COVID-19 CONTROLLED BY MOBILE TECHNOLOGY RECENT RESEARCH

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COVID-19 CONTROLLED BY MOBILE TECHNOLOGY RECENT RESEARCH.

The exponentially increasing number of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections has led to “an urgent need to expand public health activities to elucidate the epidemiology of the novel virus and characterize its potential impact” . Understanding risk factors for infection and predictors of subsequent outcomes is critical to gain control of the coronavirus disease 2019 (COVID-19) pandemic . However, the speed at which the pandemic is unfolding poses an unprecedented challenge to collecting exposure data characterizing the full breadth of disease severity, hampering efforts to disseminate accurate information in a timely manner to impact public health planning and clinical management. Thus, there is an urgent need for an adaptable real-time data-capture platform to rapidly and prospectively collect actionable high-quality data that encompasses the spectrum of subclinical and acute presentations while identifying disparities in diagnosis, treatment, and clinical outcomes. Addressing this priority will allow for more accurate estimates of disease incidence, inform risk mitigation strategies, more effectively allocate still-scarce testing resources, and allow for appropriate quarantine and treatment of those afflicted.

An evolving body of literature suggests COVID-19 incidence and outcomes vary according to age, sex, race/ethnicity, and underlying health status, with inconsistent evidence suggesting that commonly used medications such as angiotensin-converting enzyme (ACE) inhibitors, thiazolidinediones (TZD), and ibuprofen may alter the natural disease course . Further, symptoms of COVID-19 vary widely, with fever and dry cough reportedly the most prevalent, though numerous investigations have demonstrated that asymptomatic carriage is a significant determinant of community spread .In addition, the full spectrum of clinical presentation is still being characterized, which may significantly differ across patient subgroups, as evidenced by recent advisories by the American Gastroenterological Association (AGA) and the American Academy of Otolaryngology - Head and Neck Surgery (AAO-HNS), and British Geriatric Society (BGS) on the potential importance of previously underappreciated gastrointestinal symptoms (e.g., nausea, anorexia, and diarrhea) or loss of taste and/or smell associated with COVID-19 infection, as well as common geriatric syndromes (e.g., falls and delirium). The pandemic has dramatically outpaced our collective efforts to fully characterize who is most at-risk or may suffer the most serious sequelae of infection.

Mobile phone applications or web-based tools facilitate self-guided collection of population-level data at scale , the results of which can then be rapidly redeployed to inform participants of urgent health information . Both are particularly advantageous when many Americans are advised to physically distance .Such digital tools have already been applied in more controlled research settings which benefit from greater lead time for field testing, question curation, and recruitment. Although an increasing number of digital collection tools for COVID-19 are being developed and launched in the U.S. and abroad (see http://mhealth-hub.org/mhealth-solutions-against-covid-19 for a continuously updated resource list from the European Union and WHO), including some in partnership with government health agencies such as the Centers for Disease Control and Prevention (CDC), most applications have largely been configured to offer a single assessment of symptoms to tailor semi-personalized recommendations for further evaluation. Infectious disease surveillance web-based tools (e.g., http://flunearyou.org) have been rapidly adapted for COVID-19-specific collection (e.g., http://covidnearyou.org). Alternatively, others have developed web portals for researchers to report patient-level information on behalf of participants already enrolled in clinical registries (e.g., ccc19.org). Integration with approaches that utilize remote data capture (e.g., wearables or symptom checkers such as real-time reporting thermometers) are also being considered. Although each of these approaches offer critical public health insights, they are often not tailored for the type of scalable longitudinal data capture that epidemiologists need to perform comprehensive, well-powered investigations.

To meet this challenge, we established a multinational collaboration, the COronavirus Pandemic Epidemiology (COPE) Consortium, comprised of leading investigators from several large clinical and epidemiological cohort studies. COPE brings together a multidisciplinary team of scientists with expertise in big data research and translational epidemiology to interrogate the COVID-19 pandemic in the largest and most diverse patient population assembled to-date. Several large cohorts have already agreed to join these efforts, including the Nurses’ Health Study (NHS), NHSII, NHS3, the Growing Up Today Study (GUTS), the Health Professionals Follow-Up Study (HPFS), TwinsUK, American Cancer Society Cancer Prevention Study 3 (CPS-3), the Multiethnic Cohort Study, the California Teachers Study (CTS), the Black Women’s Health Study (BWHS), the Sister Study, Aspirin in Reducing Events in the Elderly (ASPREE), the Stanford Nutrition Studies, the Gulf Long-term Follow-up (GuLF) Study, the Agricultural Health Study, the NIEHS Environmental Polymorphisms Registry, and the Predicting Progression of Developing Myeloma in a High-Risk Screened Population (PROMISE) and Precursor Crowdsourcing (PCROWD) Studies. To aid in our data harmonization efforts in the US, we co-developed the COVID Symptom Tracker mobile app in collaboration with in-kind contributions from Zoe Global Ltd, a digital healthcare company, and academic scientists from Massachusetts General Hospital and King’s College London. By leveraging the established digital backbone of an application used for personal nutrition studies, the COVID Symptom Tracker was launched in the UK on March 24, 2020, and became available in the U.S. on March 29, 2020 (https://covid.joinzoe.com/us). The COPE Consortium is committed to the shared international pursuit of combating COVID-19 and has worked with scientific collaborators and thought leaders in real-time epidemiology to prioritize data harmonization and sharing as a part of the Coronavirus Census Collective .

The COVID Symptom Tracker enables self-report of data related to COVID-19 exposure and infections.. On first use, the app queries location, age, and core health risk factors. Daily prompts query for updates on interim symptoms, health care visits, and COVID-19 testing results. In those self-quarantining or seeking health care, the level of intervention and related outcomes are collected. Individuals without obvious symptoms are also encouraged to use the app. Through pushed software updates, we can add or modify questions in real-time to test emerging hypotheses about COVID-19 symptoms and treatments. Importantly, participants enrolled in ongoing epidemiologic studies, clinical cohorts, or clinical trials can provide informed consent to link survey data collected through the app in a Health Insurance Portability and Accountability Act (HIPAA)- and General Data Protection Regulation (GDPR)-compliant manner to their pre-existing study cohort data and any relevant biospecimens. A specific module is also provided for participants who identify as healthcare workers to determine the intensity and type of their direct patient care experiences, the availability and use of personal protective equipment (PPE), and work-related stress and anxiety.

Through rapid deployment of this tool, we can gain critical insights into population dynamics of the disease  By collecting participant-reported geospatial data, highlighted as a critical need for pandemic epidemiologic researches .we can rapidly identify populations with highly prevalent symptoms that may emerge as hot spots for outbreaks. An early snapshot of the first 1.6 million users in the UK over the first five days of use confirms the variability in symptoms reported across suspected COVID-19 cases and is useful for generating and testing broader hypotheses. At the time, users were a mean age of 41 with a range from 18 to 90 years, with 75% female. Graphic visualization of initial results ,demonstrates that among those reporting symptoms by March 27, 2020 (n = 265,851) the most common symptoms were fatigue and cough, followed by diarrhea, fever, and anosmia. Shortness of breath was relatively rarely reported. Only 0.4% (n = 1,176) of individuals reporting possible COVID-19 symptoms reported receiving a qPCR test for COVID-19 Comparing users with symptoms who reported testing within the initial launch period generated several hypotheses for future study using the growing dataset. The frequency of cough and fatigue alone or in combination appeared to commonly lead to testing, but did not appear to be particularly sensitive for a positive test. Similarly, no individuals reporting diarrhea in the absence of other symptoms tested positive. Interestingly, more complex presentations with cough and/or fatigue and at least one additional symptom, including less commonly appreciated complaints such as diarrhea and anosmia, appeared to be enriched among those with positive test results compared to negative results. In particular, anosmia may be a more sensitive symptom as it was more common than fever in individuals who tested positive. Indeed in subsequent analyses with a larger sample set, we have shown that anosmia appears to be a strong predictor for COVID-19 . In contrast, fever alone was not particularly discriminatory; however, in combination with lesser appreciated symptoms, a greater frequency of positive tests was observed. These findings suggest that individuals with complex or multiple (3 or more) symptomatic presentation perhaps should be prioritized for testing. Concerningly, 20% of individuals reported complex symptoms (cough and/or fatigue plus at least one of anosmia, diarrhea, or fever) but had not yet received testing, representing a substantial population who appear to be at greater risk for the disease. Additional work is warranted to confirm if complex or multiple (3 or more) symptomatic cases may accurately predict COVID incidence.

Based on these initial findings, our team subsequently developed a weighted prediction model based on these symptoms trained on more than 2 million individuals using the app .Using this prediction model, we demonstrate the potential utility of the COVID Symptom Tracker to collect data not only for long-term studies, but also for immediate public health planning. In Southern Wales in the United Kingdom, users reported symptoms that predicted, five to seven days in advance, two spikes in the number of individuals reported by public health authorities to be confirmed with COVID. Conversely, a decline in reports of symptoms preceded a drop in confirmed cases by several days. These results demonstrate that this app prospectively captures the dynamics of COVID incidence days in advance of traditional measures, such as positive tests, hospitalizations, or mortality. We are currently planning additional studies using a broadly representative sample of individuals who will undergo uniform COVID-19 testing to further validate our approach to symptom-based modeling of incidence. These data demonstrate compelling evidence for the potential predictive power of our approach, which will improve as more data are collected to inform the model. Further, they highlight the potential utility of real-time symptom tracking to help guide allocation of resources for testing and treatment as well as recommendations for lockdown or easement in specific areas.

With additional data collection, we will also apply big-data approaches (e.g., machine learning) to identify novel patterns that emerge in dynamic settings of exposure, onset of symptoms, disease trajectory, and clinical outcomes. Our launch of the app within several large epidemiology cohorts that have previously gathered longitudinal data on lifestyle, diet and health factors and genetic information will allow investigation of a much broader range of putative risk factors for COVID-19 outcomes. With additional follow-up, we will also be uniquely positioned to investigate long-term outcomes of COVID-19, including mental health, disability, mortality, and financial outcomes. Mobile technology can also supplement recently launched clinical trials or biobanking protocols already embedded within clinical settings. In collaboration with the Stand Up to Cancer Foundation, we have also developed a strategy to track information among individuals living with cancer, including those enrolled in clinical trials. At the Massachusetts General Hospital and Brigham and Women’s Hospital, we are deploying the tool within several clinical studies, centralized biobanking efforts, and healthcare worker surveillance programs. Healthcare workers are a particularly vulnerable population to COVID-19’s effects beyond infection, including work hazards from PPE shortages, emotional stress, and absenteeism. Real-time data generation focused within these populations will be critical to optimally allocate resources to protect our healthcare workforce and assess their efficacy.

Our approach has limitations. We recognize that a smartphone application does not represent a random sampling of the population. However, this is an inherent limitation of any epidemiologic study which relies on voluntary participation. However, our approach has the benefit of allowing rapid deployment across a large cross-section of the population during an unprecedented health crisis. With time and continued use, the large number of participants will include a sufficient number of users within key subgroups that will allow for adjustment for potential sources of confounding. By engaging cohorts with underrepresented populations, such as the BWHS in the U.S., we also hope to leverage existing investigator-participant relationships to encourage enrollment of individuals who are traditionally more challenging to recruit. Moreover, by encouraging longitudinal, prospective data collection, we can capture associations based on within-person variation over time, a significant advantage over repeated cross-sectional surveys that introduce significant between-person variation. In the near future, we hope to release our app as fair-use open source software to facilitate translation and development in other regions. We have begun working with colleagues in Canada, Australia, and Sweden to implement this tool within their countries. We have also developed a practical toolkit for clinical researchers to facilitate local Institutional Review Board (IRB) and regulatory approval to facilitate deployment within research studies (www.monganinstitute.org/cope-consortium). This toolkit includes full detail of the questions, consent documents, privacy policies, and terms of use for the mobile app. With broader implementation, data generated from the COVID Symptom Tracker app have become increasingly linked to the public health response within the National Health Service in the UK. The app is endorsed by the Welsh Government, NHS Wales, the Scottish Government and NHS Scotland. Our scientific team update the UK Chief Scientific Officer daily. We are working to develop a similar approach in the US. However, the lack of a national healthcare system has required a strategy focused on engaging local public health leaders. For example, we have partnered with the University of Texas School of Public Health to conduct state-wide surveillance to support public health decision making, especially as their state government begins softening mitigation strategies.