Increased Internet Search Interest for GI Symptoms May Predict COVID-19 Cases in US Hotspots.
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ABSTRACT:
SUBMITTER: Ahmad I
PROVIDER: S-EPMC7834024 | biostudies-literature | 2020 Nov
REPOSITORIES: biostudies-literature
Publications
Increased Internet Search Interest for GI Symptoms May Predict COVID-19 Cases in US Hotspots.
Ahmad Imama I Flanagan Ryan R Staller Kyle K
Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association 20200703 12
Google Trends is an online tool that allows measurement of search term popularity on Google, spatially and temporally. While not an epidemiological tool for determining incidence, it can estimate the popularity of a certain disease by search volume over time.<sup>1</sup><sup>,</sup><sup>2</sup> It has previously correlated well with infectious disease incidence and has demonstrated utility in disease forecasting, especially with influenza data.<sup>3</sup> We utilized Google Trends to investigat ...[more]
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Project description:BackgroundReal-time surveillance of search behavior on the internet has achieved accessibility in measuring disease activity. In this study, we systematically assessed the associations between internet search trends of gastrointestinal (GI) symptom terms and daily newly confirmed COVID-19 cases at both global and regional levels.MethodsRelative search volumes (RSVs) of GI symptom terms were derived from internet search engines. Time-series analyses with autoregressive integrated moving average models were conducted to fit and forecast the RSV trends of each GI symptom term before and after the COVID-19 outbreak. Generalized additive models were used to quantify the effects of RSVs of GI symptom terms on the incidence of COVID-19. In addition, dose-response analyses were applied to estimate the shape of the associations.ResultsThe RSVs of GI symptom terms could be characterized by seasonal variation and a high correlation with symptoms of "fever" and "cough" at both global and regional levels; in particular, "diarrhea" and "loss of taste" were abnormally increased during the outbreak period of COVID-19, with elevated point changes of 1.31 and 8 times, respectively. In addition, these symptom terms could effectively predict a COVID-19 outbreak in advance, underlying the lag correlation at 12 and 5 days, respectively, and with mutual independence. The dose-response curves showed a consistent increase in daily COVID-19 risk with increasing search volumes of "diarrhea" and "loss of taste".ConclusionThis is the first and largest epidemiologic study that comprehensively revealed the advanced prediction of COVID-19 outbreaks at both global and regional levels via GI symptom indicators.
| S-EPMC8625285 | biostudies-literature
Project description:Background and objectives:The daily incidence and deaths of coronavirus disease 2019 (COVID-19) in the USA are poorly understood. Internet search interest was found to be correlated with COVID-19 daily incidence in China, but has not yet been applied to the USA. Therefore, we examined the association of internet search-interest with COVID-19 daily incidence and deaths in the USA. Methods:We extracted COVID-19 daily new cases and deaths in the USA from two population-based datasets, namely 1-point-3-acres.com and the Johns Hopkins COVID-19 data repository. The internet search-interest of COVID-19-related terms was obtained using Google Trends. The Pearson correlation test and general linear model were used to examine correlations and predict trends, respectively. Results:There were 636,282 new cases and,325 deaths of COVID-19 in the USA from March 1 to April 15, 2020, with a crude mortality of 4.45%. The daily new cases peaked at 35,098 cases on April 10, 2020 and the daily deaths peaked at 2,494 on April 15, 2020. The search interest of COVID, "COVID pneumonia" and "COVID heart" were correlated with COVID-19 daily incidence, with 12 or 14 days of delay (Pearson's r = 0.978, 0.978 and 0.979, respectively) and deaths with 19 days of delay (Pearson's r = 0.963, 0.958 and 0.970, respectively). The 7-day follow-up with prospectively collected data showed no significant correlations of the observed data with the predicted daily new cases or daily deaths, using search interest of COVID, COVID heart, and COVID pneumonia. Conclusions:Search terms related to COVID-19 are highly correlated with the COVID-19 daily new cases and deaths in the USA.
| S-EPMC7176069 | biostudies-literature
Project description:The COVID-19 outbreak is a global pandemic with community circulation in many countries, including the United States, with confirmed cases in all states. The course of this pandemic will be shaped by how governments enact timely policies and disseminate information and by how the public reacts to policies and information. Here, we examine information-seeking responses to the first COVID-19 case public announcement in a state. Using an event study framework for all US states, we show that such news increases collective attention to the crisis right away. However, the elevated level of attention is short-lived, even though the initial announcements are followed by increasingly strong policy measures. Specifically, searches for "coronavirus" increased by about 36% (95% CI: 27 to 44%) on the day immediately after the first case announcement but decreased back to the baseline level in less than a week or two. We find that people respond to the first report of COVID-19 in their state by immediately seeking information about COVID-19, as measured by searches for coronavirus, coronavirus symptoms, and hand sanitizer. On the other hand, searches for information regarding community-level policies (e.g., quarantine, school closures, testing) or personal health strategies (e.g., masks, grocery delivery, over-the-counter medications) do not appear to be immediately triggered by first reports. These results are representative of the study period being relatively early in the epidemic, and more-elaborate policy responses were not yet part of the public discourse. Further analysis should track evolving patterns of responses to subsequent flows of public information.
| S-EPMC7260988 | biostudies-literature
Project description:BackgroundCoronavirus disease (COVID-19) is a novel viral illness that has rapidly spread worldwide. While the disease primarily presents as a respiratory illness, gastrointestinal symptoms such as diarrhea have been reported in up to one-third of confirmed cases, and patients may have mild symptoms that do not prompt them to seek medical attention. Internet-based infodemiology offers an approach to studying symptoms at a population level, even in individuals who do not seek medical care.ObjectiveThis study aimed to determine if a correlation exists between internet searches for gastrointestinal symptoms and the confirmed case count of COVID-19 in the United States.MethodsThe search terms chosen for analysis in this study included common gastrointestinal symptoms such as diarrhea, nausea, vomiting, and abdominal pain. Furthermore, the search terms fever and cough were used as positive controls, and constipation was used as a negative control. Daily query shares for the selected symptoms were obtained from Google Trends between October 1, 2019 and June 15, 2020 for all US states. These shares were divided into two time periods: pre-COVID-19 (prior to March 1) and post-COVID-19 (March 1-June 15). Confirmed COVID-19 case numbers were obtained from the Johns Hopkins University Center for Systems Science and Engineering data repository. Moving averages of the daily query shares (normalized to baseline pre-COVID-19) were then analyzed against the confirmed disease case count and daily new cases to establish a temporal relationship.ResultsThe relative search query shares of many symptoms, including nausea, vomiting, abdominal pain, and constipation, remained near or below baseline throughout the time period studied; however, there were notable increases in searches for the positive control symptoms of fever and cough as well as for diarrhea. These increases in daily search queries for fever, cough, and diarrhea preceded the rapid rise in number of cases by approximately 10 to 14 days. The search volumes for these terms began declining after mid-March despite the continued rises in cumulative cases and daily new case counts.ConclusionsGoogle searches for symptoms may precede the actual rises in cases and hospitalizations during pandemics. During the current COVID-19 pandemic, this study demonstrates that internet search queries for fever, cough, and diarrhea increased prior to the increased confirmed case count by available testing during the early weeks of the pandemic in the United States. While the search volumes eventually decreased significantly as the number of cases continued to rise, internet query search data may still be a useful tool at a population level to identify areas of active disease transmission at the cusp of new outbreaks.
| S-EPMC7371406 | biostudies-literature
Project description:As the COVID-19 spread over the globe and new variants of COVID-19 keep occurring, reliable real-time forecasts of COVID-19 hospitalizations are critical for public health decisions on medical resources allocations. This paper aims to forecast future 2 weeks national and state-level COVID-19 new hospital admissions in the United States. Our method is inspired by the strong association between public search behavior and hospitalization admissions and is extended from a previously-proposed influenza tracking model, AutoRegression with GOogle search data (ARGO). Our LASSO-penalized linear regression method efficiently combines Google search information and COVID-19 related time series information with dynamic training and rolling window prediction. Compared to other publicly available models collected from COVID-19 forecast hub, our method achieves substantial error reduction in a retrospective out-of-sample evaluation from Jan 4, 2021, to Dec 27, 2021. Overall, we showed that our method is flexible, self-correcting, robust, accurate, and interpretable, making it a potentially powerful tool to assist healthcare officials and decision making for the current and future infectious disease outbreaks.
| S-EPMC9188562 | biostudies-literature
Project description:BackgroundLimited timely, cost-effective, and regional availability of healthcare data is considered one of the limiting factors for timely analyses in the field of health services research and thus for healthcare delivery to the general population. Thus, Internet search engine analyses are coming into focus in this context.ObjectivesWhat contribution can data on the volume of Internet searches for various diseases make to the health care of the German population? Where is the potential and where are the limitations?MethodsA review of the current literature and a selective overview of Internet search engine analyses of dermatological, allergological, and infectious diseases published in the last 4 years in Germany were performed.ResultsBy analyzing web search data, temporal developments such as seasonality, months with the highest interest, and geographical differences can be mapped at the national, state (Bundesländer), and city level and major points of interest (e.g., skin cancer of the eye or anal itching) regarding a certain health topic can be identified. In addition, some studies were able to show correlations between search volume and external factors (e.g., temperature, care structure) and to registered cases (e.g., skin cancer, Lyme disease).ConclusionWeb search data, given the limitations noted, are able to directly provide spatiotemporal mapping regarding the needs of the Internet-using population. They are particularly useful in situations where traditional health data are limited or unavailable.
| S-EPMC8609262 | biostudies-literature
Project description:BackgroundHerpes zoster (HZ) and its complication postherpetic neuralgia (PHN), whose incidence are both expected to increase with an ageing population, have demonstrated high costs on healthcare systems and burden on individual quality of life. Previous studies have shown the possibility of assessing public interest in a disease and factors that influence search behaviour using internet search data. The aim of this study was to analyze internet search data for HZ in Germany to evaluate public interest in the disease and relevant influential temporal and geographic factors that modify search behavior.MethodsGoogle Ads Keyword Planner was used to generate a list of HZ-related keywords including their search volume for Germany as a whole and its sixteen federal states from October 2016 to September 2020. All keywords were qualitatively categorized, and changes over time and correlations with population density, physician density, and vaccination rates were assessed using Welch's ANOVA, Bonferroni correction for post-hoc analyses, and Pearson's correlation.ResultsA total of 1,651 relevant keywords with a search volume of 20,816,210 searches were identified. Overall, national search volume increased each year of the study period with a peak in August 2020. More than half of the total search volume related to general queries (55.1%). The highest average monthly search volumes were observed in the states of Hamburg, Saarland, and Bremen. Average monthly search volume showed strong positive correlations with population density (r = .512, p = .043) and a strong negative correlation with the number of inhabitants per working physician (r = -.689, p = .003).ConclusionsThe study demonstrated that evaluating internet search data is a viable method for assessing public interest in HZ, thereby identifying areas of unmet need to support targeted public health campaigns.
| S-EPMC10426197 | biostudies-literature
Project description:We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19-related symptoms. Specifically, we represent state-level time series data for COVID-19 and Google search trends for symptoms as smoothed functional curves. Given these functional data, we explore the modes of variation in the data using functional principal component analysis (FPCA). We also apply functional clustering analysis to identify patterns of COVID-19 confirmed case and death trajectories across the US. Moreover, we quantify the associations between Google COVID-19 search trends for symptoms and COVID-19 confirmed case and death trajectories using dynamic correlation. Finally, we examine the dynamics of correlations for the top nine Google search trends of symptoms commonly associated with COVID-19 confirmed case and death trajectories. Our results reveal and characterize distinct patterns for COVID-19 spread and mortality across the US. The dynamics of these correlations suggest the feasibility of using Google queries to forecast COVID-19 cases and mortality for up to three weeks in advance. Our results and analysis framework set the stage for the development of predictive models for forecasting COVID-19 confirmed cases and deaths using historical data and Google search trends for nine symptoms associated with both outcomes.
| S-EPMC8123439 | biostudies-literature
Project description:The coronavirus (COVID-19) pandemic has impacted young adults across a number of different domains. It is critical to establish the degree to which the COVID-19 pandemic has affected mental health and identify predictors of poor outcomes. Neuroticism and (low) respiratory sinus arrhythmia (RSA) are risk factors of internalizing disorders that might predict increased psychopathology symptoms. The present study included 222 undergraduate students from [name removed] in Long Island, NY. Before the COVID-19 pandemic, participants completed self-report measures of neuroticism and internalizing symptoms and an electrocardiogram. Between April 15th to May 30th, 2020, participants again completed the measure of internalizing symptoms and a questionnaire about COVID-19 experiences. The COVID-19 pandemic was associated with increased distress, fear/obsessions, and (low) positive mood symptoms. There was a Neuroticism x RSA interaction in relation to distress symptoms, such that greater pre-COVID-19 neuroticism was associated with increased distress symptoms, but only in the context of low RSA. These findings suggest the COVID-19 pandemic has contributed to increased internalizing symptoms in young adults, and individuals with specific personality and autonomic risk factors may be at heightened risk for developing psychopathology.
| S-EPMC8213985 | biostudies-literature
Project description:Prolonged symptoms that occur after COVID-19 (long-COVID) vary from mild, which do not interfere with daily life, to severe, which require long-term social support. This study assessed the secular trend in online searches on long-COVID in Japan. We conducted an observational study using data provided by Yahoo! JAPAN on the monthly search volume of query terms related to long-COVID from January 2020 to December 2022, including the search volume of the query "コロナ" (long-COVID in Japanese). The number of new cases of COVID-19 by month was used as a control for search trends, and the symptoms retrieved in conjunction with long-COVID were compared. Trends in online searches for each symptom of long-COVID were analyzed. The symptoms of long-COVID were classified according to "Component 1-Symptoms and Complaints" of the International Classification of Primary Care, 2nd edition (ICPC-2). Interest in long-COVID increased in response to peaks in the number of new cases of COVID-19 in Japan. The most frequent symptom searches with long-COVID were hair loss/baldness (3,530, 21,400, and 33,600 searches in 2020, 2021, and 2022, respectively), cough (340, 7,900 and 138,910 searches in 2020, 2021, and 2022, respectively), disturbance of smell/taste (230, 13,340, and 44,160 searches in 2020, 2021, and 2022, respectively), and headache (580, 6,180, and 42,870 searches in 2020, 2021, and 2022, respectively). In addition, the ranking of interest in "weakness/tiredness, general" in long-COVID increased each year (not in the top 10 in 2020, seventh in 2021, and second in 2022), and the absolute number of searches also increased. To our knowledge, this is the first study to investigate secular trends in online interest in long-COVID in the world. Continued monitoring of online interest in long-COVID is necessary to prepare for a possible increase in the number of patients with long-COVID.
| S-EPMC10650984 | biostudies-literature