Predicting Norovirus in the United States Using Google Trends: Infodemiology Study.
Ontology highlight
ABSTRACT: Background
Norovirus is a contagious disease. The transmission of norovirus spreads quickly and easily in various ways. Because effective methods to prevent or treat norovirus have not been discovered, it is important to rapidly recognize and report norovirus outbreaks in the early phase. Internet search has been a useful method for people to access information immediately. With the precise record of internet search trends, internet search has been a useful tool to manifest infectious disease outbreaks.Objective
In this study, we tried to discover the correlation between internet search terms and norovirus infection.Methods
The internet search trend data of norovirus were obtained from Google Trends. We used cross-correlation analysis to discover the temporal correlation between norovirus and other terms. We also used multiple linear regression with the stepwise method to recognize the most important predictors of internet search trends and norovirus. In addition, we evaluated the temporal correlation between actual norovirus cases and internet search terms in New York, California, and the United States as a whole.Results
Some Google search terms such as gastroenteritis, watery diarrhea, and stomach bug coincided with norovirus Google Trends. Some Google search terms such as contagious, travel, and party presented earlier than norovirus Google Trends. Some Google search terms such as dehydration, bar, and coronavirus presented several months later than norovirus Google Trends. We found that fever, gastroenteritis, poison, cruise, wedding, and watery diarrhea were important factors correlated with norovirus Google Trends. In actual norovirus cases from New York, California, and the United States as a whole, some Google search terms presented with, earlier, or later than actual norovirus cases.Conclusions
Our study provides novel strategy-based internet search evidence regarding the epidemiology of norovirus.
SUBMITTER: Yuan K
PROVIDER: S-EPMC8515228 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:During the unprecedented situation that all countries around the globe are facing due to the Coronavirus disease 2019 (COVID-19) pandemic, which has also had severe socioeconomic consequences, it is imperative to explore novel approaches to monitoring and forecasting regional outbreaks as they happen or even before they do so. To that end, in this paper, the role of Google query data in the predictability of COVID-19 in the United States at both national and state level is presented. As a preliminary investigation, Pearson and Kendall rank correlations are examined to explore the relationship between Google Trends data and COVID-19 data on cases and deaths. Next, a COVID-19 predictability analysis is performed, with the employed model being a quantile regression that is bias corrected via bootstrap simulation, i.e., a robust regression analysis that is the appropriate statistical approach to taking against the presence of outliers in the sample while also mitigating small sample estimation bias. The results indicate that there are statistically significant correlations between Google Trends and COVID-19 data, while the estimated models exhibit strong COVID-19 predictability. In line with previous work that has suggested that online real-time data are valuable in the monitoring and forecasting of epidemics and outbreaks, it is evident that such infodemiology approaches can assist public health policy makers in addressing the most crucial issues: flattening the curve, allocating health resources, and increasing the effectiveness and preparedness of their respective health care systems.
| S-EPMC7692493 | biostudies-literature
Project description:Internet data are being increasingly integrated into health informatics research and are becoming a useful tool for exploring human behavior. The most popular tool for examining online behavior is Google Trends, an open tool that provides information on trends and the variations of online interest in selected keywords and topics over time. Online search traffic data from Google have been shown to be useful in analyzing human behavior toward health topics and in predicting disease occurrence and outbreaks. Despite the large number of Google Trends studies during the last decade, the literature on the subject lacks a specific methodology framework. This article aims at providing an overview of the tool and data and at presenting the first methodology framework in using Google Trends in infodemiology and infoveillance, including the main factors that need to be taken into account for a strong methodology base. We provide a step-by-step guide for the methodology that needs to be followed when using Google Trends and the essential aspects required for valid results in this line of research. At first, an overview of the tool and the data are presented, followed by an analysis of the key methodological points for ensuring the validity of the results, which include selecting the appropriate keyword(s), region(s), period, and category. Overall, this article presents and analyzes the key points that need to be considered to achieve a strong methodological basis for using Google Trends data, which is crucial for ensuring the value and validity of the results, as the analysis of online queries is extensively integrated in health research in the big data era.
| S-EPMC6660120 | biostudies-literature
Project description:BackgroundLiver transplantation (LT) is the only curative treatment for end-stage liver disease. Less than 10% of global transplantation needs are met worldwide, and the need for LT is still increasing. The death rates on the waiting list remain too high.ObjectiveIt is, therefore, critical to raise awareness among the public and health care providers and in turn increasingly acquire donors.MethodsWe performed a Google Trends search using the search terms liver transplantation and liver transplant on October 15, 2020. On the basis of the resulting monthly data, the annual average Google Trends indices were calculated for the years 2004 to 2018. We not only investigated the trend worldwide but also used data from the United Network for Organ Sharing (UNOS), Spain, and Eurotransplant. Using pairwise Spearman correlations, Google Trends indices were examined over time and compared with the total number of liver transplants retrieved from the respective official websites of UNOS, the Organización Nacional de Trasplantes, and Eurotransplant.ResultsFrom 2004 to 2018, there was a significant decrease in the worldwide Google Trends index from 78.2 in 2004 to 20.5 in 2018 (-71.2%). This trend was more evident in UNOS than in the Eurotransplant group. In the same period, the number of transplanted livers increased worldwide. The waiting list mortality rate was 31% for Eurotransplant and 29% for UNOS. However, in Spain, where there are excellent awareness programs, the Google Trends index remained stable over the years with comparable, increasing LT numbers but a significantly lower waiting list mortality (15%).ConclusionsPublic awareness in LT has decreased significantly over the past two decades. Therefore, novel awareness programs should be initialized.
| S-EPMC8408753 | biostudies-literature
Project description:Background As access barriers to in-person abortion care increase due to legal restrictions and COVID-19–related disruptions, individuals may be turning to the internet for information and services on out-of-clinic medication abortions. Google searches allow us to explore timely population-level interest in this topic and assess its implications. Objective We examined the extent to which people searched for out-of-clinic medication abortions in the United States in 2020 through 3 initial search terms: home abortion, self abortion, and buy abortion pill online. Methods Using the Google Trends website, we estimated the relative search index (RSI)—a comparative measure of search popularity—for each initial search term and determined trends and its peak value between January 1, 2020, and January 1, 2021. RSI scores also helped to identify the 10 states where these searches were most popular. We developed a master list of top search queries for each of the initial search terms using the Google Trends application programming interface (API). We estimated the relative search volume (RSV)—the search volume of each query relative to other associated terms—for each of the top queries using the Google Health Trends API. We calculated average RSIs and RSVs from multiple samples to account for low-frequency data. Using the Custom Search API, we determined the top webpages presented to people searching for each of the initial search terms, contextualizing the information found when searching them on Google. Results Searches for home abortion had average RSIs that were 3 times higher than self abortion and almost 4 times higher than buy abortion pill online. Interest in home abortion peaked in November 2020, during the third pandemic wave, at a time when providers could dispense medication abortion using telemedicine and by mail. Home abortion was most frequently queried by searching for Planned Parenthood, abortion pill, and abortion clinic, presumably denoting varying degrees of clinical support. Consistently lower search popularity for self abortion and buy abortion pill online reflect less population interest in mostly or completely self-managed out-of-clinic abortions. We observed the highest interest for home abortion and self abortion in states hostile to abortion, suggesting that state restrictions encourage these online searches. Top webpages provided limited evidence-based clinical content on self-management of abortions, and several antiabortion sites presented health-related disinformation. Conclusions During the pandemic in the United States, there has been considerably more interest in home abortions than in minimally or nonclinically supported self-abortions. While our study was mainly descriptive, showing how infrequent abortion-related search data can be analyzed through multiple resampling, future studies should explore correlations between the keywords denoting interest in out-of-clinic abortion and abortion care measures and test models that allow for improved monitoring and surveillance of abortion concerns in our rapidly evolving policy context.
| S-EPMC10014087 | biostudies-literature
Project description:Google Flu Trends (GFT) uses Internet search queries in an effort to provide early warning of increases in influenza-like illness (ILI). In the United States, GFT estimates the percentage of physician visits related to ILI (%ILINet) reported by the Centers for Disease Control and Prevention (CDC). However, during the 2012-13 influenza season, GFT overestimated %ILINet by an appreciable amount and estimated the peak in incidence three weeks late. Using data from 2010-14, we investigated the relationship between GFT estimates (%GFT) and %ILINet. Based on the relationship between the relative change in %GFT and the relative change in %ILINet, we transformed %GFT estimates to better correspond with %ILINet values. In 2010-13, our transformed %GFT estimates were within ± 10% of %ILINet values for 17 of the 29 weeks that %ILINet was above the seasonal baseline value determined by the CDC; in contrast, the original %GFT estimates were within ± 10% of %ILINet values for only two of these 29 weeks. Relative to the %ILINet peak in 2012-13, the peak in our transformed %GFT estimates was 2% lower and one week later, whereas the peak in the original %GFT estimates was 74% higher and three weeks later. The same transformation improved %GFT estimates using the recalibrated 2013 GFT model in early 2013-14. Our transformed %GFT estimates can be calculated approximately one week before %ILINet values are reported by the CDC and the transformation equation was stable over the time period investigated (2010-13). We anticipate our results will facilitate future use of GFT.
| S-EPMC4281210 | biostudies-literature
Project description:BackgroundCoronavirus Disease 2019 (COVID-19) pandemic affects common diseases, but its impact on hand, foot, and mouth disease (HFMD) is unclear. Google Trends data is beneficial for approximate real-time statistics and because of ease in access, is expected to be used for infection explanation from an information-seeking behavior perspective. We aimed to explain HFMD cases before and during COVID-19 using Google Trends.MethodsHFMD cases were obtained from the National Institute of Infectious Diseases, and Google search data from 2009 to 2021 in Japan were downloaded from Google Trends. Pearson correlation coefficients were calculated between HFMD cases and the search topic "HFMD" from 2009 to 2021. Japanese tweets containing "HFMD" were retrieved to select search terms for further analysis. Search terms with counts larger than 1000 and belonging to ranges of infection sources, susceptible sites, susceptible populations, symptoms, treatment, preventive measures, and identified diseases were retained. Cross-correlation analyses were conducted to detect lag changes between HFMD cases and search terms before and during the COVID-19 pandemic. Multiple linear regressions with backward elimination processing were used to identify the most significant terms for HFMD explanation.ResultsHFMD cases and Google search volume peaked around July in most years, excluding 2020 and 2021. The search topic "HFMD" presented strong correlations with HFMD cases, except in 2020 when the COVID-19 outbreak occurred. In addition, the differences in lags for 73 (72.3%) search terms were negative, which might indicate increasing public awareness of HFMD infections during the COVID-19 pandemic. The results of multiple linear regression demonstrated that significant search terms contained the same meanings but expanded informative search content during the COVID-19 pandemic.ConclusionsThe significant terms for the explanation of HFMD cases before and during COVID-19 were different. Awareness of HFMD infections in Japan may have improved during the COVID-19 pandemic. Continuous monitoring is important to promote public health and prevent resurgence. The public interest reflected in information-seeking behavior can be helpful for public health surveillance.
| S-EPMC9617033 | biostudies-literature
Project description:BackgroundFactors such as anxiety, worry, and perceptions of insufficient knowledge about a topic motivate individuals to seek web-based health information to guide their health-related decision-making. These factors converged during the COVID-19 pandemic and were linked to COVID-19 vaccination decision-making. While research shows that web-based search relevant to COVID-19 was associated with subsequent vaccine uptake, less is known about COVID-19 vaccine intent search (which assesses vaccine availability, accessibility, and eligibility) as a signal of vaccine readiness.ObjectiveTo increase knowledge about vaccine intent search as a signal of vaccine readiness, we investigated the relationship between COVID-19 vaccine readiness and COVID-19 vaccine intent relative search volume on Google.MethodsWe compiled panel data from several data sources in all US counties between January 2021 and April 2023, a time during which those with primary COVID-19 vaccinations increased from <57,000 to >230 million adults. We estimated a random effects generalized least squares regression model with time-fixed effects to assess the relationship between county-level COVID-19 vaccine readiness and COVID-19 vaccine intent relative search volume. We controlled for health care capacity, per capita COVID-19 cases and vaccination doses administered, and sociodemographic indicators.ResultsThe county-level proportions of unvaccinated adults who reported that they would wait and see before getting a COVID-19 vaccine were positively associated with COVID-19 vaccine intent relative search volume (β=9.123; Z=3.59; P<.001). The county-level proportions of vaccine-enthusiast adults, adults who indicated they were either already vaccinated with a primary COVID-19 vaccine series or planned to complete the vaccine series soon, were negatively associated with COVID-19 vaccine intent relative search volume (β=-10.232; Z=-7.94; P<.001). However, vaccine intent search was higher in counties with high proportions of people who decided to wait and see and lower in counties with high proportions of vaccine enthusiasts.ConclusionsDuring this period of steep increase in COVID-19 vaccination, web-based search may have signaled differences in county-level COVID-19 vaccine readiness. More vaccine intent searches occurred in high wait-and-see counties, whereas fewer vaccine intent searches occurred in high vaccine-enthusiast counties. Considering previous research that identified a relationship between vaccine intent search and subsequent vaccine uptake, these findings suggest that vaccine intent search aligned with people's transition from the wait-and-see stage to the vaccine-enthusiast stage. The findings also suggest that web-based search trends may signal localized changes in information seeking and decision-making antecedent to vaccine uptake. Changes in web-based search trends illuminate opportunities for governments and other organizations to strategically allocate resources to increase vaccine uptake. Resource use is part of the larger public policy decisions that influence vaccine uptake, such as efforts to educate the public during evolving public health crises, including future pandemics.
| S-EPMC11319879 | biostudies-literature
Project description:Noroviruses are the leading cause of epidemic acute gastroenteritis in the United States. From September 2009 through August 2013, 3,960 norovirus outbreaks were reported to CaliciNet. Of the 2,895 outbreaks with a known transmission route, person-to-person and food-borne transmissions were reported for 2,425 (83.7%) and 465 (16.1%) of the outbreaks, respectively. A total of 2,475 outbreaks (62.5%) occurred in long-term care facilities (LTCF), 389 (9.8%) in restaurants, and 227 (5.7%) in schools. A total of 435 outbreaks (11%) were typed as genogroup I (GI) and 3,525 (89%) as GII noroviruses. GII.4 viruses caused 2,853 (72%) of all outbreaks, of which 94% typed as either GII.4 New Orleans or GII.4 Sydney. In addition, three non-GII.4 viruses, i.e., GII.12, GII.1, and GI.6, caused 528 (13%) of all outbreaks. Several non-GII.4 genotypes (GI.3, GI.6, GI.7, GII.3, GII.6, and GII.12) were significantly more associated with food-borne transmission (odds ratio, 1.9 to 7.1; P < 0.05). Patients in LTCF and people ?65 years of age were at higher risk for GII.4 infections than those in other settings and with other genotypes (P < 0.05). Phylogeographic analysis identified three major dispersions from two geographic locations that were responsible for the GI.6 outbreaks from 2011 to 2013. In conclusion, our data demonstrate the cyclic emergence of new (non-GII.4) norovirus strains, and several genotypes are more often associated with food-borne outbreaks. These surveillance data can be used to improve viral food-borne surveillance and to help guide studies to develop and evaluate targeted prevention methods such as norovirus vaccines, antivirals, and environmental decontamination methods.
| S-EPMC3911414 | biostudies-literature
Project description:BackgroundTo reduce the burden from the COVID-19 pandemic in the United States, federal and state local governments implemented restrictions such as limitations on gatherings, restaurant dining, and travel, and recommended non-pharmaceutical interventions including physical distancing, mask-wearing, surface disinfection, and increased hand hygiene. Resulting behavioral changes impacted other infectious diseases including enteropathogens such as norovirus and rotavirus, which had fairly regular seasonal patterns prior to the COVID-19 pandemic. The study objective was to project future incidence of norovirus and rotavirus gastroenteritis as contacts resumed and other NPIs are relaxed.MethodsWe fitted compartmental mathematical models to pre-pandemic U.S. surveillance data (2012-2019) for norovirus and rotavirus using maximum likelihood estimation. Then, we projected incidence for 2022-2030 under scenarios where the number of contacts a person has per day varies from70%, 80%, 90%, and full resumption (100%) of pre-pandemic levels.ResultsWe found that the population susceptibility to both viruses increased between March 2020 and November 2021. The 70-90% contact resumption scenarios led to lower incidence than observed pre-pandemic for both viruses. However, we found a greater than two-fold increase in community incidence relative to the pre-pandemic period under the 100% contact scenarios for both viruses. With rotavirus, for which population immunity is driven partially by vaccination, patterns settled into a new steady state quickly in 2022 under the 70-90% scenarios. For norovirus, for which immunity is relatively short-lasting and only acquired through infection, surged under the 100% contact scenario projection.ConclusionsThese results, which quantify the consequences of population susceptibility build-up, can help public health agencies prepare for potential resurgence of enteric viruses.
| S-EPMC10117239 | biostudies-literature
Project description:BACKGROUND:Adolescents and young adults in the age range of 13-24 years are at the highest risk of developing HIV infections. As social media platforms are extremely popular among youths, researchers can utilize these platforms to curb the HIV epidemic by investigating the associations between the discourses on HIV infections and the epidemiological data of HIV infections. OBJECTIVE:The goal of this study was to examine how Twitter activity among young men is related to the incidence of HIV infection in the population. METHODS:We used integrated human-computer techniques to characterize the HIV-related tweets by male adolescents and young male adults (age range: 13-24 years). We identified tweets related to HIV risk and prevention by using natural language processing (NLP). Our NLP algorithm identified 89.1% (2243/2517) relevant tweets, which were manually coded by expert coders. We coded 1577 HIV-prevention tweets and 17.5% (940/5372) of general sex-related tweets (including emojis, gifs, and images), and we achieved reliability with intraclass correlation at 0.80 or higher on key constructs. Bivariate and multivariate analyses were performed to identify the spatial patterns in posting HIV-related tweets as well as the relationships between the tweets and local HIV infection rates. RESULTS:We analyzed 2517 tweets that were identified as relevant to HIV risk and prevention tags; these tweets were geolocated in 109 counties throughout the United States. After adjusting for region, HIV prevalence, and social disadvantage index, our findings indicated that every 100-tweet increase in HIV-specific tweets per capita from noninstitutional accounts was associated with a multiplicative effect of 0.97 (95% CI [0.94-1.00]; P=.04) on the incidence of HIV infections in the following year in a given county. CONCLUSIONS:Twitter may serve as a proxy of public behavior related to HIV infections, and the association between the number of HIV-related tweets and HIV infection rates further supports the use of social media for HIV disease prevention.
| S-EPMC7380998 | biostudies-literature