Curcumin vs Chloroquine in Coronavirus Global Pandemic: Trend analysis Study in Google (original) (raw)
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Inefficacious drugs against covid-19: analysis of sales, tweets, and search engines
Revista de saúde pública/Revista de Saúde Pública, 2024
OBJECTIVE: Assess the correlation between the sales of two drugs with no proven efficacy against covid-19, ivermectin and chloroquine, and other relevant variables, such as Google ® searches, number of tweets related to these drugs, number of cases and deaths resulting from covid-19. METHODS: The methodology adopted in this study has four stages: data collection, data processing, exploratory data analysis, and correlation analysis. Spearman's method was used to obtain cross-correlations between each pair of variables. RESULTS: The results show similar behaviors between variables. Peaks occurred in the same or near periods. The exploratory data analysis showed shortage of chloroquine in the period corresponding to the beginning of advertising for the application of these drugs against covid-19. Both drugs showed a high and statistically significant correlation with the other variables. Also, some of them showed a higher correlation with drug sales when we employed a one-month lag. In the case of chloroquine, this was observed for the number of deaths. In the case of ivermectin, this was observed for the number of tweets, cases, and deaths. CONCLUSIONS: The results contribute to decision making in crisis management by governments, industries, and stores. In times of crisis, as observed during the covid-19 pandemic, some variables can help sales forecasting, especially Google ® and tweets, which provide a realtime analysis of the situation. Monitoring social media platforms and search engines would allow the determination of drug use by the population and better prediction of potential peaks in the demand for these drugs.
Prediction of the Development of Covid-19 Case in Indonesia Based on Google Trend Analysis
Eduvest - Journal of Universal Studies
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.
International Journal of Public Health Science (IJPHS), 2020
In combating COVID-19, maintaining the immune system is important. Providing this information to the general population will increase public awareness towards improving their immune system. The use of Google Trends for exploring web behavior related to a topic or search term also considered as a tool for monitoring public awareness to help risk communication during the COVID-19 pandemic. Therefore, this study was conducted to assess the use of Google Trends to monitor public awareness to immune system improvement during the COVID-19 pandemic in Indonesia. This quantitative and qualitative research used time-series data from 31 December 2019 to 2 May 2020. The time-lag correlation analysis was performed to compare between relative search volume (RSV) of “Vitamin C”, “Vaksin” (Vaccine), “Berjemur” (Sunbathing) from Google Trends (GT), and the number of reported COVID-19 new cases. Validation using time-lag correlation shows the significant correlation between RSV keywords related to p...
International Journal of Public Health Science (IJPHS), 2020
COVID-19 Pandemic has become a major problem in various infected countries, including Indonesia. The proper risk communication strategy during this outbreak was important to reduce the impact. Therefore, this research was intended to assess the potential use of Google Trends as a tool to monitor risk communication during COVID-19 pandemic in Indonesia. Search patterns were analyzed using the terminology used to identify COVID-19 in Indonesia, followed by information-finding keywords 'gejala (symptoms)', 'mencegah (preventing)', and 'obat (drug)' keywords compared to the number of newly confirmed COVID-19 cases in Indonesia using time-lagged correlation analysis from December 31th, 2019 to April 20th, 2020. Peaks within respective timelines were qualitatively described according to current COVID-19 related events. ”Corona” was the terminology mostly used in Indonesia to identify COVID-19. There were five spikes observed from “corona” keyword timeline, which ea...
JMIR Public Health and Surveillance, 2020
Background The coronavirus disease (COVID-19) is the latest pandemic of the digital age. With the internet harvesting large amounts of data from the general population in real time, public databases such as Google Trends (GT) and the Baidu Index (BI) can be an expedient tool to assist public health efforts. Objective The aim of this study is to apply digital epidemiology to the current COVID-19 pandemic to determine the utility of providing adjunctive epidemiologic information on outbreaks of this disease and evaluate this methodology in the case of future pandemics. Methods An epidemiologic time series analysis of online search trends relating to the COVID-19 pandemic was performed from January 9, 2020, to April 6, 2020. BI was used to obtain online search data for China, while GT was used for worldwide data, the countries of Italy and Spain, and the US states of New York and Washington. These data were compared to real-world confirmed cases and deaths of COVID-19. Chronologic patt...
Using “Google trends” for dengue surveillance and epidemiological research
International Journal Of Community Medicine And Public Health
Millions of people worldwide search online for health-related information and search engines have become an increasingly popular resource for accessing health-related information and provides valuable source. Key words used as well as the number and geographic location of searches can provide trend data, available by Google trends. In this study exploring this resource using dengue disease as an example. Objectives were to use Google trends data for comparison across different locations in India for the past 5 years, and to assess the specific search terms used in Google trends data and to correlate the real time dengue outbreak of Tamil Nadu with Google trend search. It was a cross sectional study. Data collection was done via Google search queries and record was included. Weekly trends were accessed from Google Trends. Data is a randomly collected sample of real time and non-real time Google search queries. Search traffic for the string “dengue fever” reflected increased likelihoo...
Searching For Vitamin C, Vitamin D and COVID-19: a Google Trends Study
Journal of Clinical and Basic Research (JCBR), 2021
Background and objectives: COVID-19 outbreak is characterized as a pandemic. Owing to the effect of this disease on people's lives, news about the methods of preventing and treating this disease is released every day. There have been some clinical data suggesting that vitamins C and D can be useful in treating patients with COVID-19 disease. In this study, we aimed to examine vitamin C and D searching trends in 10 countries and worldwide about the COVID-19 news based on the data on Google Trends. Methods: We surveyed the searches about vitamins C and D using some keywords on Google Trends from December 15, 2019 to April 29, 2020. Results: The number of searches increased after the release of news about the effect of vitamins C and D on COVID-19. Conclusion: The results suggest that as the news about the role of vitamins on infection prevention and treatment spreads, people become more interested in expanding their nutritional knowledge.
Correlations between Web Searches and COVID-19 Epidemiological Indicators in Brazil
Brazilian Archives of Biology and Technology
COVID-19 rapidly spread across the world in an unprecedented outbreak with a massive number of infected and fatalities. The pandemic was heavily discussed and searched on the internet, which generated big amounts of data related to it. This led to the possibility of attempting to forecast coronavirus indicators using the internet data. For this study, Google Trends statistics for 124 selected search terms related to pandemic were used in an attempt to find which keywords had the best Spearman correlations with a lag, as well as a forecasting model. It was found that keywords related to coronavirus testing among some others, such as "I have contracted covid", had high correlations (≥0.7) with few weeks of lag (≤4 weeks). Besides that, the ARIMAX model using those keywords had promising results in predicting the increase or decrease of epidemiological indicators, although it was not able to predict their exact values. Thus, we found that Google Trends data may be useful for predicting outbreaks of coronavirus a few weeks before they happen, and may be used as an auxiliary tool in monitoring and forecasting the disease in Brazil.
Relative Search Interest and Twitter Altmetrics of Chloroquine, Hydroxychloroquine and Ivermectin in
Journal of Scientometric Research, 2022
This study aims to present exploratory and descriptive research about Latin America's online information search and Twitter activity and the repercussions of chloroquine, hydroxychloroquine, and ivermectin, which were at the time considered promising alternatives to prevent or treat COVID-19. From the perspective of Webmetrics and Altmetrics-data were collected and analysed from Twitter around indexed outputs by titles with one of these three drugs and from Google Trends (Relative Search Interest) in Latin American countries. The results demonstrated that there might be parallels between Google Search and Twitter activity. The results also showed that ivermectin was, among the three selected drugs, the most searched in Google Search and higher activity on Latin America's Twitter accounts.
International Journal of Public Health Science (IJPHS), 2023
Indonesia has distributed the COVID-19 vaccinations to its people starting from January 2021 based on certain priorities to deal with COVID-19 pandemic. News of deaths after the COVID-19 vaccination has made some people hesitate to get vaccinated. This study aims to depict the pattern and determinant of public interest in COVID-19 vaccine information using Google Trends data. The pattern can be used as a suggestion to the government to conduct a campaign on the COVID-19 vaccine. Several topics related to the COVID-19 vaccine were collected from Google Trends and then clustered by the province using K-Means. By total within sum of square, best number of clusters is two. Then, a logistic regression analysis was done with cluster as response variable to find out what factors made people interested in the COVID-19 vaccine topic. As a result, percentage of people who received the first dose of the COVID-19 vaccine and the rate of COVID-19 patients who were treated had influenced public interest in the COVID-19 vaccine. Hence, the campaign must be transparent so that the public can see both the good and bad effects of vaccination. It will help to reduce the number of people dying after receiving vaccinations.