Building an international consortium for tracking coronavirus health status

Segal, Eran; Zhang, Feng; Lin, Xihong; King, Gary; Shalem, Ophir; Shilo, Smadar; Allen, William E.; Alquaddoomi, Faisal; Altae-Tran, Han; Anders, Simon; Balicer, Ran D.; Bauman, Tal; Bonilla, Ximena; Booman, Gisel; Chan, Andrew T.; Cohen, Ori; Coletti, Silvano; Davidson, Natalie R.; Dor, Yuval; Drew, David A.; Elemento, Olivier; Evans, G. Edward; Ewels, Phil; Gale, J. S.; Gavrieli, Amir; Geiger, Benjamin; Grad, Yonatan H.; Greene, Casey S.; Hajirasouliha, Iman; Jerala, Roman; Kahles, André; Kallioniemi, Olli; Keshet, Ayya; Kocarev, Ljupčo; Landua, Gregory; Meir, Tomer; Müller, Aline; Nguyen, Long H.; Orešič, Matej; Ovchinnikova, Svetlana; Peterson, Hedi; Prodanova, Jana; Rajagopal, Jay; Rätsch, Gunnar; Rossman, Hagai; Rung, Johan; Sboner, Andrea; Sigaras, Alexandros; Spector, Tim D.; Steinherz, Ron; Stevens, Irene; Vilo, Jaak; Wilmes, Paul

doi:10.1038/s41591-020-0929-x

Cited by 28 publications

(13 citation statements)

References 14 publications

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“…Similarly, we are committed to working together with similar data participatory infectious disease surveillance efforts in the United States ( 24 , 25 ), including collaborations with CovidNearYou and the HowWeFeel Project ( 26 ). Although several other digital collection tools for COVID-19 symptoms have been launched in the United States, these applications have largely been configured to offer a single assessment of symptoms to tailor recommendations for further evaluation.…”

Section: Cope: Addressing the Near-term Covid-19 Crisismentioning

confidence: 99%

The COronavirus Pandemic Epidemiology (COPE) Consortium: A Call to Action

Chan

Drew

Nguyen

et al. 2020

Cancer Epidemiology, Biomarkers &Amp; Prevention

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The rapid pace of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; COVID-19) pandemic presents challenges to the real-time collection of population-scale data to inform near-term public health needs as well as future investigations. We established the COronavirus Pandemic Epidemiology (COPE) consortium to address this unprecedented crisis on behalf of the epidemiology research community. As a central component of this initiative, we have developed a COVID Symptom Study (previously known as the COVID Symptom Tracker) mobile application as a common data collection tool for epidemiologic cohort studies with active study participants. This mobile application collects information on risk factors, daily symptoms, and outcomes through a user-friendly interface that minimizes participant burden. Combined with our efforts within the general population, data collected from nearly 3 million participants in the United States and United Kingdom are being used to address critical needs in the emergency response, including identifying potential hot spots of disease and clinically actionable risk factors. The linkage of symptom data collected in the app with information and biospecimens already collected in epidemiology cohorts will position us to address key questions related to diet, lifestyle, environmental, and socioeconomic factors on susceptibility to COVID-19, clinical outcomes related to infection, and long-term physical, mental health, and financial sequalae. We call upon additional epidemiology cohorts to join this collective effort to strengthen our impact on the current health crisis and generate a new model for a collaborative and nimble research infrastructure that will lead to more rapid translation of our work for the betterment of public health.

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Section: Cope: Addressing the Near-term Covid-19 Crisismentioning

confidence: 99%

The COronavirus Pandemic Epidemiology (COPE) Consortium: A Call to Action

Chan

Drew

Nguyen

et al. 2020

Cancer Epidemiology, Biomarkers &Amp; Prevention

Self Cite

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“…Our understanding of COVID-19's long-term impact is preliminary. Contributing serious insights will require a mix of domain expertise from multiple fields, and there is already a push for better international collaboration and tracking of COVID-19 [230]. For example, the use of black-box models might yield a superficially practical solution, but could be useless without the involvement of (international) medical and biotechnology expert interpretations.…”

Section: The Need For Multidisciplinary Collaborationmentioning

confidence: 99%

Leveraging Data Science To Combat COVID-19: A Comprehensive Review

Latif¹,

Usman²,

Manzoor³

et al. 2020

Preprint

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<div>COVID-19, an infectious disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organisation (WHO) in March 2020. At the time of writing, more than 2.8 million people have tested positive. Infections have been growing exponentially and tremendous efforts are being made to fight the disease. In this paper, we attempt to systematise ongoing data science activities in this area. As well as reviewing the rapidly growing body of recent research, we survey public datasets and repositories that can be used for further work to track COVID-19 spread and mitigation strategies.</div><div>As part of this, we present a bibliometric analysis of the papers produced in this short span of time. Finally, building on these insights, we highlight common challenges and pitfalls observed across the surveyed works.</div>

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“…Our understanding of COVID-19's long-term impact is still limited. Contributing serious insights will require a mix of domain expertise from multiple fields, and there is already a push for better international collaboration and tracking of COVID-19 [214]. For example, the use of black-box models might yield a superficially practical solution, but could be useless without the involvement of (international) medical and biotechnology expert interpretations.…”

Section: The Need For Multidisciplinary Collaborationmentioning

confidence: 99%