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Digital technologies in the public-health response to COVID-19


Digital technologies are being harnessed to support the public-health response to COVID-19 worldwide, including population surveillance, case identification, contact tracing and evaluation of interventions on the basis of mobility data and communication with the public. These rapid responses leverage billions of mobile phones, large online datasets, connected devices, relatively low-cost computing resources and advances in machine learning and natural language processing. This Review aims to capture the breadth of digital innovations for the public-health response to COVID-19 worldwide and their limitations, and barriers to their implementation, including legal, ethical and privacy barriers, as well as organizational and workforce barriers. The future of public health is likely to become increasingly digital, and we review the need for the alignment of international strategies for the regulation, evaluation and use of digital technologies to strengthen pandemic management, and future preparedness for COVID-19 and other infectious diseases.


COVID-19, a previously unknown respiratory illness caused by the coronavirus SARS-CoV-21,2, was declared a pandemic by the World Health Organization (WHO) on 11 March 2020, less than 3 months after cases were first detected. With now over 9.8 million confirmed cases and more than 495,000 deaths3 recorded worldwide, there are grave concerns about the global health, societal and economic effects of this virus, particularly on vulnerable and disadvantaged populations, and in low- and middle-income countries with fragile health systems4,5. At the time of this writing, 7.1 billion people live in countries that have had substantial travel and social restrictions6.

As with the control of outbreaks and pandemics before it, controlling the COVID-19 pandemic rests on the detection and containment of clusters of infection and the interruption of community transmission to mitigate the impact on human health. During the plague outbreak that affected 14th-century Europe, isolation of affected communities and restriction of population movement were used to avoid further spread7. These public-health measures for outbreak response remain relevant today, including surveillance, rapid case identification, interruption of community transmission and strong public communication. Monitoring how these measures are implemented and their impact on incidence and mortality is essential.

All countries are required by the International Health Regulations (2005)8 to have core capacity to ensure national preparedness for infectious hazards that have the potential to spread internationally. Research and development of new methods and technologies to strengthen these core capacities often occurs during outbreaks, when innovation is an absolute necessity9. During the outbreak of severe acute respiratory syndrome in 2003, Hong Kong identified clusters of disease through the use of electronic data systems10. During the Ebola outbreaks in West Africa in 2014–2016, mobile phone data were used to model travel patterns11, and hand-held sequencing devices permitted more-effective contact tracing and a better understanding of the dynamics of the outbreaks12. Similarly, digital technologies also have been deployed in the COVID-19 pandemic13,14 (Table 1) to strengthen each of the four public-health measures noted above.

Table 1 Digital technologies used in the COVID-19 pandemic

The digital revolution has transformed many aspects of life. As of 2019, 67% of the global population had subscribed to mobile devices, of which 65% were smartphones—with the fastest growth in Sub-Saharan Africa15. In 2019, 204 billion apps were downloaded16, and as of January 2020, 3.8 billion people actively used social media17.

Here we critically review how digital technologies are being harnessed for the public-health response to COVID-19 worldwide (Fig. 1). We discuss the breadth of innovations and their respective limitations. This systems-level approach is needed to inform how digital strategies can be incorporated into COVID-19-control strategies, and to help prepare for future epidemics.

Fig. 1: The interconnected digital technologies used in the public-health response to COVID-19.

Many approaches use a combination of digital technologies and may rely on telecommunications infrastructure and internet availability. Machine learning is shown as a separate branch for clarity, although it also underpins many of the other technologies. Much of the data generated from these technologies feeds into data dashboards. SMS, short message service.

Digital epidemiological surveillance

A core public-health function of outbreak management is understanding infection transmission in time, place and person, and identifying risk factors for the disease to guide effective interventions. A range of digital data sources are being used to enhance and interpret key epidemiological data gathered by public-health authorities for COVID-19.

Online data sources for early disease detection

Established population-surveillance systems typically rely on health-related data from laboratories, notifications of cases diagnosed by clinicians and syndromic surveillance networks. Syndromic surveillance networks are based on reports of clinical symptoms, such as ‘influenza-like illness’, rather than a laboratory diagnosis, from hospital and selected sentinel primary and secondary healthcare facilities, which agree to provide regular surveillance data of all cases. These sources, however, ultimately miss cases in which healthcare is not sought. In the UK, for example, where until recently only hospitalized patients and healthcare workers were routinely tested for COVID-19, confirmed cases represent an estimated 4.7% of symptomatic COVID-19 cases18. Identifying undetected cases would help elucidate the magnitude and characteristics of the outbreak19 and reduce onward transmission.

In the past two decades, data from online news sites, news-aggregation services, social networks, web searches and participatory longitudinal community cohorts have aimed to fill this gap. Data-aggregation systems, including ProMED-mail20, GPHIN21, HealthMap22 and EIOS23, which use natural language processing and machine learning to process and filter online data, have been developed to provide epidemiological insight. These data sources are increasingly being integrated into the formal surveillance landscape24 and have a role in COVID-19 surveillance. The WHO’s platform EPI-BRAIN brings together diverse datasets for infectious-disease emergency preparedness and response, including environmental and meteorological data25. Several systems have claimed detection of early disease reports for COVID-19, through the use of crowdsourced data and news reports, before the WHO released a statement about the outbreak14,20,26. The UK’s automatic syndromic surveillance system scans National Health Service digital records27 to pick up clusters of a respiratory syndrome that could signal COVID-19. There is also interest in using online data to estimate the true community spread of infectious diseases28,29. Preliminary work on the epidemiological analysis of COVID-19-related social-media content has been reported30,31,32. Models for COVID-19 (ref. 33), building on previously established internet search algorithms for influenza34, are included in Public Health England’s weekly reports35.

Crowdsourcing systems used to elucidate the true burden of disease are also supporting syndromic surveillance. InfluenzaNet gathers information about symptoms and compliance with social distancing from volunteers in several European countries through a weekly survey36. Similar efforts exist in other countries, such as COVID Near You37 in the USA, Canada and Mexico. The COVID-19 symptom-tracker app has been downloaded by 3.9 million people in the UK and USA38 and is feeding into national surveillance. While rapid and informative, these systems can suffer from selection bias, over-interpretation of findings and lack of integration with official national surveillance that report established surveillance metrics. A fragmented approach has meant that there are 39 initiatives in the UK alone that are collecting symptoms from people in the community, with no centralized data collection (M. Edelstein, personal communication).

Data-visualization tools for decision support

Data dashboards are being used extensively in the pandemic, collating real-time public-health data, including confirmed cases, deaths and testing figures, to keep the public informed and support policymakers in refining interventions39,40,41. COVID-19 dashboards typically focus on time-series charts and geographic maps, ranging from region-level statistics to case-level coordinate data40,42. Several dashboards show wider responses to the pandemic, such as clinical trials43, policy and economic interventions44 and responses to social-distancing directives45. Few dashboards include data on contact tracing or community surveillance from apps or their effectiveness. Challenges with the quality and consistency of data collection remain a concern. Lack of official standards and inconsistencies in government reporting of statistics across countries make global comparisons difficult. Up-to-date and accurate offline statistics from governments are also not always accessible. Novel visualization approaches are emerging, such as the NextStrain open repository, which presents viral sequence data to create a global map of the spread of infection41. This is enabled by open sharing of data and is based on open-source code. Such speed of the sharing of such data has not been witnessed in previous global outbreaks46.

Rapid case identification

Early and rapid case identification is crucial during a pandemic47 for the isolation of cases and appropriate contacts in order to reduce onward spread and understand key risks and modes of transmission. Digital technologies can supplement clinical and laboratory notification, through the use of symptom-based case identification and widespread access to community testing and self testing, and with automation and acceleration of reporting to public-health databases.

Case identification by online symptom reporting, as seen in Singapore48 and the UK49, is traditionally used for surveillance, but it now offers advice on isolation and referrals to further healthcare services, such as video assessments50 and testing. These services can be rapidly implemented but must be linked to ongoing public-health surveillance and to action, such as isolation of cases and quarantining of contacts. Although this approach is suitable for symptomatic people, widespread testing of people and populations, as well as contact tracing, has a crucial role in case identification, as an estimated 80% of COVID-19 cases are mild or asymptomatic19. Sensors, including thermal imaging cameras and infrared sensors, are being deployed to identify potential cases on the basis of febrile symptoms (for example, at airports). The large numbers of false-positive and false-negative results mean that this is unlikely to have a substantial effect beyond increasing awareness51,52. Wearable technologies are also being explored for monitoring COVID-19 in populations53.

There has been increasing interest in decentralized, digitally connected rapid diagnostic tests to widen access to testing, increase capacity and ease the strain on healthcare systems and diagnostic laboratories54,55,56. Several point-of-care COVID-19 PCR tests are in development57,58; however, their use is still limited to healthcare settings. Drive-through testing facilities and self-swab kits have widened access to testing. There are inherent delays between sampling, sending samples to centralized labs, waiting for results and follow-up. By contrast, point-of-care rapid diagnostic antibody tests could be implemented in home or community or social-care settings and would give results within minutes. Linking to smartphones with automatic readout through the use of image processing and machine-learning methods59,60 could allow mass testing to be linked with geospatial and patient information rapidly reported to both clinical systems and public-health systems and could speed up results. For this to work effectively, standardization of data and integration of data into electronic patient records are required.

Identifying past infections by antibody testing is also central to population-level surveillance and evaluating the efficacy of interventions such as social distancing. So far, point-of-care serology tests in particular have variable performance, and in light of the possibility that antibody responses may be short-lived, how such testing can assist in patient management remains unclear61,62,63. Some have argued that seropositive workers who must remain active in the economy could receive a digital ‘immunity passport’ to demonstrate protection from infection, although such a strategy is fraught with operational and clinical uncertainty63,64. Machine-learning algorithms are also being developed for case identification by automated differentiation of COVID-19 from community-acquired pneumonia through the use of hospital chest scans by computerized tomography65,66,67. Further evaluation of their utility is recommended68,69.

Interrupting community transmission

After case identification and isolation, rapid tracing and quarantining of contacts is needed to prevent further transmission70. In areas of high transmission, the implementation and monitoring of these interventions is needed at a scale that is becoming increasingly unfeasible or at least challenging by traditional means71.

Digital contact tracing

Digital contact tracing automates tracing on a scale and speed not easily replicable without digital tools71. It reduces reliance on human recall, particularly in densely populated areas with mobile populations. In the COVID-19 pandemic, digital contact-tracing apps have been developed for use in several countries; these apps rely on approaches and technologies not previously tried on this scale and are controversial in terms of privacy. Evaluating their accuracy and effectiveness is essential.

Early digital tracing initiatives raised concerns about privacy72. In South Korea, contacts of confirmed cases were traced through the use of linked location, surveillance and transaction data73. In China, the AliPay HealthCode app automatically detected contacts by concurrent location and automated the enforcement of strict quarantine measures by limiting the transactions permitted for users deemed to be high risk74,75. More-recent voluntary contact-tracing apps have been launched in collaboration with governments; these collect location data by global positioning system (GPS) or cellular networks76, proximity data collected by Bluetooth72,77 or a combination of those78,79. Concerns have been raised about centralized systems (Fig. 2) and GPS tracking. Norway halted the use of and data collection from its Smittestopp app after the country’s data-protection watchdog objected to the app’s collection of location data as ‘disproportionate to the task’, and they recommended a Bluetooth-only approach80. Several international frameworks with varying levels of privacy preservation are emerging, including Decentralized Privacy-Preserving Proximity Tracing81, the Pan-European Privacy-Preserving Proximity Tracing initiative82 and the joint Google–Apple framework83.

Fig. 2: Contact tracing for COVID-19 with Bluetooth-enabled smartphone apps.

Proximity-detecting contact-tracing apps use Bluetooth signals emitting from nearby devices to record contact events. Centralized apps share information about contacts and contact events with a central server. The centralized TraceTogether app72 uploads information when a user reports testing positive for COVID-19. Some centralized Bluetooth-enabled contact-tracing apps upload the contact graph for all users148. Decentralized apps, such as SwissCovid149, upload only an anonymous identifier of the user who reports testing positive for COVID-19. This identifier is then broadcast to all users of the app, which compares the identifier with on-phone contact-event records.

A key limitation of contact-tracing apps is that they require a large proportion of the population to use the app and comply with advice for them to be effective in interrupting community transmission (effective reproduction number (R), <1)71. Placing this in perspective, national uptake of the TraceTogether app in Singapore had reached only 30% as of June 2020 (ref. 72). Adoption is also limited by smartphone ownership, user trust, usability and handset compatibility. Key practical issues remain, such as understanding which contacts are deemed to be close enough for transmission and when exposure time is considered long enough to trigger an alert. System effectiveness in identifying transmission events is not well described, and it is therefore arguable that human interpretation is still important.

Evaluating interventions through the use of mobility data

Aggregated location data collected by smartphones via GPS, cellular network and Wi-Fi can monitor real-time population flows84, identify potential transmission hotspots and give insight into the effectiveness of public-health interventions such as travel restrictions on actual human behavior. Access to mobility data is a major challenge, and these approaches have raised ethical and privacy concerns85.

Mobility data with privacy-preserving aggregation steps have recently been made available by several technology and telecom companies for the purposes of COVID-19 control; however, the datasets are limited and there is no long-term commitment in place for data sharing. Daily aggregated origin-destination data from Baidu86 are being used to evaluate the effect of travel restrictions87 and quarantine measures88 on COVID-19 transmission in China. Analysis of the location data of Italian smartphone users estimated a reduction of 50% in the total trips between Italian provinces in the week after the announcement of lockdown on 12 March 2020 (ref. 89). Google has released weekly mobility reports with sub-national granularity, including breakdown by journey type and destination (such as workplaces and parks), and has made their dataset publicly downloadable90. Apple has similarly released a dataset with daily figures for mobility and assumed method of transport91. There is no standardization of these datasets between providers, however, and not all countries or regions are included in these datasets.

Assessing local differences in mobility and contact patterns may be critical for predicting the heterogeneity of transmission rates between different communities and in different regions in which household size and age-stratified contact patterns may differ. This contextual information can provide insight into the effect of interventions to slow transmission, including the impact of handwashing92, social distancing and school closures93. The monitoring of social-distancing measures could also be used to forecast health-system demands94 and will be important in assessing the easing of restrictions when appropriate. Concerns have been raised over breaches of civil liberties and privacy when people are tracked to monitor adherence to quarantine and social distancing, including the use of wearable devices95 and drones96.

Public communication: informing populations

Effective implementation of interventions during a pandemic relies on public education and cooperation, supported by an appropriate communications strategy that includes active community participation to ensure public trust. With 4.1 billion people accessing the internet97 and 5.2 billion unique mobile subscribers15, targeted communication through digital platforms has the potential to rapidly reach billions and encourage community mobilization (Fig. 3). Key challenges persist, including the rise of potentially harmful misinformation98,99 and digital inequalities100 (discussed below).

Fig. 3: The global reach of mobile phones to areas affected by COVID-19.

Mobile subscriptions per 100 people (blue; International Telecoms Union150, 2018) and reported COVID-19 cases by country (red; WHO151, 8 June 2020). COVID-19 is a global pandemic, yet some countries may be better resourced than others to respond with digital health interventions. There may be intra-country inequalities in mobile subscription rates. Case detection and reporting practices differ among countries, with variable under-reporting of true cumulative case counts.

Online data and social media have had an ongoing, important role in public communication101 since the first reports of an unusual influenza-like illness resistant to conventional treatment methods emerged in China102. Public-health organizations and technology companies are stepping up efforts to mitigate the spread of misinformation103,104 and to prioritize trusted news sites; for example, Google’s SOS alert intervention105 prioritizes the WHO and other trusted sources at the top of search results. There are few reports about the impact of these interventions106,107 and difficulties in defining misinformation108. A United Nations study found that 86% of member states had placed COVID-19 information on national websites by early April 2020 (ref. 109), and many are using text messaging to reach populations who do not have access to the internet. Chat-bots are also providing information to reduce the burden on non-emergency health-advice call centers110, and clinical practice is being transformed by the rapid adoption of remote health-service delivery, including telemedicine, especially in primary care50

Digital communication platforms are also supporting adherence to social-distancing measures. Video conferencing is allowing people to work and attend classes from home111, online services are supporting mental health112 and digital platforms are enabling community-mobilization efforts by providing ways to assist those in need113. Nevertheless, the security and privacy of freely available communication platforms remains a concern, particularly for the flow of confidential healthcare information.

Future directions

Digital technologies join a long line of public-health innovations that have been at the heart of disease-prevention-and-containment strategies for centuries. Public health has been slower to take up digital innovations than have other sectors, with the first WHO guidelines on digital health interventions for health-system strengthening published in 2019 (refs. 114,115). The unprecedented humanitarian and economic needs presented by COVID-19 are driving the development and adoption of new digital technologies at scale and speed. We have highlighted the potential of digital technologies to support epidemiological intelligence with online datasets, identify cases and clusters of infections, rapidly trace contacts, monitor travel patterns during lockdown and enable public-health messaging at scale. Barriers to the widespread use of digital solutions remain.


Digital technologies cannot operate in isolation and need to be integrated into existing public healthcare systems116. For example, South Korea and Singapore successfully introduced contact-tracing apps to support large teams of manual contact tracers as one of many measures, including strict isolation of cases and quarantine73.

Digital data sources, like any data source, need to be integrated and interoperable, such as with electronic patient records. Analysis and use of these data will depend on the digital infrastructure and readiness of public-health systems, spanning secondary, primary and social-care systems. The logistics of delivery to ensure population impact are often given too little attention and can lead to over-focus on the individual technology and not its effective operation in a system. The coordination of interventions is also a challenge, with multiple symptom-reporting sites in a single country, which risks fragmentation.

Looking ahead, there is a need for a systems-level approach for the vision of the ideal fit-for-purpose digital public-health system117 that links symptom-tracking apps, rapid testing and case isolation, contact tracing and monitoring of aggregated population-mobility levels, access to care and long-term follow-up and monitoring, with public communication (Fig. 4). These types of integrated online care pathways are not new concepts, having been shown to be highly acceptable and feasible for other infectious diseases, such as chlamydia118.

Fig. 4: The flow of information in a digitally enabled and integrated public-health system during an infectious-disease outbreak.

Digital data are created by the public, both at the population level and at the individual level, for epidemiological intelligence and public-health interventions, and for the support of clinical case management. They are also informed by conventional surveillance via laboratory and clinical notification. This feeds into public-health decision-making and communication with the public through digital channels. Other relevant sources of information include population, demographic, economic, social, transport, weather and environmental data.

Data sharing and data quality

Big-data and artificial-intelligence approaches are only as good as the empirical datasets that are put into them, yet detailed public-health and private datasets are often inaccessible, due to privacy and security concerns, and often lack standardized formats or are incomplete. Researchers are calling for technology and telecom companies to share their data in a ‘proportionate, ethical and privacy-preserving manner’85,119,120, often citing a moral imperative for these companies to contribute where there is justification for data use. Some companies are making subsets of aggregated data available86,90,91,121,122. These data are not consistent and are not provided within the same timeframe, and there is no standard format or long-term commitment. Researcher-led international collaborations have aimed to aggregate multiple international data sources of voluntarily reported information41,123.

Equally, governments should provide much greater transparency in their datasets, including epidemiological data and risk factors for acquisition, with downloadable formats for researchers. Several governments have made available de-personalized individual-level datasets for research purposes124,125, although this raises potential privacy concerns. Open-source data, code and scientific methods are being rapidly and widely shared online, including increased use of preprints, which speed up data availability but lack peer review126.

Evidence of effectiveness and regulation

Evidence of the effectiveness of any new technology is needed for wider adoption, but as the current pandemic is ongoing, many digital technologies have not yet been peer-reviewed, been integrated into public-health systems, undergone rigorous testing127 or been evaluated by digital health-evidence frameworks, such as the evidence standards framework for digital health technologies of the National Institute for Health and Care Excellence128. Contact-tracing apps have been launched in at least 40 countries129, but there is currently no evidence of the effectiveness of these apps130, such as the yield of identified cases and contacts, costs, compliance with advice, empirical estimates of a reduction in the R value or a comparison with traditional methods. Although it is challenging, due to the urgency of the pandemic, evaluation of the effectiveness of interventions is essential. Researchers, companies and governments should publish the effectiveness of their technologies in peer-reviewed journals and through appropriate clinical evaluation.

There is an urgent need for coordinated international digital public-health strategies, but these have been slow to emerge. On 22 March 2020, the WHO release a draft of its global strategy on digital health for 2020–2024 (ref. 131). On 8 April, the European Union called for a pan-European approach on the use of apps and mobile data for COVID-1982,132.

Legal, ethical and privacy concerns

Highly granular or personal data for public-health surveillance raises legal concerns133, ethical concerns134,135 and security and privacy concerns136. Not all digital interventions have allowed consensual adoption or have made the option of consent for specific purposes explicit75, and some have been used to enforce measures as well as to monitor them. In many cases, widespread adoption is related to effectiveness, which highlights the need for public trust and engagement. There is concern that emergency measures set precedent and may remain in place beyond the emergency, which will lead to the ongoing collection of information about private citizens with no emergency-related purpose137,138. All systems will need to be ‘proofed’ against invasions of privacy and will need to comply with appropriate legal, ethical and clinical governance75. Data can be shared under a legal contract for a well-defined purpose and time, with requirements for independent audit139 to ensure data are not used for purposes outside of the pandemic. Dynamic consent processes could also allow users to share their data, and privacy-preserving technologies, such as differential privacy and homomorphic encryption, could ensure that access is possible only for specific purposes and is available in a tamper-proof manner13,140 to allow auditing.

Inequalities and the digital divide

In 2018, the World Health Assembly Resolution on Digital Health recognized the value of digital technologies in advancing universal health coverage and the Sustainable Development Goals. Although trends are narrowing, today there remains a digital divide, and 51% of the world’s population does not subscribe to the mobile internet15. The lack of access to mobile communications is seen in low- and middle-income countries, although people with lower socio-economic status in high-income countries are also affected141. The Pew Research Center reported large disparities between people 18–29 years of age and those over 50 years of age in their mobile-communication access142. There are also reports of restricted mobile internet access, such as in areas of Myanmar, which have left some populations unware of the pandemic143. This outbreak has also disproportionately affected some communities, such as Black and minority ethnic groups, more than others144. It is therefore essential to develop tools and messaging that are accessible100 and can be tailored to specific risks, languages and cultural contexts.

Workforce and organizational barriers

The spread of the COVID-19 pandemic has exposed the need for government leadership to accelerate the evaluation and adoption of digital technologies. Successful implementation strategies will require carefully accelerated and coordinated policies, with collaboration among multiple areas of governments, regulators, companies, non-governmental organizations and patient groups. Public health has long been under-funded compared with the funding of other areas of health145. Long-term changes will necessitate investment in national and international digital centers of excellence, with the necessary balance of partners and pre-agreed access to digital datasets. A substantial investment in workforce education and skills is essential for growing digital public-health leadership146.


The COVID-19 pandemic is ongoing, and it is too early to fully quantify the added value of digital technologies to the pandemic response. While digital technologies offer tools for supporting a pandemic response, they are not a silver bullet. The emerging consensus is that they have an important role in a comprehensive response to outbreaks and pandemics, complementing conventional public-health measures, and thereby contribute to reducing the human and economic impact of COVID-19. Cost-effectiveness and sustainability will require systems-level approaches to building digital online care pathways that link rapid and widespread testing with digital symptom checkers, contact tracing, epidemiological intelligence and long-term clinical follow up. The COVID-19 pandemic has confirmed not only the need for data sharing but also the need for rigorous evaluation and ethical frameworks with community participation to evolve alongside the emerging field of mobile and digital healthcare. Building public trust through strong communication strategies across all digital channels and demonstrating a commitment to proportionate privacy are imperative147.

The future of public health is likely to be increasingly digital, and recognizing the importance of digital technology in this field and in pandemic preparedness planning has become urgent. Key stakeholders in the digital field, such as technology companies, should be long-term partners in preparedness rather than being partners only when emergencies are ongoing. Viruses know no borders and, increasingly, neither do digital technologies and data. There is an urgent need for alignment of international strategies for the regulation, evaluation and use of digital technologies to strengthen pandemic management and future preparedness for COVID-19 and other infectious diseases.


  1. 1.

    Wu, F. et al. A new coronavirus associated with human respiratory disease in China. Nature 579, 265–269 (2020).

    CAS  Article  Google Scholar 

  2. 2.

    Zhu, N. et al. A novel coronavirus from patients with pneumonia in China, 2019. N. Engl. J. Med. 382, 727–733 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    World Health Organization. Coronavirus Disease (COVID-19) Situation Reports Report no. 160 (2020).

  4. 4.

    Hopman, J., Allegranzi, B. & Mehtar, S. Managing COVID-19 in low- and middle-income countries. J. Am. Med. Assoc. 323, 1549 (2020).

    CAS  Google Scholar 

  5. 5.

    Lloyd-Sherlock, P., Ebrahim, S., Geffen, L. & McKee, M. Bearing the brunt of covid-19: older people in low and middle income countries. Br. Med. J. 368, m1052 (2020).

    Google Scholar 

  6. 6.

    Connor, P. More than nine-in-ten people worldwide live in countries with travel restrictions amid COVID-19. Pew Research Center (2020).

  7. 7.

    Tognotti, E. Lessons from the history of quarantine, from plague to influenza A. Emerg. Infect. Dis. 19, 254–259 (2013).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    World Health Organization. International Health Regulations (2005) Third Edition (2016).

  9. 9.

    Heymann, D. L. Public health surveillance for communicable diseases: from rigid and static to flexible and innovative. Am. J. Public Health 107, 845–846 (2017).

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Leung, G. M. et al. The epidemiology of severe acute respiratory syndrome in the 2003 Hong Kong epidemic: an analysis of all 1755 patients. Ann. Intern. Med. 141, 662–673 (2004).

    PubMed  Google Scholar 

  11. 11.

    Wesolowski, A. et al. Commentary: containing the Ebola outbreak—the potential and challenge of mobile network data. PLoS Curr. 6, ecurrents.outbreaks.0177e7fcf52217b8b634376e2f3efc5e (2014).

  12. 12.

    Quick, J. et al. Real-time, portable genome sequencing for Ebola surveillance. Nature 530, 228–232 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

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

    CAS  PubMed  Google Scholar 

  14. 14.

    McCall, B. COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread. Lancet Digit Health 2, e166–e167 (2020).

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    GSMA. The mobile economy 2020. (2020).

  16. 16.

    Perez, S. App stores saw record 204 billion app downloads in 2019, consumer spend of $120 billion. TechCrunch (2020).

  17. 17.

    Kemp, S. Digital 2020: global digital overview. DataReportal (2020).

  18. 18.

    Russel, T. W. et al. Using a delay-adjusted case fatality ratio to estimate under-reporting. Centre for Mathematical Modelling of Infectious Diseases Repository (accessed 26 April 2020).

  19. 19.

    Heneghan, C., Brassey, J. & Jefferson, T. COVID-19: What proportion are asymptomatic? Centre for Evidence-Based Medicine (2020).

  20. 20.

    ProMED. ProMED-mail. (2020).

  21. 21.

    Government of Canada. About GPHIN. (2020)

  22. 22.

    HealthMap. COVID-19. (accessed 29 June 2020)

  23. 23.

    World Health Organization. Epidemic intelligence from open sources (EIOS). (2020).

  24. 24.

    Edelstein, M., Lee, L. M., Herten-Crabb, A., Heymann, D. L. & Harper, D. R. Strengthening global public health surveillance through data and benefit sharing. Emerg. Infect. Dis. 24, 1324–1330 (2018).

    PubMed Central  Google Scholar 

  25. 25.

    World Health Organization. EPI-BRAIN. (2020)

  26. 26.

    Bogoch, I. I. et al. Pneumonia of unknown aetiology in Wuhan, China: potential for international spread via commercial air travel. J. Travel Med. 27, 1–3 (2020).

    Google Scholar 

  27. 27.

    Smith, G. E. et al. Novel public health risk assessment process developed to support syndromic surveillance for the 2012 Olympic and Paralympic Games. J. Public Health (Bangk.) 39, e111–e117 (2017).

    Google Scholar 

  28. 28.

    Gomide, J. et al. Dengue surveillance based on a computational model of spatio-temporal locality of Twitter. Proc. 3rd Int. Web Sci. Conf. WebSci 2011 (2011).

  29. 29.

    Lampos, V., Yom-Tov, E., Pebody, R. & Cox, I. J. Assessing the impact of a health intervention via user-generated Internet content. Data Min. Knowl. Discov. 29, 1434–1457 (2015).

    Google Scholar 

  30. 30.

    Sun, K., Chen, J. & Viboud, C. Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study. Lancet Digit Health 2, e201–e208 (2020).

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Qin, L. et al. Prediction of number of cases of 2019 novel coronavirus (COVID-19) using social media search index. Int. J. Environ. Res. Public Health 17, 2365 (2020).

    PubMed Central  Google Scholar 

  32. 32.

    Lu, Y. & Zhang, L. Social media WeChat infers the development trend of COVID-19. J. Infect. 81, e82–e83 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Lampos, V. et al. Tracking COVID-19 using online search. Preprint at (2020).

  34. 34.

    Lampos, V. et al. Advances in nowcasting influenza-like illness rates using search query logs. Sci. Rep. 5, 12760 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Public Health England. Weekly Coronavirus Disease 2019 (COVID-19) Surveillance Report (2020).

  36. 36.

    Koppeschaar, C. E. et al. Influenzanet: citizens among 10 countries collaborating to monitor influenza in Europe. Surveill 3, e66 (2017).

    Google Scholar 

  37. 37.

    COVID Near You. (2020).

  38. 38.

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

    CAS  PubMed  Google Scholar 

  39. 39.

    Ministry of Health Singapore. Updates on COVID-19 (coronavirus disease 2019) local situation. (2020).

  40. 40.

    Centre for Health Protection, Department of Health. Latest situation of novel coronavirus infection in Hong Kong. The Government of the Hong Kong Special Administrative Region (accessed 25 April 2020).

  41. 41.

    Nextstrain team. Genomic epidemiology of novel coronavirus—global subsampling. (accessed 25 April 2020).

  42. 42.

    covid19 SG. Dashboard of the COVID-19 virus outbreak in Singapore. (accessed 25 April 2020).

  43. 43.

    Thorlund, K. et al. A real-time dashboard of clinical trials for COVID-19. Lancet Digit. Heal. 2, e286–e287 (2020).

    Google Scholar 

  44. 44.

    The World Bank. World Bank Education and COVID-19. (accessed 25 April 2020).

  45. 45.

    COVID-19 Mobility Data Network. Movement trends. (accessed 25 April 2020).

  46. 46.

    World Health Organization. Revised Analysis: Approaches to Seasonal Influenza and Genetic Sequence Data Under the PIP Framework (2018).

  47. 47.

    World Health Organization. WHO Director-General’s Opening Remarks at the Media Briefing on COVID-19 - 16 March 2020 (2020).

  48. 48.

    Singapore COVID-19 Symptom Checker. (2020).

  49. 49.

    NHS 111 online. (2020).

  50. 50.

    Greenhalgh, T., Koh, G. C. H. & Car, J. Covid-19: a remote assessment in primary care. Br. Med. J. 368, m1182 (2020).

    Google Scholar 

  51. 51.

    Gostic, K., Gomez, A. C. R., Mummah, R. O., Kucharski, A. J. & Lloyd-Smith, J. O. Estimated effectiveness of symptom and risk screening to prevent the spread of COVID-19. eLife 9, e55570 (2020).

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Quilty, B. J., Clifford, S., Flasche, S. & Eggo, R. M. Effectiveness of airport screening at detecting travellers infected with novel coronavirus (2019-nCoV). Eur. Surveill. 25, 2000080 (2020).

    Google Scholar 

  53. 53.

    Armitage, H. Stanford Medicine scientists hope to use data from wearable devices to predict illness, including COVID-19. Stanford Medicine News Center (2020).

  54. 54.

    Wood, C. S. et al. Taking mobile health connected infectious disease diagnostics to the field. Nature 565, 467–474 (2019).

    Google Scholar 

  55. 55.

    Land, K. J., Boeras, D. I., Chen, X.-S., Ramsay, A. R. & Peeling, R. W. REASSURED diagnostics to inform disease control strategies, strengthen health systems and improve patient outcomes. Nat. Microbiol. 4, 46–54 (2019).

    CAS  PubMed  Google Scholar 

  56. 56.

    Udugama, B. et al. Diagnosing COVID-19: the disease and tools for detection. ACS Nano 14, 3822–3835 (2020).

    CAS  PubMed  Google Scholar 

  57. 57.

    Green, K., Graziadio, S., Turner, P., Fanshawe, T. & Allen, J. Molecular and antibody point-of-care tests to support the screening, diagnosis and monitoring of COVID-19. Centre for Evidence-Based Medicine (2020).

  58. 58.

    FIND. SARS-CoV-2 diagnostic pipeline. (accessed 15 June 2020).

  59. 59.

    Mudanyali, O. et al. Integrated rapid-diagnostic-test reader platform on a cellphone. Lab Chip 12, 2678–2686 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Med-Tech Innovation News. Sanofi and Luminostics to explore COVID-19 self-testing solution. Med-Tech Innovation News (2020).

  61. 61.

    Medicines and Healthcare products Regulatory Agency. Target Product Profile: antibody tests to help determine if people have recent infection to SARS-CoV-2: version 2. Gov.UK (2020).

  62. 62.

    Mallapaty, S. Will antibody tests for the coronavirus really change everything? Nature 580, 571–572 (2020).

    CAS  PubMed  Google Scholar 

  63. 63.

    Adams, E. R. et al. Antibody testing for COVID-19: a report from the National COVID Scientific Advisory Panel. Preprint at medRxiv (2020).

  64. 64.

    Phelan, A. L. COVID-19 immunity passports and vaccination certificates: scientific, equitable, and legal challenges. Lancet 395, 1595–1598 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Mei, X. et al. Artificial intelligence-enabled rapid diagnosis of patients with COVID-19. Nat. Med. (2020).

  66. 66.

    Wang, S. et al. A deep learning algorithm using CT images to screen for coronavirus disease (COVID-19). Preprint at medRxiv (2020).

  67. 67.

    Wynants, L. et al. Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. Br. Med. J. 369, m1328 (2020).

    Google Scholar 

  68. 68.

    Laghi, A. Cautions about radiologic diagnosis of COVID-19 infection driven by artificial intelligence. Lancet Digit Health 2, e225 (2020).

    PubMed  PubMed Central  Google Scholar 

  69. 69.

    Burlacu, A., Crisan-dabija, R., Popa, I. V., Artene, B. & Birzu, V. Curbing the AI-induced enthusiasm in diagnosing COVID-19 on chest X-rays: the present and the near-future. Preprint at medRxiv (2020).

  70. 70.

    Fraser, C., Riley, S., Anderson, R. M. & Ferguson, N. M. Factors that make an infectious disease outbreak controllable. Proc. Natl Acad. Sci. USA 101, 6146–6151 (2004).

    CAS  PubMed  Google Scholar 

  71. 71.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Singapore Government Agency Website. TraceTogether. (2020).

  73. 73.

    Zastrow, M. South Korea is reporting intimate details of COVID-19 cases: has it helped? Nature (2020).

  74. 74.

    Kupferschmidt, K. & Cohen, J. Can China’s COVID-19 strategy work elsewhere? Science 367, 1061–1062 (2020).

    CAS  PubMed  Google Scholar 

  75. 75.

    Bonsall, D. & Fraser, C. Sustainable containment of COVID-19 using smartphones in China: scientific and ethical underpinnings for implementation of similar approaches in other settings. (2020).

  76. 76.

    Ministry of Health. HaMagen - the Ministry of Health app for fighting the spread of coronavirus. (2020).

  77. 77.

    Australian Government Department of Health. COVIDSafe app. (2020).

  78. 78.

    Government of India. Aarogya Setu mobile app. (2020).

  79. 79.

    Together we can fight coronavirus – Smittestopp temporarily deactivated. (accessed 26 April 2020).

  80. 80.

    Halt to COVID-19 contact tracing app a major win for privacy. Amnesty International (accessed 27 July 2020).

  81. 81.

    DP-3T/documents: decentralized privacy-preserving proximity tracing–documents. The DP-3T Project (accessed 26 April 2020).

  82. 82.

    PEPP-PT. High-level pverview: pan-European privacy-preserving proximity tracing. (accessed 27 July 2020).

  83. 83.

    Apple. Apple and Google partner on COVID-19 contact tracing technology. (2020).

  84. 84.

    Jia, J. S. et al. Population flow drives spatio-temporal distribution of COVID-19 in China. Nature 582, 389–394 (2020).

    CAS  PubMed  Google Scholar 

  85. 85.

    Oliver, N. et al. Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle. Sci. Adv. 6, eabc0764 (2020).

    PubMed  PubMed Central  Google Scholar 

  86. 86.

    China Data Lab. Baidu mobility data. Harvard Dataverse (2020).

  87. 87.

    Chinazzi, M. et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science 368, 395–400 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Kraemer, M. U. G. et al. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science 368, 493–497 (2020).

    CAS  PubMed  Google Scholar 

  89. 89.

    Pepe, E. et al. COVID-19 outbreak response: a first assessment of mobility changes in Italy following national lockdown. Preprint at medRxiv (2020).

  90. 90.

    Google. COVID-19 Community Mobility Reports. (accessed 27 April 2020).

  91. 91.

    Apple. COVID-19 - Mobility Trends Reports. (accessed 27 April 2020).

  92. 92.

    Klepac, P. et al. Contacts in context: large-scale setting-specific social mixing matrices from the BBC Pandemic project. Preprint at medRxiv (2020).

  93. 93.

    Zhang, J. et al. Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China. Science 368, 1481–1486 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Deasy, J. et al. Forecasting ultra-early intensive care strain from COVID-19 in England. Preprint at medRxiv (2020).

  95. 95.

    The Government of the Hong Kong Special Administrative Region. Together, We Fight the Virus. StayHomeSafe Mobile App User Guide. (2020).

  96. 96.

    Gupta, M., Abdelsalam, M. & Mittal, S. Enabling and enforcing social distancing measures using smart city and ITS infrastructures: a COVID-19 use case. Preprint at (2020).

  97. 97.

    International Telecommunication Union. Measuring digital development: facts and figures 2019. ITUPublications (2019).

  98. 98.

    Ball, P. & Maxmen, A. The epic battle against coronavirus misinformation and conspiracy theories. Nature 581, 371–374 (2020).

    CAS  PubMed  Google Scholar 

  99. 99.

    Depoux, A. et al. The pandemic of social media panic travels faster than the COVID-19 outbreak. J. Travel Med. 27, taaa031 (2020).

    PubMed  Google Scholar 

  100. 100.

    Beaunoyer, E., Dupéré, S. & Guitton, M. J. COVID-19 and digital inequalities: Reciprocal impacts and mitigation strategies. Comput. Hum. Behav. 111, 106424 (2020).

    Google Scholar 

  101. 101.

    Merchant, R. M. & Lurie, N. Social media and emergency preparedness in response to novel coronavirus. J. Am. Med. Assoc. 323, 2011–2012 (2020).

    CAS  Google Scholar 

  102. 102.

    Green, A. Li Wenliang. Lancet 395, 682 (2020).

    CAS  PubMed Central  Google Scholar 

  103. 103.

    World Health Organization. A Coordinated Global Research Roadmap (2020).

  104. 104.

    World Health Organization. Coronavirus Disease (COVID-19) Situation Reports Report no. 13 (2020).

  105. 105.

    Google. SOS alerts help. (accessed 8 May 2020).

  106. 106.

    Farooq, A., Laato, S. & Islam, A. K. M. N. Impact of online information on self-isolation intention during the COVID-19 pandemic: cross-sectional study. J. Med. Internet Res. 22, e19128 (2020).

    PubMed  PubMed Central  Google Scholar 

  107. 107.

    Sesagiri Raamkumar, A., Tan, S. G. & Wee, H.-L. Measuring the outreach efforts of public health authorities and the public response on facebook during the COVID-19 pandemic in early 2020: cross-country comparison. J. Med. Internet Res. 22, e19334 (2020).

    PubMed  PubMed Central  Google Scholar 

  108. 108.

    Limaye, R. J. et al. Building trust while influencing online COVID-19 content in the social media world. Lancet Digit Health 2019, 2019–2020 (2020).

    Google Scholar 

  109. 109.

    United Nations Department of Economic and Social Affairs. COVID-19: Embracing Digital Government During the Pandemic and Beyond (2020).

  110. 110.

    WhatsApp. How WhatsApp can help you stay connected during the coronavirus (COVID-19) pandemic. (2020)

  111. 111.

    Anderson, M. & Vogels, E. A. Americans turn to technology during COVID-19 outbreak, say an outage would be a problem. Pew Research Center (2020).

  112. 112.

    Liu, S. et al. Online mental health services in China during the COVID-19 outbreak. Lancet Psychiatry 7, e17–e18 (2020).

    PubMed  PubMed Central  Google Scholar 

  113. 113.

    GoodSAM. Instant location/video/consultation 999/111/101/112/911/000 (2020).

  114. 114.

    World Health Organization. Seventy-first World Health Assembly Agenda item 12.4 (2018).

  115. 115.

    World Health Organization. Recommendations on Digital Interventions for Health System Strengthening (WHO, 2019).

  116. 116.

    Gong, M. et al. Cloud-based system for effective surveillance and control of COVID-19: useful experiences from Hubei, China. J. Med. Internet Res. 22, e18948 (2020).

    PubMed  PubMed Central  Google Scholar 

  117. 117.

    Jia, P. & Yang, S. China needs a national intelligent syndromic surveillance system. Nat. Med. 26, 990 (2020).

    CAS  PubMed  Google Scholar 

  118. 118.

    Estcourt, C. S. et al. The eSexual Health Clinic system for management, prevention, and control of sexually transmitted infections: exploratory studies in people testing for Chlamydia trachomatis. Lancet Digit Health 2, e182–e190 (2017).

    Google Scholar 

  119. 119.

    Buckee, C. O. et al. Aggregated mobility data could help fight COVID-19. Science 368, 145–146 (2020).

    PubMed  Google Scholar 

  120. 120.

    McKendry, R. A. et al. Share mobile and social-media data to curb COVID-19. Nature 580, 29–29 (2020).

    CAS  PubMed  Google Scholar 

  121. 121.

    Pham, M. European operators monitor Covid-19 movements. Mobile World Live (2020)

  122. 122.

    Microsoft. Bing-COVID-19-Data. GitHub (2020).

  123. 123.

    Segal, E. et al. Building an international consortium for tracking coronavirus health status. Preprint at medRxiv (2020).

  124. 124.

    Korea to share clinical data of COVID-19 patients. Korea Times (accessed 23 July 2020).

  125. 125.

    Government data, מאגר COVID-19 - מאגרי מידע - (2020).

  126. 126.

    Kupferschmidt, K. Preprints bring ‘firehose’ of outbreak data. Science 367, 963–964 (2020).

    CAS  PubMed  Google Scholar 

  127. 127.

    Department of Immunization. Vaccines and Biologicals (IVB) SAGE Strategic Advisory Group of Experts on Immunization (2019).

  128. 128.

    National Institute for Health and Care Excellence. Evidence standards framework for digital health technologies. (2020).

  129. 129.

    Linklaters. 40 countries ploughing ahead with contact-tracing apps as debate intensifies on differing approaches. (Accessed: 15th June 2020)

  130. 130.

    Show evidence that apps for COVID-19 contact-tracing are secure and effective. Nature 580, 563 (2020).

  131. 131.

    World Health Organization. Draft Global Strategy on Digital Health 2020–2024 (2020).

  132. 132.

    European Commission. Coronavirus: commission adopts recommendation to support exit strategies through mobile data and apps. (2020).

  133. 133.

    Santow, E. Emerging from AI utopia. Science 368, 9 (2020).

    CAS  PubMed  Google Scholar 

  134. 134.

    Parker, M. J., Fraser, C., Abeler-Dörner, L. & Bonsall, D. Ethics of instantaneous contact tracing using mobile phone apps in the control of the COVID-19 pandemic. J. Med. Ethics 46, 427–431 (2020).

    PubMed  PubMed Central  Google Scholar 

  135. 135.

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

    CAS  PubMed  Google Scholar 

  136. 136.

    Ienca, M. & Vayena, E. On the responsible use of digital data to tackle the COVID-19 pandemic. Nat. Med. 26, 463–464 (2020).

    CAS  PubMed  Google Scholar 

  137. 137.

    Calvo, R. A., Deterding, S. & Ryan, R. M. Health surveillance during covid-19 pandemic. Br. Med. J. 369, m1373 (2020).

    Google Scholar 

  138. 138.

    Nay, O. Can a virus undermine human rights? Lancet Public Health (2020). doi:

  139. 139.

    Ada Lovelace Institute. Exit through the App Store? A rapid evidence review on the technical considerations and societal implications of using technology to transition from the COVID-19 crisis. (2020).

  140. 140.

    Mashamba-Thompson, T. P. & Crayton, E. D. Blockchain and artificial intelligence technology for novel coronavirus disease-19 self-testing. Diagnostics (Basel) 10, 198 (2020).

    Google Scholar 

  141. 141.

    Office for National Statistics. Exploring the UK’s digital divide. (accessed 8 May 2020).

  142. 142.

    Schumacher, S. & Kent, N. 8 charts on internet use around the world as countries grapple with COVID-19. Pew Research Center (2020).

  143. 143.

    Human Rights Watch. Myanmar: end world’s longest internet shutdown. (2020).

  144. 144.

    Rimmer, A. Covid-19: disproportionate impact on ethnic minority healthcare workers will be explored by government. Br. Med. J. 369, m1562 (2020).

    Google Scholar 

  145. 145.

    Marmot, M., Allen, J., Boyce, T., Goldblatt, P. & Morrison, J. Health equity in England: the Marmot Review 10 years on. The Health Foundation (2020).

  146. 146.

    Secretary of State for Health and Social Care. The Topol Review: Preparing the healthcare workforce to deliver the digital future. NHS Health Education England (2019).

  147. 147.

    The Academy of Medical Sciences. Interdisciplinary research in epidemic preparedness and response. (2019).

  148. 148.

    PwC. Check-in. (2020).

  149. 149.

    DP-3T. dp3t-app-android-ch. (accessed 15 June 2020).

  150. 150.

    The World Bank. Mobile cellular subscriptions (per 100 people). (accessed 8 May 2020).

  151. 151.

    World Health Organization. WHO coronavirus disease (COVID-19) dashboard. (accessed 8 May 2020).

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Correspondence to Rachel A. McKendry.

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A.M.J., I.J.C., V.C.E., B.S.M., E.M.M., D.P., M.M.S., N.K. and R.A.M. received funding from the EPSRC for i-sense IRC in Agile Early Warning Sensing Systems for Infectious Diseases and AMR (EP/R00529X/1), and collaborate with Google, Microsoft and Telefonica, as well as diagnostic companies. N.K., R.A.M. and M.M.S. received funding from IRC Next Steps Plus: Ultra-Sensitive Enhanced NanoSensing of Anti-Microbial Resistance (u-sense, EP/R018391/1). A.M.J. received funding from NIHR. G.R. undertakes paid consultancy work for Google Health, and undertakes research on predictive analytics in brain imaging funded by the Wellcome Trust. M.M.S. is a consultant for 30T/Edixomed, Simmons and Simmons and Origin Sciences, and receives research grant funding from CRUK, The Wellcome Trust, EPSRC/MRC/UKRI, H2020 and ERC, as well as other grants.

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Budd, J., Miller, B.S., Manning, E.M. et al. Digital technologies in the public-health response to COVID-19. Nat Med 26, 1183–1192 (2020).

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