior on Covid-19. The findings from this study can be used as a tool to monitor public searching activities on Covid-19, which could predict future action regarding the outbreak.

Did the COVID-19 Pandemic Spark a Public Interest in Pet Adoption?

This study aimed to determine if there has been an increase of global interest on pet adoption immediately after the WHO declaration of the pandemic and if the effect has been sustainable in 8 months on. We conducted a Google Trends search using keywords related to pet adoption. Relative search volume (RSV) was scored between 0 and 100 for the lowest and the highest, respectively. Top countries contributing to the dataset included Australia, the United States, Canada, New Zealand, the United Kingdom, Singapore, the Philippines, and Malaysia. From 2015 through 2020, the worldwide RSV for the categories of pet, dog and cat adoption peaked between April and May 2020, the early epidemic phase of the pandemic. These were significantly higher than the 5-year worldwide average RSV for all three categories (P = 0.001). Comparing to the same period in 2019, the RSV ratio (2020/2019) for both dog and cat adoption increased by up to 250%. Nonetheless, the RSV for dog adoption has been decreasing since July 2020 and returned to the 5-year average by December 2020. In contrast, the interest in cat adoption remained sustainably high, possibly reflecting the feline acclimation to indoor living. In conclusion, the global interest in pet adoptions surged in the early phase of the pandemic but not sustainable. With the launch of COVID-19 vaccines, there is a concern for separation anxiety and possible abandonment of these newly adopted pets when the owners would leave their homes for work in the future.

Can google trends search inform us about the population response and public health impact of abrupt change in alcohol policy? A case study from India during the covid-19 pandemic.

BACKGROUND: Sudden alcohol prohibition in India during the COVID-19 pandemic presented an opportunity to test whether Google Trends data could indicate population responses and the public health impact of alcohol policy. We hypothesized, following prohibition: there would be a significant change in the relative search volumes (RSV) of alcohol-related queries; that temporal analysis of the trends would reflect a public response to policy changes; and that geospatial analysis of RSV would correlate with the prevalence of alcohol use. METHODS: Three different search periods were used to test the hypotheses. The search inputs were based on potential public response to alcohol prohibition, as evidenced by the literature, newspaper articles, and consensus. We used RSV as the unit of analysis. Mean RSV of search queries, pre-post implementation of prohibition, were compared. Smoothing of scatter plots examined the temporal association of trends with policy measures. Multiple linear regression tested the relationship of state-wise RSV and alcohol use prevalence. RESULTS: Post-implementation of prohibition, a significant increase in the RSV was observed for searches related to alcohol withdrawal (p<0.001), how to extract alcohol from sanitizer (p = 0.002), alcohol home delivery online (p<0.001), alcohol home delivery (p<0.001), and sleeping pills (p = 0.006). The trends suggested a decrease in general interest in alcohol but increased demand, and a possible connection with changes in policy measures. State-level RSV and alcohol use prevalence did not reveal a significant relationship. CONCLUSION: Google trend is a potential source of rapid feedback to policymakers about population responses to an abrupt change in alcohol policies.