FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3092968609> ?p ?o ?g. }

• W3092968609 endingPage "407" @default.
• W3092968609 startingPage "406" @default.
• W3092968609 abstract "We read “Increased internet search interest for GI symptoms may predict COVID-19 cases in US hotspots” by Ahmad et al1Ahmad I. et al.Clin Gastroenterol Hepatol. 2020; 18 (2833–2834.e3)Abstract Full Text Full Text PDF Scopus (16) Google Scholar with interest. The authors compared search volume for gastrointestinal (GI) symptoms with coronavirus disease 2019 (COVID-19) incidence in 15 states to observe that searches for the terms ageusia, loss of appetite, and diarrhea correlated with disease burden at 4 weeks. We commend the authors for bringing attention to the role of digital epidemiology in the fight against the pandemic. Findings are important and could have substantial public health implications. Further examination of the factors that could influence these relationships may be worthwhile. As the authors demonstrated, choices for data collection could impact results. In this study, 15 states were chosen for analysis, ensuring equal coverage by disease burden. Would an expanded sample including all 50 states be informative? It is possible that factors including state-by-state differences in testing capabilities could influence measurable disease burden. Incorporation of time-lagged cross-correlations is prudent because it is expected that search activity will precede case diagnosis. Lag times of 1, 2, 3, and 4 weeks were examined to find that a lag time of 4 weeks corresponded best to case load. The authors appropriately acknowledge this exceeds the 1- to 2-week lag time observed with influenza. Research has shown that median time from symptom onset to hospital admission is 7.0 days (interquartile range, 4.0–8.0).2Wang D. et al.JAMA. 2020; 323: 1061-1069Crossref PubMed Scopus (15144) Google Scholar People tend to search symptoms at the time they are experiencing them. Therefore, the increased incubation period for COVID-19 compared with influenza does not completely explain the increased lag between symptom searching and case volume. One alternative explanation is that searching before symptom onset may be more indicative of disease interest than disease burden, which could contribute to this delay. Among the GI symptoms analyzed, ageusia and loss of appetite can also be encountered with upper respiratory symptoms due to nasal congestion and malaise. Perhaps diarrhea is the most specific GI symptom because severe acute respiratory syndrome–associated coronavirus 2 (SARS-CoV-2) uses angiotensin-converting enzyme 2 receptors in the intestinal enterocytes, resulting in diarrhea, similar to norovirus.3Zhou P. et al.Nature. 2020; 579: 270-273Crossref PubMed Scopus (13296) Google Scholar,4Zhang H. Int J Infect Dis. 2020; 96: 19-24Abstract Full Text Full Text PDF PubMed Scopus (183) Google Scholar One systematic review and meta-analysis found a pooled rate of 7.4% of COVID-19 patients with diarrhea and 4.6% with nausea or vomiting.5Parasa S. et al.JAMA Netw Open. 2020; 3e2011335Crossref PubMed Scopus (271) Google Scholar Because GI symptoms are not present in more than 90% of patients, estimation of COVID-19 cases on the basis of GI symptoms appears suboptimal compared with more typical respiratory symptoms. People often use search engines to investigate symptoms before seeking medical care during a pandemic or otherwise. It is estimated that 72% of American Internet users look online for medical information, and 77% start with a search engine.6Fox S. Available at: https://www.pewresearch.org/internet/2013/01/15/health-online-2013/. Accessed June 8, 2020.Google Scholar Thus, assessing searches for fever and common respiratory symptoms as controls or comparisons to searches for GI symptoms and capturing the relative frequency of searches occurring with COVID-19 or independently of COVID-19 could provide further insight. In our own analysis of Google Trends, we made similar observations with a few distinctions. We assessed COVID-19 plus diarrhea searches and United States COVID-19 epidemiology by using the Pearson correlation coefficient. We used Centers for Disease Control and Prevention (CDC) data for reported incidence and mortality (deaths per capita), cross-referencing with U.S. census data.7Centers for Disease Control and Prevention website. Available at: https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html. Accessed May 10, 2020.Google Scholar,8United States Census Bureau. Available at: https://data.census.gov/cedsci/all?q=Total%20Population%20in%20the%20United%20States&hidePreview=false&tid=ACSDP1Y2018.DP05. Accessed May 10, 2020.Google Scholar In contrast to the current study, we found no significant correlation between state-by-state searches for COVID-19 plus diarrhea and disease incidence (P = .19). However, there was a weak correlation between COVID-19 plus diarrhea searches and mortality (R = 0.31, P = .03), and use of individual state health department data, instead of CDC data, revealed a weak correlation (R = 0.37, P = .008) between search frequency and percent positivity. Assessment of a control search term fever (a more common symptom of COVID-19) also revealed weak correlations between fever and disease incidence (R = 0.29, P = .041) and between fever and mortality (R = 0.38, P = .007). In addition, we examined searches for diarrhea alone. At outbreak emergence, diarrhea was searched twice as often as COVID-19 and 8 times as often as COVID-19 plus diarrhea. We also observed that peak search activity for COVID-19 and diarrhea paralleled initial media reports describing GI symptoms of COVID-19, suggesting that Internet activity could be shaped by media coverage. In other infectious disease surveillance studies, the Pearson coefficient R values are often >0.70, not 0.2–0.4 range, as demonstrated in our analysis. Our findings suggest that diarrhea searches do not correlate well with disease burden; however, although analysis of online searches for GI symptoms and COVID-19 is not likely to be a good substitute for more traditional epidemiologic methods, search activity could still be useful as part of a more complex model. As you have concluded, Google Trends is a valuable tool, and it is our responsibility to carefully understand and refine its role in this global pandemic. Increased Internet Search Interest for GI Symptoms May Predict COVID-19 Cases in US HotspotsClinical Gastroenterology and HepatologyVol. 18Issue 12PreviewGoogle 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., It has previously correlated well with infectious disease incidence and has demonstrated utility in disease forecasting, especially with influenza data. We utilized Google Trends to investigate whether search interest in common gastrointestinal (GI) symptoms would correlate with coronavirus disease 2019 (COVID-19) incidence data. Full-Text PDF" @default.
• W3092968609 created "2020-10-22" @default.
• W3092968609 creator A5009248318 @default.
• W3092968609 creator A5023133909 @default.
• W3092968609 creator A5083048130 @default.
• W3092968609 date "2021-02-01" @default.
• W3092968609 modified "2023-10-17" @default.
• W3092968609 title "COVID-19 Epidemiology and Google Searches" @default.
• W3092968609 cites W3004280078 @default.
• W3092968609 cites W3017309779 @default.
• W3092968609 cites W3033466557 @default.
• W3092968609 cites W3039832130 @default.
• W3092968609 cites W3165656738 @default.
• W3092968609 doi "https://doi.org/10.1016/j.cgh.2020.10.020" @default.
• W3092968609 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7557354" @default.
• W3092968609 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33068785" @default.
• W3092968609 hasPublicationYear "2021" @default.
• W3092968609 type Work @default.
• W3092968609 sameAs 3092968609 @default.
• W3092968609 citedByCount "0" @default.
• W3092968609 crossrefType "journal-article" @default.
• W3092968609 hasAuthorship W3092968609A5009248318 @default.
• W3092968609 hasAuthorship W3092968609A5023133909 @default.
• W3092968609 hasAuthorship W3092968609A5083048130 @default.
• W3092968609 hasBestOaLocation W30929686091 @default.
• W3092968609 hasConcept C107130276 @default.
• W3092968609 hasConcept C119060515 @default.
• W3092968609 hasConcept C126322002 @default.
• W3092968609 hasConcept C138816342 @default.
• W3092968609 hasConcept C142724271 @default.
• W3092968609 hasConcept C144024400 @default.
• W3092968609 hasConcept C149923435 @default.
• W3092968609 hasConcept C2779134260 @default.
• W3092968609 hasConcept C3008058167 @default.
• W3092968609 hasConcept C524204448 @default.
• W3092968609 hasConcept C71924100 @default.
• W3092968609 hasConcept C89623803 @default.
• W3092968609 hasConceptScore W3092968609C107130276 @default.
• W3092968609 hasConceptScore W3092968609C119060515 @default.
• W3092968609 hasConceptScore W3092968609C126322002 @default.
• W3092968609 hasConceptScore W3092968609C138816342 @default.
• W3092968609 hasConceptScore W3092968609C142724271 @default.
• W3092968609 hasConceptScore W3092968609C144024400 @default.
• W3092968609 hasConceptScore W3092968609C149923435 @default.
• W3092968609 hasConceptScore W3092968609C2779134260 @default.
• W3092968609 hasConceptScore W3092968609C3008058167 @default.
• W3092968609 hasConceptScore W3092968609C524204448 @default.
• W3092968609 hasConceptScore W3092968609C71924100 @default.
• W3092968609 hasConceptScore W3092968609C89623803 @default.
• W3092968609 hasIssue "2" @default.
• W3092968609 hasLocation W30929686091 @default.
• W3092968609 hasLocation W30929686092 @default.
• W3092968609 hasLocation W30929686093 @default.
• W3092968609 hasOpenAccess W3092968609 @default.
• W3092968609 hasPrimaryLocation W30929686091 @default.
• W3092968609 hasRelatedWork W2061253854 @default.
• W3092968609 hasRelatedWork W2971392718 @default.
• W3092968609 hasRelatedWork W3106370311 @default.
• W3092968609 hasRelatedWork W3119540162 @default.
• W3092968609 hasRelatedWork W3120887007 @default.
• W3092968609 hasRelatedWork W3152916563 @default.
• W3092968609 hasRelatedWork W3162511055 @default.
• W3092968609 hasRelatedWork W4313474620 @default.
• W3092968609 hasRelatedWork W4327956415 @default.
• W3092968609 hasRelatedWork W4381942459 @default.
• W3092968609 hasVolume "19" @default.
• W3092968609 isParatext "false" @default.
• W3092968609 isRetracted "false" @default.
• W3092968609 magId "3092968609" @default.
• W3092968609 workType "article" @default.