About: Jonathan Wolf

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An Entity of Type : wikibase:Item, within Data Space : wikidata.demo.openlinksw.com associated with source document(s)

researcher 0000-0002-0530-2257

Attributes	Values
rdf:type	Item
description	Forscher (de) 연구원 (ko) investigador (es) araştırmacı (tr) chercheur (fr) wetenschapper (nl) дослідник (uk) 研究者 (zh) 研究員 (ja) հետազոտող (hy) researcher 0000-0002-0530-2257 (en)
rdfs:label	Jonathan Wolf (en) Jonathan Wolf (nl)
skos:prefLabel	Jonathan Wolf (en) Jonathan Wolf (nl)
name	Jonathan Wolf (en) Jonathan Wolf (nl)
given name	wds:Q94551497-B35F771B-1B75-4B5F-91A6-DE76FDAB0906
given name	Jonathan
instance of	wds:Q94551497-025AC14D-9B68-4ECF-9D25-1EB64B7AACA6
instance of	Homo sapiens
occupation	wds:Q94551497-C7FA399D-CEFC-4A0F-932F-9FAAA3FEF277
sex or gender	wds:Q94551497-0a2cf4a9-4aff-5ddb-69ad-7071419d82c7
on focus list of Wikimedia project	wds:Q94551497-12E04C2F-3641-4F8B-8145-3DC97B5500A8
educated at	wds:Q94551497-185B6291-4B0C-4A29-ADA2-9EE2F5EC81FB
family name	wds:Q94551497-CA5214D9-F2B0-47DE-A4C0-8E52DE2B6E2E
occupation	researcher
sex or gender	male
on focus list of Wikimedia project	WikiProject COVID-19
educated at	University of Oxford
family name	Wolf
ORCID iD	wds:Q94551497-41E26999-F225-4781-8A01-34623412D888
ORCID iD	https://orcid.org/0000-0002-0530-2257
ORCID iD	0000-0002-0530-2257
ORKG ID	wds:Q94551497-7a22a809-ed5e-48fe-90bb-2aa52b3274a2
ORKG ID	R146497
is about of	https://www.wikidata.org/wiki/Special:EntityData/Q94551497
is author of	Attributes and predictors of Long-COVID: analysis of COVID cases and their symptoms collected by the Covid Symptoms Study App Human postprandial responses to food and potential for precision nutrition Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study Estimates of the rate of infection and asymptomatic COVID-19 disease in a population sample from SE England Real-time tracking of self-reported symptoms to predict potential COVID-19 Geo-social gradients in predicted COVID-19 prevalence in Great Britain: results from 1 960 242 users of the COVID-19 Symptoms Study app Anosmia and other SARS-CoV-2 positive test-associated symptoms, across three national, digital surveillance platforms as the COVID-19 pandemic and response unfolded: an observation study Changes in symptomatology, re-infection and transmissibility associated with SARS-CoV-2 variant B.1.1.7: an ecological study Symptoms and syndromes associated with SARS-CoV-2 infection and severity in pregnant women from two community cohorts Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals Geo-social gradients in predicted COVID-19 prevalence and severity in Great Britain: results from 2,266,235 users of the COVID-19 Symptoms Tracker app Rapid implementation of mobile technology for real-time epidemiology of COVID-19 SARS-CoV-2 (COVID-19) infection in pregnant women: characterization of symptoms and syndromes predictive of disease and severity through real-time, remote participatory epidemiology Race, ethnicity, community-level socioeconomic factors, and risk of COVID-19 in the United States and the United Kingdom Blue poo: impact of gut transit time on the gut microbiome using a novel marker Improved Cardiometabolic Health Using a Personalised Nutrition Approach: The ZOE METHOD Study Current smoking and COVID-19 risk: results from a population symptom app in over 2.4 million people Publisher Correction: Human postprandial responses to food and potential for precision nutrition Rapid implementation of mobile technology for real-time epidemiology of COVID-19 Attributes and predictors of long COVID Author Correction: Attributes and predictors of long COVID Probable delirium is a presenting symptom of COVID-19 in frail, older adults: a cohort study of 322 hospitalised and 535 community-based older adults Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application: a prospective, observational study Anxiety and depression symptoms after COVID-19 infection: results from the COVID Symptom Study app Vaccine side-effects and SARS-CoV-2 infection after vaccination in users of the COVID Symptom Study app in the UK: a prospective observational study Symptom clusters in Covid19: A potential clinical prediction tool from the COVID Symptom study app Glycaemic variability, assessed with continuous glucose monitors, is associated with diet, lifestyle and health in people without diabetes. Knowledge barriers in the symptomatic-COVID-19 testing programme in the UK: an observational study Association of social distancing and masking with risk of COVID-19 High intake of vegetables is linked to lower white blood cell profile and the effect is mediated by the gut microbiome Risk factors and disease profile of post-vaccination SARS-CoV-2 infection in UK users of the COVID Symptom Study app: a prospective, community-based, nested, case-control study Loss of smell and taste in combination with other symptoms is a strong predictor of COVID-19 infection Modest effects of dietary supplements during the COVID-19 pandemic: insights from 445 850 users of the COVID-19 Symptom Study app Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app Impact of COVID-19 on health behaviours and body weight: A prospective observational study in a cohort of 1.1 million UK and US individuals The COronavirus Pandemic Epidemiology (COPE) Consortium: A Call to Action Risk of COVID-19 among frontline healthcare workers and the general community: a prospective cohort study Robust, reproducible clinical patterns in hospitalised patients with COVID-19 Validity of continuous glucose monitoring for categorizing glycemic responses to diet: implications for use in personalized nutrition Changes in symptomatology, reinfection, and transmissibility associated with the SARS-CoV-2 variant B.1.1.7: an ecological study Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application Post-vaccination SARS-CoV-2 infection: risk factors and illness profile in a prospective, observational community-based case-control study Exploring the relationship between social jetlag with gut microbial composition, diet and cardiometabolic health, in the ZOE PREDICT 1 cohort Plasma protein N-glycome composition associates with postprandial lipaemic response Identification of optimal symptom combinations to trigger diagnostic work-up of suspected COVID-19 cases: analysis from a community-based, prospective, observational cohort Accessible data curation and analytics for international-scale citizen science datasets Diagnostic value of cutaneous manifestation of SARS‐CoV‐2 infection* Postprandial glycaemic dips predict appetite and energy intake in healthy individuals Effect of Postprandial Glucose Dips on Hunger and Energy Intake in 1102 Subjects in US and UK: The PREDICT 1 Study Characterisation of Fasting and Postprandial NMR Metabolites: Insights from the ZOE PREDICT 1 Study Cancer and Risk of COVID-19 Through a General Community Survey Current tobacco smoking and risk from COVID-19: results from a population symptom app in over 2.4 million people Diet quality and risk and severity of COVID-19: a prospective cohort study Dietary supplements during the COVID-19 pandemic: insights from 1.4M users of the COVID Symptom Study app - a longitudinal app-based community survey Attributes and predictors of Long-COVID: analysis of COVID cases and their symptoms collected by the Covid Symptoms Study App Anosmia, ageusia, and other COVID-19-like symptoms in association with a positive SARS-CoV-2 test, across six national digital surveillance platforms: an observational study Ten Hour Time-Restricted Eating (TRE) Is Associated with Improvements in Energy, Mood, Hunger and Weight in Free-Living Settings: The ZOE BIG IF Study Menopause is associated with postprandial metabolism, metabolic health and lifestyle: The ZOE PREDICT study Snack quality and snack timing are associated with cardiometabolic blood markers: the ZOE PREDICT study
is author of	wds:Q108242643-D05FA6FC-AB1F-47FF-AD81-9FB7EECACFFA wds:Q100696335-D6408D66-392B-4C12-90C4-A6618346D0EA wds:Q104615457-E5F2CEBB-B779-48BA-A302-95CCF3EB9E72

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