Increased Internet Search Interest for GI Symptoms May Predict COVID-19 Cases in US Hotspots

Ahmad, Imama; Flanagan, Ryan; Staller, Kyle

doi:10.1016/j.cgh.2020.06.058

Cited by 31 publications

(36 citation statements)

References 5 publications

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“…Other authors [10], focusing on Google queries concerning loss of smell and taste in the US and Italy, draw opposite conclusions and suggest that Google Trends may not be reliably used to predict COVID-19 cases. Nevertheless [11] show that search interest in common gastrointestinal symptoms tend to correlate with coronavirus data recorded in the U.S. hotpots. Finally, a statistically significant correlation between Google search related to smell loss with COVID-19 cases and death has been proven for eight countries [12].…”

Section: Introductionmentioning

confidence: 81%

Searching for the Peak Google Trends and the COVID-19 Outbreak in Italy

Brunori

Resce

Serlenga

2021

Preprint

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One of the difficulties faced by policy makers during the COVID-19 outbreak in Italy was the monitoring of the virus diffusion. Due to changes in the criteria and insufficient resources to test all suspected cases, the number of ‘confirmed infected’ rapidly proved to be unreliably reported by official statistics. We explore the possibility of using information obtained from Google Trends to predict the evolution of the epidemic. Following the most recent developments on the statistical analysis of longitudinal data, we estimate a dynamic heterogeneous panel. This approach allows to takes into account the presence of common shocks and unobserved components in the error term both likely to occur in this context. We find that Google queries contain useful information to predict number patients admitted to the intensive care units, number of deaths and excess mortality in Italian regions.

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Section: Introductionmentioning

confidence: 81%

Searching for the Peak Google Trends and the COVID-19 Outbreak in Italy

Brunori

Resce

Serlenga

2021

Preprint

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“…Thus, we encourage caution regarding the interpretation of COVID-19 modeling experiments based on Google search engines. For example, Ahmad et al, 2020, found a correlation between gastrointestinal search terms obtained through GT and COVID-19 cases and suggested that Internet searches may be useful in predicting COVID-19 cases using a four-week lag in the U.S. This correlation might be an artifact since none of the gastrointestinal terms are specific to COVID-19, and the only COVID-19 specific term— ‘ageusia’—increased during the time that the pandemic was declared (i.e., 11 March) and decreased while cases started to increase (Figure 1 in Ahmad et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Brodeur et al, 2021, used GT to see how lockdowns affected well-being in the U.S. Once lockdowns were implemented, well-being likely decreased, as searches for certain terms such as ‘stress’, ‘suicide’, and ‘worry’ increased over the lockdown period. Ahmad et al, 2020 used gastrointestinal-related symptom search terms to determine whether GT could predict COVID-19 incidence, and found correlations between the search terms and increases of COVID-19 cases in multiple regions across the U.S. with a four-week lag.…”

Section: Introductionmentioning

confidence: 99%

Using Google Health Trends to investigate COVID-19 incidence in Africa

Fulk

Romero-Álvarez

Abu-Saymeh

et al. 2021

Preprint

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Objectives: In the present manuscript we used Internet-derived data to assess whether Google Health Trends (GHT) search counts are able to track COVID-19 incidences in Africa. Methods: We collected COVID-19 case and death incidence for 54 African countries from February 2020 to January 2021. We used GHT to characterize COVID-19 incidence up to 24 January 2021, collecting the number of searches of four terms: coronavirus, coronavirus symptoms, COVID19, and pandemic. The terms were related to weekly COVID-19 case incidences for the study period via multiple linear regressions. We also collected 72 predictors assessing Internet accessibility, demographics, economics, etc., to explain the potential mechanisms linking the relationship between GHT searches and COVID-19. Results: Important increases for COVID-19 death incidence were observed for South Africa and Tunisia at the end of the study period. Our study demonstrated a lack of correlation between GHT and COVID-19 incidence for most African countries. The predictors analyzed were unhelpful in explaining the pattern of GHT statistics and their relationship to COVID-19, complicating interpretability of GHT. Conclusions: According to our results, GHT-based surveillance for an ongoing epidemic might be useful only in specific situations. Future studies might assess the algorithm in different epidemic contexts.

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“…Another group of studies did examine symptom-related search terms, including: “COVID pneumonia” and “COVID heart” in the US 36 ; “COVID” and “pneumonia” in China 37 ; “worry”, “hysteria” and other mental health symptoms in the US 38 ; “diarrhea”, “nausea”, “vomiting”, “abdominal pain”, “fever” and “cough” in the US 39 ; “can’t smell” “ear pain”, “sinus pain”, “voice pain”, “ears ringing”. Among other terms in the US 40 ; “ageusia”, “abdominal pain”, “loss of appetite”, “anorexia”, “diarrhea”, and “vomiting” in the US 41 ; “chest pain,” “myocardial infarction,” “cough,” and “fever” in four countries 42 ; “Loss of sense of smell”,”Sense of smell”, “Loss of sense of taste”, and “Sense of taste” across multiple regions in five countries 25 ; and “smell”, “loss of smell”, “anosmia”, “hyposmia”, “olfaction”, “taste”, “loss of taste”, and “dysgeusia” in eight countries 43 . While these studies investigated a range of important questions, including the effects of COVID-19 on mental health 38 , gastrointestinal health 39 , 41 , otolaryngological health 40 , and coronary-related conditions 42 , they did not study whether the temporal relationships between these search terms could be useful in understanding the clinical progression of the disease.…”

Section: Discussionmentioning

confidence: 99%

Internet search patterns reveal clinical course of COVID-19 disease progression and pandemic spread across 32 countries

Reis

2020

Preprint

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Background: Coronavirus disease 2019 is an emerging infectious disease.It was first reported in Wuhan, China, and then broke out on a large scale around the world.This study aimed to assess the clinical significance of two different nutritional indices in 245 patients with COVID-19. Methods:In this retrospective single-center study, we finally included 245 consecutive patients who confirmed COVID-19 in Wuhan University Zhongnan Hospital from January 1 to February 29. Cases were classified as either discharged or dead. Demographic, clinical and laboratory datas were registered, two different nutritional indices were calculated: (i)the Controlling nutritional status (CONUT) score;(ii) prognostic nutritional index (PNI). We used univariate and multivariate logistic regression analysis to explore the relationship between nutritional indices and hospital death .Results: 212 of them were discharged and 33 of them died. In-hospital mortality was signifcantly higher in the severe group of PNI than in the moderate and normal groups. It was also significantly worse in the severe-CONUT group than in the moderate-, mild-, and normal-CONUT groups.Multivariate logistic regression analysis showed the CONUT score (odds ratio3.371,95%CI(1.124-10.106), p = 0.030) and PNI(odds ratio 0.721,95% CI(0.581-0.896),P=0.003) were independent predictors of all-cause death at an early stage; Multivariate logistic regression analysis also showed that the severe group of PNI was the independent risk predictor of in-hospital death(odds ratio 24.225, 95% CI (2.147-273.327) , p=0.010).The CONUT score cutoff value was 5.5 (56.00 and 80.81%; AUC 0.753; 95% CI (0.644-0.862) ;respectively).The PNI cutoff value was 40.58 (81.80 and 66.20%; AUC 0.778; 95% CI(0.686-0.809); respectively).We use PNI and the COUNT score to assess malnutrition, which can have a prognosis effect of COVID-19patients. Conclusion:The CONUT score and PNI could be a reliable prognostic marker of all-cause deathin patients with COVID-19. (933-5129) <0·001 PCT,ng/ml 0.0(0.00-0.11) 0 (0.00-0.07) 0 (0.00-0.03) 0.06 (0.00-0.23) 0.43 (0.1-1.16) <0·001 All-cause death 25(16.13%) 1(5.6%) 6(8.3%) 12(20.69%) 6(85.71%) <0·001 COPD, chronic obstructive pulmonary disease; WBC, white blood cells; ALT, alanine aminotransferase;TC,Total cholesterol; HDL, high density lipoprotein PT, prothrombin time;PCT,Procalcitonin; CONUT, Controlling nutritional status; PNI, Prognostic nutritional index; BMI, Body mass index *Between evaluation of clinical indicators and COUNT score, 90 patients lacked records of lymphocyte counts or albumin or total cholesterolModel 1 :The multivariate analysis of PNI associated with in-hospital death adjusted for age, sex, BMI, history of hypertension, history of chronic obstructive pulmonary disease ， history of coronary heart disease ,respiratory rate, alanine transaminase, creatinine, D-dimer, white blood cell count, Lymphocyte, PCT, Model 2 :The multivariate analysis of COUNT score associated with in-hospital death adjusted for age, sex, BMI, history of hypertension, h...

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Increased Internet Search Interest for GI Symptoms May Predict COVID-19 Cases in US Hotspots

Cited by 31 publications

References 5 publications

Searching for the Peak Google Trends and the COVID-19 Outbreak in Italy

Searching for the Peak Google Trends and the COVID-19 Outbreak in Italy

Using Google Health Trends to investigate COVID-19 incidence in Africa

Internet search patterns reveal clinical course of COVID-19 disease progression and pandemic spread across 32 countries

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