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Smartphone apps in the COVID-19 pandemic


  • Johnson, N. P. & Mueller, J. Updating the accounts: global mortality of the 1918–1920 “Spanish” influenza pandemic. Bull. Hist. Med. 76, 105–115 (2002).

  • Dong, E. Du, H. & and Gardner, L. An interactive web-based dashboard to track COVID-19 in real time. Lancet 20, 533–534 (2020).

  • Pei, S., Yamana, T. K., Kandula, S., Galanti, M. & Shaman, J. Burden and characteristics of COVID-19 in the United States during 2020. Nature 598, 338–341 (2021).

    PubMed 
    Article 
    CAS 

    Google Scholar 

  • Kim, Y. C., Dema, B. & Reyes-Sandoval, A. COVID-19 vaccines: breaking record times to first-in-human trials. NPJ Vaccines 5, 34 (2020).

  • Jester, B. J., Uyeki, T. M., Patel, A., Koonin, L. & Jernigan, D. B. 100 Years of medical countermeasures and pandemic influenza preparedness. Am. J. Public Health 108, 1469–1472 (2018).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Fineberg, H. V. Pandemic preparedness and response—lessons from the H1N1 influenza of 2009. N. Engl. J. Med. 370, 1335–1342 (2014).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Bedford, J. et al. A new twenty-first century science for effective epidemic response. Nature 575, 130–136 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Whitelaw, S., Mamas, M. A., Topol, E. & Van Spall, H. G. Applications of digital technology in COVID-19 pandemic planning and response. Lancet Digit. Health 2, e435–e440 (2020).

  • Kim, J., Campbell, A. S., de Ávila, B. E.-F. & Wang, J. Wearable biosensors for healthcare monitoring. Nat. Biotechnol. 37, 389–406 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Tromberg, B. J. et al. Rapid scaling up of Covid-19 diagnostic testing in the United States—the NIH RADx initiative. N. Engl. J. Med. 383, 1071–1077 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Kliff, S. & Sanger-Katz, M. Bottleneck for US coronavirus response: the fax machine. The New York Times (13 July 2020).

  • Mahindra, A. et al. Paper card-based vs application-based vaccine credentials: a comparison. Preprint at https://doi.org/10.48550/arXiv.2102.04512 (2021).

  • Bates, M. Tracking disease: digital epidemiology offers new promise in predicting outbreaks. IEEE Pulse 8, 18–22 (2017).

    PubMed 
    Article 

    Google Scholar 

  • Brown, B., Chui, M. & Manyika, J. Are you ready for the era of ‘big data’. McKinsey and Company https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/are-you-ready-for-the-era-of-big-data (2011).

  • Mackert, M., Mabry-Flynn, A., Champlin, S., Donovan, E. E. & Pounders, K. Health literacy and health information technology adoption: the potential for a new digital divide. J. Med. Internet Res. 18, e264 (2016).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Bol, N., Helberger, N. & Weert, J. C. Differences in mobile health app use: a source of new digital inequalities? Inf. Soc. 34, 183–193 (2018).

    Article 

    Google Scholar 

  • Brewer, L. C. et al. Back to the future: achieving health equity through health informatics and digital health. JMIR mHealth uHealth 8, e14512 (2020).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Price, W. N. & Cohen, I. G. Privacy in the age of medical big data. Nat. Med. 25, 37–43 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Landau, S. Digital exposure tools: design for privacy, efficacy, and equity apps can cut transmission of SARS-CoV-2—but how do we ensure that they don’t exacerbate public health inequities? Science 373, 1202–1204 (2021).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Wang, D. et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. JAMA 323, 1061–1069 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Guan, W.-j et al. Clinical characteristics of coronavirus disease 2019 in China. N. Engl. J. Med. 382, 1708–1720 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Radin, J. M., Wineinger, N. E., Topol, E. J. & Steinhubl, S. R. Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study. Lancet Digit. Health 2, e85–e93 (2020).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Quer, G. et al. Wearable sensor data and self-reported symptoms for COVID-19 detection. Nat. Med. 27, 73–77 (2021).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Ferretti, L. et al. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science 368, eabb6936 (2020).

  • Yang, S., Santillana, N. & Kou, S. C. Accurate estimation of influenza epidemics using Google search data via ARGO. Proc. Natl Acad. Sci. USA 112, 14463–14478 (2015).

    Google Scholar 

  • Meyers, D. J. et al. Combining healthcare-based and participatory approaches to surveillance: trends in diarrheal and respiratory conditions collected by a mobile phone system by community health workers in rural Nepal. PLoS ONE 11, e0152738 (2016).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • Smolinski, M. S. et al. Flu near you: crowdsourced symptom reporting spanning 2 influenza seasons. Am. J. Public Health 105, 2124–2130 (2015).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Guerrisi, C. et al. Participatory syndromic surveillance of influenza in Europe. J. Infect. Dis. 214, S386–S392 (2016).

    PubMed 
    Article 

    Google Scholar 

  • Wójcik, O. P., Brownstein, J. S., Chunara, R. & Johansson, M. A. Public health for the people: participatory infectious disease surveillance in the digital age. Emerg. Themes Epidemiol. 11, 7 (2014).

  • Leal-Neto, O., Santos, F., Lee, J. Y., Albuquerque, J. & Souza, W. V. Prioritizing COVID-19 tests based on participatory surveillance and spatial scanning. Int. J. Med. Inform. 143, 104263 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Leal-Neto, O. et al. Digital SARS-CoV-2 detection among hospital employees: participatory surveillance study. JMIR Public Health Surveill. 7, e33576 (2021).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Sudre, C. H. et al. 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. Lancet Digit. Health 3, e577–e586 (2021).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Cook, S., Conrad, C., Fowlkes, A. L. & Mohebbi, M. H. Assessing Google flu trends performance in the United States during the 2009 influenza virus A (H1N1) pandemic. PLoS ONE 6, e23610 (2011).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Freifeld, C. C., Mandl, K. D., Reis, B. Y. & Brownstein, J. S. HealthMap: global infectious disease monitoring through automated classification and visualization of Internet media reports. J. Am. Med. Inform. Assoc. 15, 150–157 (2008).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Hossain, N. & Househ, M. S. Using HealthMap to analyse Middle East respiratory syndrome (MERS) data. Stud. Health Technol. Inform. 226, 213–216 (2016).

  • Chamberlain, S. D. et al. Real-time detection of COVID-19 epicenters within the United States using a network of smart thermometers. Preprint at medRxiv https://doi.org/10.1101/2020.04.06.20039909 (2020).

  • Miller, A. C., Peterson, R. A., Singh, I., Pilewski, S. & Polgreen, P. M. Improving state-level influenza surveillance byincorporating real-time smartphone-connected thermometer readings across different geographic domains. Open Forum Infect. Dis. 6, ofz455 (2019).

    Article 

    Google Scholar 

  • Miller, A. C., Singh, I., Koehler, E. & Polgreen, P. M. A smartphone-driven thermometer application for real-time population-and individual-level influenza surveillance. Clin. Infect. Dis. 67, 388–397 (2018).

    PubMed 
    Article 

    Google Scholar 

  • Brueck, H. Florida is looking like the next major US hotspot of COVID-19, according to a strikingly accurate thermometer map that shows where cases may surge next. Business Insider https://www.businessinsider.com/kinsa-thermometer-readings-could-track-covid-19-across-us-2020-3?r=US&IR=T (2020).

  • Gangavarapu, K. et al. Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations. Preprint at medRxiv https://doi.org/10.1101/2022.01.27.22269965 (2022).

  • Lazer, D., Kennedy, R., King, G. & Vespignani, A. The parable of Google Flu: traps in big data analysis. Science 343, 1203–1205 (2014).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • SAFER-COVID: A safe return to daily activities. CareEvolution https://careevolution.com/mydatahelps-research-wellness-platform/safer-covid/ (2020).

  • Liang, F. COVID-19 and health code: how digital platforms tackle the pandemic in China. Soc. Media Soc. 6, 2056305120947657 (2020).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Vespignani, A. et al. Modelling Covid-19. Nat. Rev. Phys. 2, 279–281 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Behnam, M., Dey, A., Gambell, T. & Talwar, V. COVID-19: overcoming supply shortages for diagnostic testing. McKinsey and Company https://www.mckinsey.com/industries/life-sciences/our-insights/covid-19-overcoming-supply-shortages-for-diagnostic-testing (2020).

  • Loclainn, M.N. et al. Key predictors of attending hospital with COVID19: an association study from the COVID symptom Tracker APP in 2,618,948 individual. Preprint at medRxiv https://doi.org/10.1101/2020.04.25.20079251 (2020).

  • Menni, C. et al. Real-time tracking of self-reported symptoms to predict potential COVID-19. Nat. Med. 26, 1037–1040 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • COVID-19 App (Apple, 2020).

  • Li, X. et al. Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS Biol. 15, e2001402 (2017).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • Scripps Research Translational Institute. DETECT https://detect.scripps.edu (2020).

  • Gadaleta, M. et al. Passive detection of COVID-19 with wearable sensors and explainable machine learning algorithms. NPJ Digit. Med. 4, 166 (2021).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Radin, J. M. et al. Assessment of prolonged physiological and behavioral changes associated with COVID-19 infection. JAMA Netw. Open 4, e2115959 (2021).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Quer, G. et al. Inter-individual variation in objective measure of reactogenicity following COVID-19 vaccination via smartwatches and fitness bands. NPJ Dig. Med. 5, 49 (2022).

    Article 

    Google Scholar 

  • Stanford Healthcare Innovation Lab. Infectious Disease and COVID-19 Wearables Study https://nnovations.stanford.edu/wearables (2019).

  • Natarajan, A., Su, H.-W. & Heneghan, C. Assessment of physiological signs associated with COVID-19 measured using wearable devices. NPJ Digit. Med. 3, 156 (2020).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Mishra, T. et al. Pre-symptomatic detection of COVID-19 from smartwatch data. Nat. Biomed. Eng. 4, 1208–1220 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Alavi, A. et al. Real-time alerting system for COVID-19 and other stress events using wearable data. Nat. Med. 28, 175–184 (2022).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Robert Koch Institut. Corona Datenspende https://corona-datnspende.de/science/en (2020).

  • Miller, D. J. et al. Analyzing changes in respiratory rate to predict the risk of COVID-19 infection. PLoS ONE 15, e0243693 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Shapiro, A. et al. Characterizing COVID-19 and influenza illnesses in the real world via person-generated health data. Patterns 2, 100188 (2021).

    PubMed 
    Article 

    Google Scholar 

  • Brakenhoff, T. B. et al. A prospective, randomized, single-blinded, crossover trial to investigate the effect of a wearable device in addition to a daily symptom diary for the remote early detection of SARS-CoV-2 infections (COVID-RED): a structured summary of a study protocol for a randomized controlled trial. Trials 22, 412 (2021).

  • Martinez‐Jimenez, M. A. et al. Diagnostic accuracy of infrared thermal imaging for detecting COVID‐19 infection in minimally symptomatic patients. Eur. J. Clin. Invest. 51, e13474 (2021).

    PubMed 
    Article 
    CAS 

    Google Scholar 

  • Nguyen, P. Q. et al. Wearable materials with embedded synthetic biology sensors for biomolecule detection. Nat. Biotechnol. 39, 1366–1374 (2021).

  • Kahn, J. P. Digital Contact Tracing for Pandemic Response: Ethics and Governance Guidance (Johns Hopkins Univ. Press, 2020).

  • Budd, J. et al. Digital technologies in the public-health response to COVID-19. Nat. Med. 26, 1183–1192 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Wu, J. T., Leung, K. & Leung, G. M. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet 395, 689–697 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Park, S., Choi, G. J. & Ko, H. Information technology–based tracing strategy in response to COVID-19 in South Korea—privacy controversies. JAMA 323, 2129–2130 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Wang, C. J., Ng, C. Y. & Brook, R. H. Response to COVID-19 in Taiwan: big data analytics, new technology, and proactive testing. JAMA 323, 1341–1342 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Colizza, V. et al. Time to evaluate COVID-19 contact-tracing apps. Nat. Med. 27, 361–362 (2021).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Apple. Apple and Google partner on COVID-19 contact tracing technology. Apple https://www.apple.com/uk/newsroom/2020/04/apple-and-google-partner-on-covid-19-contact-tracing-technology/ (2020).

  • Arevalo, F. N. Decoding the public interest of Aarogya Setu, contact tracing app for managing the COVID-19 pandemic in India. In Proc. 2020 IEEE International Symposium on Technology and Society (ISTAS) 508–512 (IEEE, 2020).

  • Aravindan, A. & Phartiyal, S. Bluetooth phone apps for tracking COVID-19 show modest early results. https://www.reuters.com/article/us-health-coronavirus-apps-idUSKCN2232A0 (2020).

  • Probyn, A. Coronavirus lockdowns could end in months if Australians are willing to have their movements monitored. ABC https://www.abc.net.au/news/2020-04-14/coronavirus-app-government-wants-australians-to-download/12148210 (2020).

  • Morley, J., Cowls, J., Taddeo, M. & Floridi, L. Ethical guidelines for COVID-19 tracing apps. Nature 582, 29–31 (2020).

  • Grande, D. et al. Consumer views on using digital data for COVID-19 control in the United States. JAMA Netw. Open 4, e2110918 (2021).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Bahrain, Kuwait and Norway contact tracing apps among most dangerous for privacy. Amnesty International https://www.amnesty.org/en/latest/news/2020/06/bahrain-kuwait-norway-contact-tracing-apps-danger-for-privacy/ (2020).

  • Hidayat-ur-Rehman, I., Ahmad, A., Ahmed, M. & Alam, A. Mobile applications to fight against COVID-19 pandemic: the case of Saudi Arabia. TEM J. 10, 69–77 (2021).

  • Wymant, C. et al. The epidemiological impact of the NHS COVID-19 App. Nature 594, 408–412 (2021).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Menges, D., Aschmann, H. E., Moser, A., Althaus, C. L. & Von Wyl, V. A data-driven simulation of the exposure notification cascade for digital contact tracing of SARS-CoV-2 in Zurich, Switzerland. JAMA Netw. Open 4, e218184 (2021).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Ladyzhets, B. We investigated whether digital contact tracing actually worked in the US. Technology Review https://www.technologyreview.com/2021/06/16/1026255/us-digital-contact-tracing-exposure-notification-analysis/ (2021).

  • Steinhauer, J. & Goodenough. A. Contact tracing is failing in many states. Here’s why. The New York Times https://www.nytimes.com/2020/07/31/health/covid-contact-tracing-tests.html (31 July 2020).

  • O’Neill, P. H. No, coronavirus apps don’t need 60% adoption to be effective. Technology Review https://www.technologyreview.com/2020/06/05/1002775/covid-apps-effective-at-less-than-60-percent-download/ (2020).

  • Rüdiger, S. et al. Predicting the SARS-CoV-2 effective reproduction number using bulk contact data from mobile phones. Proc. Natl Acad. Sci. USA 118, e2026731118 (2021).

  • Krieg, S. J. et al. Data-driven testing program improves detection of COVID-19 cases and reduces community transmission. NPJ Digit. Med. 5, 17 (2022).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Sharma, T. & Bashir, M. Use of apps in the COVID-19 response and the loss of privacy protection. Nat. Med. 26, 1165–1167 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Gasser, U., Ienca, M., Scheibner, J., Sleigh, J. & Vayena, E. Digital tools against COVID-19: taxonomy, ethical challenges, and navigation aid. Lancet Digit. Health 2, e425–e434 (2020).

  • Ting, D. S. W., Carin, L., Dzau, V. & Wong, T. Y. Digital technology and COVID-19. Nat. Med. 26, 459–461 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Rimmer, A. Sixty seconds on… the pingdemic. BMJ 374, 1822 (2021).

  • Mina, M. J. & Andersen, K. G. COVID-19 testing: one size does not fit all. Science 371, 126–127 (2021).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Dror, A. A. et al. Vaccine hesitancy: the next challenge in the fight against COVID-19. Eur. J. Epidemiol. 35, 775–779 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Geneviève, L. D. et al. Participatory disease surveillance systems: ethical framework. J. Med. Internet Res. 21, e12273 (2019).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

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