Rapid implementation of mobile technology for real-time epidemiology of COVID-19

David A Drew, Long H Nguyen, Claire J Steves, Cristina Menni, Maxim Freydin, Thomas Varsavsky, Carole H Sudre, M Jorge Cardoso, Sebastien Ourselin, Jonathan Wolf, Tim D Spector, Andrew T Chan, COPE Consortium, Andrew T Chan, David A Drew, Long H Nguyen, Amit D Joshi, Chuan-Guo Guo, Wenjie Ma, Chun-Han Lo, Raaj S Mehta, Sohee Kwon, Daniel R Sikavi, Marina V Magicheva-Gupta, Zahra S Fatehi, Jacqueline J Flynn, Brianna M Leonardo, Christine M Albert, Gabriella Andreotti, Laura E Beane-Freeman, Bijal A Balasubramanian, John S Brownstein, Fiona Bruinsma, Annie N Cowan, Anusila Deka, Michael E Ernst, Jane C Figueiredo, Paul W Franks, Christopher D Gardner, Irene M Ghobrial, Christopher A Haiman, Janet E Hall, Sandra L Deming-Halverson, Brenda Kirpach, James V Lacey Jr, Loic Le Marchand, Catherine R Marinac, Maria Elena Martinez, Roger L Milne, Anne M Murray, Denis Nash, Julie R Palmer, Alpa V Patel, Lynn Rosenberg, Dale P Sandler, Shreela V Sharma, Shepherd H Schurman, Lynne R Wilkens, Jorge E Chavarro, A Heather Eliassen, Jamie E Hart, Jae Hee Kang, Karestan C Koenen, Laura D Kubzansky, Lorelei A Mucci, Sebastien Ourselin, Janet W Rich-Edwards, Mingyang Song, Meir J Stampfer, Claire J Steves, Walter C Willett, Jonathan Wolf, Tim Spector, David A Drew, Long H Nguyen, Claire J Steves, Cristina Menni, Maxim Freydin, Thomas Varsavsky, Carole H Sudre, M Jorge Cardoso, Sebastien Ourselin, Jonathan Wolf, Tim D Spector, Andrew T Chan, COPE Consortium, Andrew T Chan, David A Drew, Long H Nguyen, Amit D Joshi, Chuan-Guo Guo, Wenjie Ma, Chun-Han Lo, Raaj S Mehta, Sohee Kwon, Daniel R Sikavi, Marina V Magicheva-Gupta, Zahra S Fatehi, Jacqueline J Flynn, Brianna M Leonardo, Christine M Albert, Gabriella Andreotti, Laura E Beane-Freeman, Bijal A Balasubramanian, John S Brownstein, Fiona Bruinsma, Annie N Cowan, Anusila Deka, Michael E Ernst, Jane C Figueiredo, Paul W Franks, Christopher D Gardner, Irene M Ghobrial, Christopher A Haiman, Janet E Hall, Sandra L Deming-Halverson, Brenda Kirpach, James V Lacey Jr, Loic Le Marchand, Catherine R Marinac, Maria Elena Martinez, Roger L Milne, Anne M Murray, Denis Nash, Julie R Palmer, Alpa V Patel, Lynn Rosenberg, Dale P Sandler, Shreela V Sharma, Shepherd H Schurman, Lynne R Wilkens, Jorge E Chavarro, A Heather Eliassen, Jamie E Hart, Jae Hee Kang, Karestan C Koenen, Laura D Kubzansky, Lorelei A Mucci, Sebastien Ourselin, Janet W Rich-Edwards, Mingyang Song, Meir J Stampfer, Claire J Steves, Walter C Willett, Jonathan Wolf, Tim Spector

Abstract

The rapid pace of the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents challenges to the robust collection of population-scale data to address this global health crisis. We established the COronavirus Pandemic Epidemiology (COPE) Consortium to unite scientists with expertise in big data research and epidemiology to develop the COVID Symptom Study, previously known as the COVID Symptom Tracker, mobile application. This application-which offers data on risk factors, predictive symptoms, clinical outcomes, and geographical hotspots-was launched in the United Kingdom on 24 March 2020 and the United States on 29 March 2020 and has garnered more than 2.8 million users as of 2 May 2020. Our initiative offers a proof of concept for the repurposing of existing approaches to enable rapidly scalable epidemiologic data collection and analysis, which is critical for a data-driven response to this public health challenge.

Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Figures

Fig. 1. Schematic of the participant workflow.
Fig. 1. Schematic of the participant workflow.
After downloading the COVID Symptom Study app and providing consent, users are prompted to enter baseline demographic and clinical information and are serially queried about new or ongoing symptoms, testing results, and extent of isolation. Health care workers offer additional information about the intensity of their patient interactions, potential exposure to infected patients, and use of PPE. With informed consent, users also participating in a variety of ongoing cohorts or clinical trials (Nurses’ Health Study, TwinsUK, and others) have the option of linking COVID Symptom Study information to their extant research data.
Fig. 2. COVID Symptom Study use, reported…
Fig. 2. COVID Symptom Study use, reported symptoms, and testing results according to geographic location in the United Kingdom.
Between 24 and 29 March 2020, more than 1.6 million unique individuals downloaded the application and shared clinical and demographic information, as well as daily symptoms and high-intensity occupational exposures (blue map). Population density of those presenting with any symptoms (left purple map) varied according to region, with widespread reports of fatigue, cough, and diarrhea, followed by anosmia and, relatively infrequently, fever (inset). Examination of individuals who reported complex symptoms (right purple map), defined as having cough or fever and at least one other symptom (diarrhea, anosmia, or fever), reveals areas that potentially need more testing. For the subset of the population that received a COVID-19 test (black map), areas with larger proportions of positive tests (orange map) appear to coincide with areas in which high proportions of the population reported complex symptoms. By contrast, some areas with low prevalence of complex symptoms have received higher rates of testing and, consequently, more negative tests (green map). This example of real-time visualization of data captured by the COVID Symptom Study may help public health and government officials reallocate resources, identify areas with unmet testing needs, and detect emerging hotspots.
Fig. 3. Symptoms reported through the COVID…
Fig. 3. Symptoms reported through the COVID Symptom Study app.
By 27 March 2020, 265,851 individuals in the United Kingdom reported any symptom potentially associated with COVID-19 (top). Participants provided data on whether they were tested for COVID-19, as well as the test result. 1176 individuals reported having received a COVID-19 test (0.4% of those with symptoms). Symptom frequencies among those who tested positive (middle; n = 340) versus negative (bottom; n = 836) are shown.
Fig. 4. Predicting COVID-19 cases on the…
Fig. 4. Predicting COVID-19 cases on the basis of real-time symptom reporting in Wales, United Kingdom.
This time series (bar graph) displays the number of new confirmed cases (gray bars) reported by the Public Health Wales NHS Trust between 31 March 2020 and 20 April 2020. After 2 April, case numbers appear to have declined through 5 April. However, our symptom-based prediction model (18), developed from symptom reports from untested users of the COVID Symptom Study app, showed a high proportion of predicted COVID-19 cases in southern Wales on 1 April [dark red areas in (A)]. Six days later, Welsh health authorities reported a subsequent peak in cases over a 4-day period (6 to 9 April), driven primarily by these southern regions (colored bars). By 10 April, new confirmed cases across Wales declined. However, on the basis of reported symptoms (B), regions in South Wales still had high predicted levels of COVID-19, which became apparent as a second spike in confirmed cases between 15 and 16 April. As of 20 April (C), predicted COVID-19 prevalence across Wales according to symptom reporting appears to be low, which corresponds to a flattening of the cumulative incidence curve. However, several regions in southern Wales still have relatively high reports of symptoms and appear at risk for subsequent cases of COVID-19. Black dots on the maps represent the relative proportion of positive tests reported by health authorities across Wales that day by region. The prediction mapping included data from 1,339,670 users of the COVID Symptom Study on 1 April; 998,244 users on 10 April; and 1,234,918 users on 20 April. Public Health Wales NHS Trust data were current as of 21 April 2020 at 13:00 local time, taken from the “Rapid COVID-19 Virology - Public” dashboard (accessed via https://phw.nhs.wales/), and downloaded on 22 April 2020 at 12:30 p.m. Eastern Standard Time.

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Source: PubMed

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