Johnson, N. P. & Mueller, J. Updating the accounts: world 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 trace COVID-19 in actual time. Lancet 20, 533–534 (2020).
Pei, S., Yamana, T. Ok., Kandula, S., Galanti, M. & Shaman, J. Burden and traits of COVID-19 in america throughout 2020. Nature 598, 338–341 (2021).
Google Scholar
Kim, Y. C., Dema, B. & Reyes-Sandoval, A. COVID-19 vaccines: breaking document instances 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 Well being 108, 1469–1472 (2018).
Google Scholar
Fineberg, H. V. Pandemic preparedness and response—classes from the H1N1 influenza of 2009. N. Engl. J. Med. 370, 1335–1342 (2014).
Google Scholar
Bedford, J. et al. A brand new twenty-first century science for efficient epidemic response. Nature 575, 130–136 (2019).
Google Scholar
Whitelaw, S., Mamas, M. A., Topol, E. & Van Spall, H. G. Purposes of digital know-how in COVID-19 pandemic planning and response. Lancet Digit. Well being 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).
Google Scholar
Tromberg, B. J. et al. Fast scaling up of Covid-19 diagnostic testing in america—the NIH RADx initiative. N. Engl. J. Med. 383, 1071–1077 (2020).
Google Scholar
Kliff, S. & Sanger-Katz, M. Bottleneck for US coronavirus response: the fax machine. The New York Occasions (13 July 2020).
Mahindra, A. et al. Paper card-based vs application-based vaccine credentials: a comparability. Preprint at (2021).
Bates, M. Monitoring illness: digital epidemiology gives new promise in predicting outbreaks. IEEE Pulse 8, 18–22 (2017).
Google Scholar
Brown, B., Chui, M. & Manyika, J. Are you prepared for the period of ‘big data’. McKinsey and Firm (2011).
Mackert, M., Mabry-Flynn, A., Champlin, S., Donovan, E. E. & Pounders, Ok. Well being literacy and well being info know-how adoption: the potential for a brand new digital divide. J. Med. Web Res. 18, e264 (2016).
Google Scholar
Bol, N., Helberger, N. & Weert, J. C. Variations in cellular well being app use: a supply of recent digital inequalities? Inf. Soc. 34, 183–193 (2018).
Google Scholar
Brewer, L. C. et al. Again to the long run: attaining well being fairness via well being informatics and digital well being. JMIR mHealth uHealth 8, e14512 (2020).
Google Scholar
Worth, W. N. & Cohen, I. G. Privateness within the age of medical massive information. Nat. Med. 25, 37–43 (2019).
Google Scholar
Landau, S. Digital publicity instruments: design for privateness, efficacy, and fairness apps can lower transmission of SARS-CoV-2—however how can we be certain that they don’t exacerbate public well being inequities? Science 373, 1202–1204 (2021).
Google Scholar
Wang, D. et al. Scientific traits of 138 hospitalized sufferers with 2019 novel coronavirus–contaminated pneumonia in Wuhan, China. JAMA 323, 1061–1069 (2020).
Google Scholar
Guan, W.-j et al. Scientific traits of coronavirus illness 2019 in China. N. Engl. J. Med. 382, 1708–1720 (2020).
Google Scholar
Radin, J. M., Wineinger, N. E., Topol, E. J. & Steinhubl, S. R. Harnessing wearable gadget information to enhance state-level real-time surveillance of influenza-like sickness within the USA: a population-based research. Lancet Digit. Well being 2, e85–e93 (2020).
Google Scholar
Quer, G. et al. Wearable sensor information and self-reported signs for COVID-19 detection. Nat. Med. 27, 73–77 (2021).
Google Scholar
Ferretti, L. et al. Quantifying SARS-CoV-2 transmission suggests epidemic management with digital contact tracing. Science 368, eabb6936 (2020).
Yang, S., Santillana, N. & Kou, S. C. Correct estimation of influenza epidemics utilizing Google search information through ARGO. Proc. Natl Acad. Sci. USA 112, 14463–14478 (2015).
Meyers, D. J. et al. Combining healthcare-based and participatory approaches to surveillance: developments in diarrheal and respiratory circumstances collected by a cell phone system by neighborhood well being staff in rural Nepal. PLoS ONE 11, e0152738 (2016).
Google Scholar
Smolinski, M. S. et al. Flu close to you: crowdsourced symptom reporting spanning 2 influenza seasons. Am. J. Public Well being 105, 2124–2130 (2015).
Google Scholar
Guerrisi, C. et al. Participatory syndromic surveillance of influenza in Europe. J. Infect. Dis. 214, S386–S392 (2016).
Google Scholar
Wójcik, O. P., Brownstein, J. S., Chunara, R. & Johansson, M. A. Public well being for the folks: participatory infectious illness surveillance within 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 exams based mostly on participatory surveillance and spatial scanning. Int. J. Med. Inform. 143, 104263 (2020).
Google Scholar
Leal-Neto, O. et al. Digital SARS-CoV-2 detection amongst hospital staff: participatory surveillance research. JMIR Public Well being Surveill. 7, e33576 (2021).
Google Scholar
Sudre, C. H. et al. Anosmia, ageusia, and different COVID-19-like signs in affiliation with a constructive SARS-CoV-2 take a look at, throughout six nationwide digital surveillance platforms: an observational research. Lancet Digit. Well being 3, e577–e586 (2021).
Google Scholar
Cook dinner, S., Conrad, C., Fowlkes, A. L. & Mohebbi, M. H. Assessing Google flu developments efficiency in america in the course of the 2009 influenza virus A (H1N1) pandemic. PLoS ONE 6, e23610 (2011).
Google Scholar
Freifeld, C. C., Mandl, Ok. D., Reis, B. Y. & Brownstein, J. S. HealthMap: world infectious illness monitoring via automated classification and visualization of Web media experiences. J. Am. Med. Inform. Assoc. 15, 150–157 (2008).
Google Scholar
Hossain, N. & Househ, M. S. Utilizing HealthMap to analyse Center East respiratory syndrome (MERS) information. Stud. Well being Technol. Inform. 226, 213–216 (2016).
Chamberlain, S. D. et al. Actual-time detection of COVID-19 epicenters inside america utilizing a community of good thermometers. Preprint at medRxiv (2020).
Miller, A. C., Peterson, R. A., Singh, I., Pilewski, S. & Polgreen, P. M. Bettering state-level influenza surveillance byincorporating real-time smartphone-connected thermometer readings throughout totally different geographic domains. Open Discussion board Infect. Dis. 6, ofz455 (2019).
Google Scholar
Miller, A. C., Singh, I., Koehler, E. & Polgreen, P. M. A smartphone-driven thermometer software for real-time population-and individual-level influenza surveillance. Clin. Infect. Dis. 67, 388–397 (2018).
Google Scholar
Brueck, H. Florida is wanting like the subsequent main US hotspot of COVID-19, in line with a strikingly correct thermometer map that reveals the place circumstances could surge subsequent. Enterprise Insider (2020).
Gangavarapu, Ok. et al. Outbreak.information genomic experiences: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations. Preprint at medRxiv (2022).
Lazer, D., Kennedy, R., King, G. & Vespignani, A. The parable of Google Flu: traps in massive information evaluation. Science 343, 1203–1205 (2014).
Google Scholar
SAFER-COVID: A protected return to day by day actions. CareEvolution (2020).
Liang, F. COVID-19 and well being code: how digital platforms sort out the pandemic in China. Soc. Media Soc. 6, 2056305120947657 (2020).
Google Scholar
Vespignani, A. et al. Modelling Covid-19. Nat. Rev. Phys. 2, 279–281 (2020).
Google Scholar
Behnam, M., Dey, A., Gambell, T. & Talwar, V. COVID-19: overcoming provide shortages for diagnostic testing. McKinsey and Firm (2020).
Loclainn, M.N. et al. Key predictors of attending hospital with COVID19: an affiliation research from the COVID symptom Tracker APP in 2,618,948 particular person. Preprint at medRxiv (2020).
Menni, C. et al. Actual-time monitoring of self-reported signs to foretell potential COVID-19. Nat. Med. 26, 1037–1040 (2020).
Google Scholar
COVID-19 App (Apple, 2020).
Li, X. et al. Digital well being: monitoring physiomes and exercise utilizing wearable biosensors reveals helpful health-related info. PLoS Biol. 15, e2001402 (2017).
Google Scholar
Scripps Analysis Translational Institute. DETECT (2020).
Gadaleta, M. et al. Passive detection of COVID-19 with wearable sensors and explainable machine studying algorithms. NPJ Digit. Med. 4, 166 (2021).
Google Scholar
Radin, J. M. et al. Evaluation of extended physiological and behavioral adjustments related to COVID-19 an infection. JAMA Netw. Open 4, e2115959 (2021).
Google Scholar
Quer, G. et al. Inter-individual variation in goal measure of reactogenicity following COVID-19 vaccination through smartwatches and health bands. NPJ Dig. Med. 5, 49 (2022).
Google Scholar
Stanford Healthcare Innovation Lab. Infectious Illness and COVID-19 Wearables Research (2019).
Natarajan, A., Su, H.-W. & Heneghan, C. Evaluation of physiological indicators related to COVID-19 measured utilizing wearable units. NPJ Digit. Med. 3, 156 (2020).
Google Scholar
Mishra, T. et al. Pre-symptomatic detection of COVID-19 from smartwatch information. Nat. Biomed. Eng. 4, 1208–1220 (2020).
Google Scholar
Alavi, A. et al. Actual-time alerting system for COVID-19 and different stress occasions utilizing wearable information. Nat. Med. 28, 175–184 (2022).
Google Scholar
Robert Koch Institut. Corona Datenspende (2020).
Miller, D. J. et al. Analyzing adjustments in respiratory fee to foretell the chance of COVID-19 an infection. PLoS ONE 15, e0243693 (2020).
Google Scholar
Shapiro, A. et al. Characterizing COVID-19 and influenza sicknesses in the true world through person-generated well being information. Patterns 2, 100188 (2021).
Google Scholar
Brakenhoff, T. B. et al. A potential, randomized, single-blinded, crossover trial to research the impact of a wearable gadget along with a day by day symptom diary for the distant early detection of SARS-CoV-2 infections (COVID-RED): a structured abstract of a research protocol for a randomized managed trial. Trials 22, 412 (2021).
Martinez‐Jimenez, M. A. et al. Diagnostic accuracy of infrared thermal imaging for detecting COVID‐19 an infection in minimally symptomatic sufferers. Eur. J. Clin. Make investments. 51, e13474 (2021).
Google Scholar
Nguyen, P. Q. et al. Wearable supplies with embedded artificial biology sensors for biomolecule detection. Nat. Biotechnol. 39, 1366–1374 (2021).
Kahn, J. P. Digital Contact Tracing for Pandemic Response: Ethics and Governance Steerage (Johns Hopkins Univ. Press, 2020).
Budd, J. et al. Digital applied sciences within the public-health response to COVID-19. Nat. Med. 26, 1183–1192 (2020).
Google Scholar
Wu, J. T., Leung, Ok. & Leung, G. M. Nowcasting and forecasting the potential home and worldwide unfold of the 2019-nCoV outbreak originating in Wuhan, China: a modelling research. Lancet 395, 689–697 (2020).
Google Scholar
Park, S., Choi, G. J. & Ko, H. Info know-how–based mostly tracing technique in response to COVID-19 in South Korea—privateness controversies. JAMA 323, 2129–2130 (2020).
Google Scholar
Wang, C. J., Ng, C. Y. & Brook, R. H. Response to COVID-19 in Taiwan: massive information analytics, new know-how, and proactive testing. JAMA 323, 1341–1342 (2020).
Google Scholar
Colizza, V. et al. Time to guage COVID-19 contact-tracing apps. Nat. Med. 27, 361–362 (2021).
Google Scholar
Apple. Apple and Google associate on COVID-19 contact tracing know-how. Apple (2020).
Arevalo, F. N. Decoding the general public curiosity of Aarogya Setu, contact tracing app for managing the COVID-19 pandemic in India. In Proc. 2020 IEEE Worldwide Symposium on Know-how and Society (ISTAS) 508–512 (IEEE, 2020).
Aravindan, A. & Phartiyal, S. Bluetooth cellphone apps for monitoring COVID-19 present modest early outcomes. (2020).
Probyn, A. Coronavirus lockdowns may finish in months if Australians are prepared to have their actions monitored. ABC (2020).
Morley, J., Cowls, J., Taddeo, M. & Floridi, L. Moral pointers for COVID-19 tracing apps. Nature 582, 29–31 (2020).
Grande, D. et al. Shopper views on utilizing digital information for COVID-19 management in america. JAMA Netw. Open 4, e2110918 (2021).
Google Scholar
Bahrain, Kuwait and Norway contact tracing apps amongst most harmful for privateness. Amnesty Worldwide (2020).
Hidayat-ur-Rehman, I., Ahmad, A., Ahmed, M. & Alam, A. Cellular purposes to struggle towards COVID-19 pandemic: the case of Saudi Arabia. TEM J. 10, 69–77 (2021).
Wymant, C. et al. The epidemiological influence of the NHS COVID-19 App. Nature 594, 408–412 (2021).
Google Scholar
Menges, D., Aschmann, H. E., Moser, A., Althaus, C. L. & Von Wyl, V. An information-driven simulation of the publicity notification cascade for digital contact tracing of SARS-CoV-2 in Zurich, Switzerland. JAMA Netw. Open 4, e218184 (2021).
Google Scholar
Ladyzhets, B. We investigated whether or not digital contact tracing truly labored within the US. Know-how Overview (2021).
Steinhauer, J. & Goodenough. A. Contact tracing is failing in lots of states. Right here’s why. The New York Occasions (31 July 2020).
O’Neill, P. H. No, coronavirus apps don’t want 60% adoption to be efficient. Know-how Overview (2020).
Rüdiger, S. et al. Predicting the SARS-CoV-2 efficient replica quantity utilizing bulk contact information from cellphones. Proc. Natl Acad. Sci. USA 118, e2026731118 (2021).
Krieg, S. J. et al. Information-driven testing program improves detection of COVID-19 circumstances and reduces neighborhood transmission. NPJ Digit. Med. 5, 17 (2022).
Google Scholar
Sharma, T. & Bashir, M. Use of apps within the COVID-19 response and the lack of privateness safety. Nat. Med. 26, 1165–1167 (2020).
Google Scholar
Gasser, U., Ienca, M., Scheibner, J., Sleigh, J. & Vayena, E. Digital instruments towards COVID-19: taxonomy, moral challenges, and navigation support. Lancet Digit. Well being 2, e425–e434 (2020).
Ting, D. S. W., Carin, L., Dzau, V. & Wong, T. Y. Digital know-how and COVID-19. Nat. Med. 26, 459–461 (2020).
Google Scholar
Rimmer, A. Sixty seconds on… the pingdemic. BMJ 374, 1822 (2021).
Mina, M. J. & Andersen, Ok. G. COVID-19 testing: one dimension doesn’t match all. Science 371, 126–127 (2021).
Google Scholar
Dror, A. A. et al. Vaccine hesitancy: the subsequent problem within the struggle towards COVID-19. Eur. J. Epidemiol. 35, 775–779 (2020).
Google Scholar
Geneviève, L. D. et al. Participatory illness surveillance techniques: moral framework. J. Med. Web Res. 21, e12273 (2019).
Google Scholar
Leave a Reply