scholarly journals The Pace and Pulse of the Fight against Coronavirus across the US, A Google Trends Approach (Preprint)

2020 ◽  
Author(s):  
Tichakunda Mangono ◽  
Peter Smittenaar ◽  
Yael Caplan ◽  
Vincent Huang ◽  
Staci Sutermaster ◽  
...  
Keyword(s):  
Health Behaviors ◽  
Real Time ◽  
Information Seeking ◽  
National Level ◽  
Google Trends ◽  
Urgent Care ◽  
Data Sets ◽  
Single Issue ◽  
The Us ◽  
Search Data

BACKGROUND The coronavirus pandemic is impacting our lives at unprecedented speed and scale - including how we eat and work, what we worry about, how much we move, and our ability to earn. Traditional surveys in the area of public health can be expensive, time-consuming, and rapidly go out of date. Analyzing big data sets (such as electronic patient records, surveillance systems) is very complex. However, Google Trends is an alternative approach which has been used before to analyze health behaviors, but most research on COVID-19 using this data, so far, looks at a single issue or a limited geographic area. This paper explores Google Trends as a proxy for what people are thinking, needing, and planning in real time across the US. OBJECTIVE We use Google Trends to provide both insights into, and potential indicators of, important changes in information-seeking patterns during pandemics like COVID-19. We asked four questions: (1) How has information seeking changed over time? (2) How does information seeking vary between regions and states? (3) Do states have particular and distinct patterns in information seeking? (4) Does search data correlate with – and even precede – real-life events? METHODS We analyzed searches on 39 terms related to COVID-19, falling into six themes: Social & Travel; Care Seeking; Government Programs; Health Programs; News & Influence; Outlook & Concerns. We generated data sets at the national level (covering Jan 1, 2016 – April 15, 2020) and state level (covering Jan 1, 2020 – April 15, 2020). Methods used include trend analysis of US search data; geographic analyses of the differences in search popularity across US states during March 1st to April 15th, 2020; and Principal Component Analyses (PCA) to extract search patterns across states. RESULTS Data showed high demand for information corresponded with increasing searches for “coronavirus” linked to news sources regardless of the ideological leaning of the news source. Changes in information seeking often happened well in advance of action by the federal government. The popularity of searches for unemployment claims predicted the actual spike in weekly claims. The increase in searches for information on coronavirus care was paralleled by a decrease in searches related to other health behaviors, such as urgent care, doctor’s appointment, health insurance/ Medicare/ Medicaid. Finally, concerns vary across the country - some search terms were more popular in some regions than in others. CONCLUSIONS COVID-19 is unlikely to be the last pandemic disease the US faces. Our research holds important lessons for both state and federal governments in a fast-evolving situation that requires a finger on the pulse of public sentiment. We suggest strategic shifts for policy makers to improve the precision and effectiveness of non-pharmaceutical interventions (NPIs) and recommend the development of a real-time dashboard as a decision-making tool. CLINICALTRIAL N/A

scholarly journals COVID-19 Symptom-Related Google Searches and Local COVID-19 Incidence in Spain: Correlational Study (Preprint)

2020 ◽  
Author(s):  
Alberto Jimenez Jimenez ◽  
Rosa M Estevez-Reboredo ◽  
Miguel A Santed ◽  
Victoria Ramos
Keyword(s):  
Real Time ◽  
Influenza A ◽  
Time Lag ◽  
Population Level ◽  
H1n1 Influenza ◽  
Google Trends ◽  
Small Contribution ◽  
Search Patterns ◽  
Search Data ◽  
Google Search

BACKGROUND COVID-19 is one of the biggest pandemics in human history, along with other disease pandemics, such as the H1N1 influenza A, bubonic plague, and smallpox pandemics. This study is a small contribution that tries to find contrasted formulas to alleviate global suffering and guarantee a more manageable future. OBJECTIVE In this study, a statistical approach was proposed to study the correlation between the incidence of COVID-19 in Spain and search data provided by Google Trends. METHODS We assessed the linear correlation between Google Trends search data and the data provided by the National Center of Epidemiology in Spain—which is dependent on the Instituto de Salud Carlos III—regarding the number of COVID-19 cases reported with a certain time lag. These data enabled the identification of anticipatory patterns. RESULTS In response to the ongoing outbreak, our results demonstrate that by using our correlation test, the evolution of the COVID-19 pandemic can be predicted in Spain up to 11 days in advance. CONCLUSIONS During the epidemic, Google Trends offers the possibility to preempt health care decisions in real time by tracking people's concerns through their search patterns. This can be of great help given the critical, if not dramatic need for complementary monitoring approaches that work on a population level and inform public health decisions in real time. This study of Google search patterns, which was motivated by the fears of individuals in the face of a pandemic, can be useful in anticipating the development of the pandemic.

scholarly journals Modeling Zika Virus Spread in Colombia Using Google Search Queries and Logistic Power Models

2018 ◽  
Author(s):  
Mekenna Brown ◽  
Christopher Cain ◽  
James Whitfield ◽  
Edwin Ding ◽  
Sara Y Del Valle ◽  
...  
Keyword(s):  
Disease Outbreaks ◽  
Google Trends ◽  
Logistic Growth ◽  
Data Sets ◽  
Surveillance Systems ◽  
Public Health Agencies ◽  
Power Models ◽  
Clinical Surveillance ◽  
Search Data ◽  
Google Search

AbstractPublic health agencies generally have a small window to respond to burgeoning disease outbreaks in order to mitigate the potential impact. There has been significant interest in developing forecasting models that can predict how and where a disease will spread. However, since clinical surveillance systems typically publish data with a lag of two or more weeks, there is a need for complimentary data streams that can close this gap. We examined the usefulness of Google Trends search data for analyzing the 2016 Zika epidemic in Colombia and evaluating their ability to predict its spread. We calculated the correlation and the time delay between the reported case data and the Google Trends data using variations of the logistic growth model, and showed that the data sets were systematically offset from each other, implying a lead time in the Google Trends data. Our study showed how Internet data can potentially complement clinical surveillance data and may be used as an effective early detection tool for disease outbreaks.

scholarly journals COVID-19 Symptom-Related Google Searches and Local COVID-19 Incidence in Spain: Correlational Study

10.2196/23518 ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. e23518 ◽  
Author(s):  
Alberto Jimenez Jimenez ◽  
Rosa M Estevez-Reboredo ◽  
Miguel A Santed ◽  
Victoria Ramos
Keyword(s):  
Real Time ◽  
Influenza A ◽  
Time Lag ◽  
Population Level ◽  
H1n1 Influenza ◽  
Google Trends ◽  
Small Contribution ◽  
Search Patterns ◽  
Search Data ◽  
Google Search

Background COVID-19 is one of the biggest pandemics in human history, along with other disease pandemics, such as the H1N1 influenza A, bubonic plague, and smallpox pandemics. This study is a small contribution that tries to find contrasted formulas to alleviate global suffering and guarantee a more manageable future. Objective In this study, a statistical approach was proposed to study the correlation between the incidence of COVID-19 in Spain and search data provided by Google Trends. Methods We assessed the linear correlation between Google Trends search data and the data provided by the National Center of Epidemiology in Spain—which is dependent on the Instituto de Salud Carlos III—regarding the number of COVID-19 cases reported with a certain time lag. These data enabled the identification of anticipatory patterns. Results In response to the ongoing outbreak, our results demonstrate that by using our correlation test, the evolution of the COVID-19 pandemic can be predicted in Spain up to 11 days in advance. Conclusions During the epidemic, Google Trends offers the possibility to preempt health care decisions in real time by tracking people's concerns through their search patterns. This can be of great help given the critical, if not dramatic need for complementary monitoring approaches that work on a population level and inform public health decisions in real time. This study of Google search patterns, which was motivated by the fears of individuals in the face of a pandemic, can be useful in anticipating the development of the pandemic.

scholarly journals Suitability of Google Trends™ for digital surveillance during ongoing COVID-19 epidemic: a case study from India

2021 ◽  
pp. 1-28
Author(s):  
Parmeshwar Satpathy ◽  
Sanjeev Kumar ◽  
Pankaj Prasad
Keyword(s):  
Information Seeking ◽  
Media Coverage ◽  
Time Lag ◽  
National Level ◽  
Google Trends ◽  
Health Seeking ◽  
High Burden ◽  
Search Volume ◽  
Digital Surveillance ◽  
Time Lag Correlation

Abstract Objective: Digital surveillance has shown mixed results as supplement to traditional surveillance. Google Trends™ (GT) has been used for digital surveillance of H1N1, Ebola and MERS. We used GT to correlate the information seeking on COVID-19 with number of tests and cases in India. Methods: We obtained data on daily tests and cases from WHO, ECDC and covid19india.org. We used a comprehensive search strategy to retrieve GT data on COVID-19 related information-seeking behaviour in India between 1st January and 31st May 2020 in the form of relative search volume (RSV). We used time-lag correlation analysis to assess the temporal relationships between RSV and daily new COVID-19 cases and tests. Results: GT RSV showed high time-lag correlation with both daily reported tests and cases for the terms “COVID 19”, “COVID”, “social distancing”, “soap” and “lockdown” at national level. In five high-burden states, high correlation was observed for these five terms along with “Corona”. Peaks in RSV both at national level and high-burden states corresponded with media coverage or government declarations on the ongoing pandemic. Conclusion: The correlation observed between GT data and COVID-19 tests/cases in India may be either due to media-coverage induced curiosity or health-seeking.

scholarly journals Use of daily Internet search query data improves real-time projections of influenza epidemics

2018 ◽  
Vol 15 (147) ◽  
pp. 20180220 ◽  
Author(s):  
Christoph Zimmer ◽  
Sequoia I. Leuba ◽  
Reza Yaesoubi ◽  
Ted Cohen
Keyword(s):  
Real Time ◽  
Temporal Resolution ◽  
Internet Search ◽  
Forecasting Performance ◽  
The Us ◽  
Control And Prevention ◽  
Influenza Activity ◽  
The Usa ◽  
Search Data ◽  
Substantial Interest

Seasonal influenza causes millions of illnesses and tens of thousands of deaths per year in the USA alone. While the morbidity and mortality associated with influenza is substantial each year, the timing and magnitude of epidemics are highly variable which complicates efforts to anticipate demands on the healthcare system. Better methods to forecast influenza activity would help policymakers anticipate such stressors. The US Centers for Disease Control and Prevention (CDC) has recognized the importance of improving influenza forecasting and hosts an annual challenge for predicting influenza-like illness (ILI) activity in the USA. The CDC data serve as the reference for ILI in the USA, but this information is aggregated by epidemiological week and reported after a one-week delay (and may be subject to correction even after this reporting lag). Therefore, there has been substantial interest in whether real-time Internet search data, such as Google, Twitter or Wikipedia could be used to improve influenza forecasting. In this study, we combine a previously developed calibration and prediction framework with an established humidity-based transmission dynamic model to forecast influenza. We then compare predictions based on only CDC ILI data with predictions that leverage the earlier availability and finer temporal resolution of Wikipedia search data. We find that both the earlier availability and the finer temporal resolution are important for increasing forecasting performance. Using daily Wikipedia search data leads to a marked improvement in prediction performance compared to weekly data especially for a three- to four-week forecasting horizon.

scholarly journals Predicting Dengue Incidence Leveraging Internet-Based Data Sources. A Case Study in 20 cities in Brazil

2020 ◽  
Author(s):  
Gal Koplewitz ◽  
Fred Lu ◽  
Leonardo Clemente ◽  
Caroline Buckee ◽  
Mauricio Santillana
Keyword(s):  
Random Forest ◽  
Real Time ◽  
Mosquito Control ◽  
National Level ◽  
Epidemiological Data ◽  
Data Sources ◽  
Surveillance Systems ◽  
Internet Search ◽  
Search Data ◽  
The City

AbstractThe dengue virus affects millions of people every year worldwide, causing large epidemic outbreaks that disrupt people’s lives and severely strain healthcare systems. In the absence of a reliable vaccine against it or an effective treatment to manage the illness in humans, most efforts to combat dengue infections have focused on preventing its vectors, mainly the Aedes aegypti mosquito, from flourishing across the world. These mosquito-control strategies need reliable disease activity surveillance systems to be deployed. Despite significant efforts to estimate dengue incidence using a variety of data sources and methods, little work has been done to understand the relative contribution of the different data sources to improved prediction. Additionally, most work has focused on prediction systems at the national level, rather than at finer spatial resolutions. We develop a methodological framework to assess and compare dengue incidence estimates at the city level and evaluate the performance of a collection of models on 20 different cities in Brazil. The data sources we use towards this end are weekly incidence counts from prior years (seasonal autoregressive terms), weekly-aggregated weather variables, and real-time internet search data. We find that a random forest-based model effectively leverages these multiple data sources and provides robust predictions, while retaining interpretability. For real-time predictions that assume long delays (6-8 weeks) in the availability of epidemiological data, we find that real-time internet search data are the strongest predictors of Dengue incidence, whereas for predictions that assume very short delays (1-2 weeks), short-term and seasonal autocorrelation are dominant as predictors. Despite the difficulties inherent to city-level prediction, our framework achieves meaningful and actionable estimates across cities with different characteristics.Author SummaryAs the incidence of infectious diseases like dengue continues to increase throughout world, tracking their spread in real time poses a significant challenge to local and national health authorities. Accurate incidence data are often impossible to obtain as outbreaks emerge and unfold, and a range of nowcasting tools have been developed to estimate disease trends using different mathematical methodologies to fill the temporal data gap. Over the past several years, researchers have investigated how to best incorporate internet search data into predictive models, since these can be obtained in real-time. Still, most such models have been regression-based, and have tended to underperform in cases when epidemiological data are only available after long reporting delays. Moreover, in tropical countries, these models have previously been tested and applied primarily at the national level. Here, we develop a machine learning model based on a random forest approach and apply it in 20 cities in Brazil. We find that our methodology produces meaningful and actionable disease estimates at the city level, and that it is more robust to delays in the availability of epidemiological data than regression-based models.

Using Google Trends to understand information-seeking behaviour about throat cancer

2019 ◽  
Vol 133 (7) ◽  
pp. 610-614 ◽  
Author(s):  
M Faoury ◽  
T Upile ◽  
N Patel
Keyword(s):  
Head And Neck Cancer ◽  
Thematic Analysis ◽  
Information Seeking ◽  
Healthcare Delivery ◽  
Google Trends ◽  
The Public ◽  
Search Terms ◽  
Information Seeking Behaviour ◽  
Search Data ◽  
Insight Into

AbstractObjectiveMany people seek health information from internet sources. Understanding this behaviour can help inform healthcare delivery. This study aimed to review Google Trends as a method for investigating internet-based information-seeking behaviour related to throat cancer in terms of quantity, content and thematic analysis.MethodData was collected using Google Trends. Normalised data was created using the search terms ‘throat cancer’, ‘cancer’, ‘HPV’, ‘laryngeal cancer’ and ‘head and neck cancer’. The search data was used to analyse the temporal and geographical interest pattern of these terms from 2004 to 2015.ResultsThree important peaks in searches for ‘throat cancer’ were identified. The first and greatest increase in interest was in September 2010, and there were also peaks in June 2013 and in October 2011.ConclusionInternet-search analysis can provide an insight into the information-seeking behaviour of the public. Mass media can hugely affect this information-seeking behaviour. Possessing tools to investigate and understand information-seeking behaviour may be used to improve healthcare delivery.

scholarly journals Searching for the Determinants of Climate Change Interest

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Patrick Cavanagh ◽  
Corey Lang ◽  
Xinran Li ◽  
Haoran Miao ◽  
John David Ryder
Keyword(s):  
Climate Change ◽  
Information Seeking ◽  
Political Ideology ◽  
Metropolitan Areas ◽  
Hot Spot ◽  
National Level ◽  
The United States ◽  
Search Activity ◽  
Mitigation And Adaptation ◽  
Search Data

A meaningful CO2 mitigation policy is unlikely at the national level in the United States. What is currently happening and what is much more likely to occur in the future are city and regional level efforts of mitigation and adaptation. This paper aims to understand the geographic and socioeconomic characteristics of metropolitan areas and regions that lead to engagement with the issue of climate change. We use geographically explicit, internet search data from Google to measure information seeking behavior, which we interpret as engagement, attention, and interest. Our spatial Hot Spot analysis creates a map that potentially could be harnessed by policymakers to gauge mitigation support or adaptation potential. The results of our multivariate analysis suggest that socioeconomic factors are the strongest determinants of search behavior and that climate and geography have little to no impact. With regard to political ideology, we find evidence of a nonlinear, inverse-U relationship with maximum search activity occurring in metropolitan areas with a near even political split, suggesting that parity may be good for engagement.

scholarly journals Analysis of 220 Years of Floodplain Population Dynamics in the US at Different Spatial Scales

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 141
Author(s):  
Firoza Akhter ◽  
Maurizio Mazzoleni ◽  
Luigia Brandimarte
Keyword(s):  
Population Dynamics ◽  
Large Scale ◽  
Spatial Scales ◽  
National Level ◽  
State Level ◽  
Economic Activities ◽  
Protection Measures ◽  
Management Planning ◽  
The Us ◽  
Over Time

In this study, we explore the long-term trends of floodplain population dynamics at different spatial scales in the contiguous United States (U.S.). We exploit different types of datasets from 1790–2010—i.e., decadal spatial distribution for the population density in the US, global floodplains dataset, large-scale data of flood occurrence and damage, and structural and nonstructural flood protection measures for the US. At the national level, we found that the population initially settled down within the floodplains and then spread across its territory over time. At the state level, we observed that flood damages and national protection measures might have contributed to a learning effect, which in turn, shaped the floodplain population dynamics over time. Finally, at the county level, other socio-economic factors such as local flood insurances, economic activities, and socio-political context may predominantly influence the dynamics. Our study shows that different influencing factors affect floodplain population dynamics at different spatial scales. These facts are crucial for a reliable development and implementation of flood risk management planning.

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