Ocular Signs of COVID-19 Suggested by Internet Search Term Patterns Worldwide.
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ABSTRACT:
SUBMITTER: Deiner MS
PROVIDER: S-EPMC7832289 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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.
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Project description:Effective public health response to novel pandemics relies on accurate and timely surveillance of pandemic spread, as well as characterization of the clinical course of the disease in affected individuals. We sought to determine whether Internet search patterns can be useful for tracking COVID-19 spread, and whether these data could also be useful in understanding the clinical progression of the disease in 32 countries across six continents. Temporal correlation analyses were conducted to characterize the relationships between a range of COVID-19 symptom-specific search terms and reported COVID-19 cases and deaths for each country from January 1 through April 20, 2020. Increases in COVID-19 symptom-related searches preceded increases in reported COVID-19 cases and deaths by an average of 18.53 days (95% CI 15.98-21.08) and 22.16 days (20.33-23.99), respectively. Cross-country ensemble averaging was used to derive average temporal profiles for each search term, which were combined to create a search-data-based view of the clinical course of disease progression. Internet search patterns revealed a clear temporal pattern of disease progression for COVID-19: Initial symptoms of fever, dry cough, sore throat and chills were followed by shortness of breath an average of 5.22 days (3.30-7.14) after initial symptom onset, matching the clinical course reported in the medical literature. This study shows that Internet search data can be useful for characterizing the detailed clinical course of a disease. These data are available in real-time at population scale, providing important benefits as a complementary resource for tracking pandemics, especially before widespread laboratory testing is available.
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Project description:The pandemic of COVID-19 is seriously challenging the medical organization in many parts of the world. This novel corona virus SARS-CoV-2 has a specific tropism for the low respiratory airways, but causes severe pneumonia in a low percentage of patients. However, the rapid spread of the infection during this pandemic is causing the need to hospitalize a high number of patients. Pneumonia in COVID-19 has peculiar features and can be studied by lung ultrasound in the early approach to suspected patients. The sonographic signs are non-specific when considered alone, but observation of some aspects of vertical artifacts can enhance the diagnostic power of the ultrasound examination. Also, the combination of sonographic signs in patterns and their correlation with blood exams in different phenotypes of the disease may allow for a reliable characterization and be of help in triaging and admitting patients.
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Project description:As the COVID-19 ravaging through the globe, accurate forecasts of the disease spread are crucial for situational awareness, resource allocation, and public health decision-making. Alternative to the traditional disease surveillance data collected by the United States (US) Centers for Disease Control and Prevention (CDC), big data from Internet such as online search volumes also contain valuable information for tracking infectious disease dynamics such as influenza epidemic. In this study, we develop a statistical model using Internet search volume of relevant queries to track and predict COVID-19 pandemic in the United States. Inspired by the strong association between COVID-19 death trend and symptom-related search queries such as "loss of taste", we combine search volume information with COVID-19 time series information for US national level forecasts, while leveraging the cross-state cross-resolution spatial temporal framework, pooling information from search volume and COVID-19 reports across regions for state level predictions. Lastly, we aggregate the state-level frameworks in an ensemble fashion to produce the final state-level 4-week forecasts. Our method outperforms the baseline time-series model, while performing reasonably against other publicly available benchmark models for both national and state level forecast.
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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.
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Project description:Firearm-related violence is a leading cause of morbidity and mortality and is at the center of a major public health and policy debate in the United States. Despite the critical role of data in informing this debate, accurate and comprehensive data on firearm sales and ownership is not readily available. In this study, we evaluate the potential of using firearm-related internet search queries as a complementary, freely available, and near-real-time data source for tracking firearm sales and ownership that enables analysis at finer geographic and temporal scales. (Here, we examine data by state and by month to compare with other data sources, but search engine volume can be analyzed by city and by the week or by day). We validate search query volume against available data on background checks in all 50 US states, and find that they are highly correlated over time (Pearson's r = 0.96, Spearman's ρ = 0.94) and space (Pearson's r = 0.78, Spearman's ρ = 0.76). We find that stratifying this analysis by gun type (long-gun vs. handgun) increases this correlation dramatically, across both time and space. We also find a positive association between firearm-related search query volume and firearm-related mortality (Pearson's r = 0.87, Spearman's ρ = 0.90), and a negative association with the strength of state-level firearm control policies (Pearson's r = -0.82, Spearman's ρ = -0.83). Based on these findings, we propose a framework for prospective surveillance that incorporates firearm-related internet search volume as a useful complementary data source to inform the public health policy debate on this issue.
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Project description:We study the impact of the COVID-19 pandemic on domestic violence in 11 countries with different ex-ante incidence of domestic violence (DV) and lockdown intensity. We use a novel measure of DV incidents that allows us to make cross-country comparisons: a Google search intensity index of DV-related topics. Our difference-in-difference estimates show an increase in DV search intensity after lockdown (30%), with larger effects as more people stayed at home (measured with Google Mobility Data). The peak of the increase in DV appears, on average, 5 weeks after the introduction of the lockdown. While we observe that the positive impact on DV is a widespread phenomenon, the effect in developed countries is more than twice as strong as in Latin American countries. We show that the difference in impact correlates with the intensity of compliance with stay-at-home measures in the two groups.
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Project description:OBJECTIVE:The spread of misinformation has accompanied the coronavirus pandemic, including topics such as immune boosting to prevent COVID-19. This study explores how immune boosting is portrayed on the internet during the COVID-19 pandemic. DESIGN:Content analysis. METHODS:We compiled a dataset of 227 webpages from Google searches in Canada and the USA using the phrase 'boost immunity' AND 'coronavirus' on 1 April 2020. We coded webpages for typology and portrayal of immune boosting and supplements. We recorded mentions of microbiome, whether the webpage was selling or advertising an immune boosting product or service, and suggested strategies for boosting immunity. RESULTS:No significant differences were found between webpages that appeared in the searches in Canada and the USA. The most common types of webpages were from news (40.5%) and commercial (24.7%) websites. The concept of immune boosting was portrayed as beneficial for avoiding COVID-19 in 85.5% of webpages and supplements were portrayed as beneficial in 40% of the webpages, but commercial sites were more likely to have these portrayals. The top immune boosting strategies were vitamin C (34.8%), diet (34.4%), sleep (34.4%), exercise (30.8%) and zinc (26.9%). Less than 10% of the webpages provide any critique of the concept of immune boosting. CONCLUSIONS:Pairing evidence-based advice for maintaining one's health (eg, healthy diet, exercise, sleep) with the phrase immune boosting and strategies lacking in evidence may inadvertently help to legitimise the concept, making it a powerful marketing tool. Results demonstrate how the spread of misinformation is complex and often more subtle than blatant fraudulent claims.
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Project description: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.1,2 It has previously correlated well with infectious disease incidence and has demonstrated utility in disease forecasting, especially with influenza data.3 We utilized Google Trends to investigate whether search interest in common gastrointestinal (GI) symptoms would correlate with coronavirus disease 2019 (COVID-19) incidence data.
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Project description:BackgroundThe outbreak of COVID-19 has affected the mental health of adolescents. To describe the Internet behavior-changing patterns of adolescents and to understand the impact of clinical features on changing patterns during the COVID-19 pandemic.Materials and methodsWe conducted a cross-sectional cohort study using data collected through online investigation in China. A total of 625 adolescents completed the online survey from May 15 to June 7, 2020. The adolescents were asked to retrospect to the Internet behaviors and game behaviors of three time periods as follows: before the COVID-19 outbreak in China, during the COVID-19 outbreak in China, and back to school. The clinical variables of the demographic data, family functionality, and emotional and behavioral symptoms were also collected. According to the Internet behaviors and game behaviors patterns across the three time periods, the subjects will be sub-grouped.ResultsFour Internet behavior-changing patterns during the COVID-19 was identified: (1) Continuous Normal Group (55.52%); (2) Normal to Internet Addiction Group (5.28%); (3) Internet Addiction to Normal Group (14.56%); and (4) Continuous Internet Addiction Group (24.64%). Years of education, academic score ranking, family functionality, and emotional and behavioral symptoms were different across the four groups. Proportions of game behaviors, scores of Strengths and Difficulties Questionnaire (SDQ), and SDQ subscale during the period before the COVID-19 outbreak were significant in predicting changing patterns.ConclusionThe Internet behavior patterns of adolescents during the COVID-19 period were various. Clinical features before the COVID-19 pandemic may predict changing patterns. The heterogeneity in characteristics between different changing patterns should be considered when intervening in adolescents' problematic Internet behavior.
| S-EPMC9685307 | biostudies-literature