Pharma Data | Industry Spotlights & Insight Articles

Mobile Health and Covid-19: How Did Big Data Help To Avoid The Worst Of The Pandemic?

This Commentary article revisits the development and implementation of mobile health data, exploring how initiatives such as the NHS track and trace system helped to mitigate the worst of the impact of the Covid-19 pandemic. Here, we consider the applications of mHealth data for present and future pandemic situations.

Presented by Robert Istepanian, Visiting Professor, Institute of Global Health Innovation at Imperial College London

Transcribed by Ben Norris

National and international experiences have shown over the past two years that mobile and digital health solutions are integral to mitigating the worst impacts of Covid-19. As Robert Istepanian, Visiting Professor at College London, explained to the Pharma IT 2020 Digital Health Symposium, Digital Technologies would prove to be instrumental to the national and international response to the Covid-19 pandemic and for future pandemics.

Monitoring Covid-19 Through Digital Health Technologies

The global spread of the SARS-Cov-2 virus leading to the Covid-19 disease has shown itself to be one of the worst pandemics for more than a century, with an estimated 22.2M cases worldwide since March 2020. Robert Istepanian explained to the virtual audience that the virus proved tricky to contain in the early months of the pandemic because it was initially novel. “Most transmissions occur during the pre-symptomatic or incubation phase of the infection – this is what makes controlling it difficult.” The long-term complications associated with some members of the public infected with the virus are still making themselves apparent, while reinfections have been observed in some patients despite them being triple-vaccinated.

Istepanian explained that the virulence of a disease is typically measured on the basis of indicators such as the associated infection and mortality rates. However, the high transmissibility of Covid-19 makes it difficult to control. Coronavirus testing methods will be familiar to all by now, but they merit revisiting for what they represent in terms of the use of big data analytics to support healthcare providers by tracking and monitoring infections. Prior to the introduction of national vaccination programmes in December 2020, the main means of limiting the spread of coronavirus in the UK was through preventative contact measures and the monitoring of individual infections via the NHS track and trace system.

Illustrative example of the antibody levels and distributions over time.
Figure. 1 – Illustrative example of the antibody levels and distributions over time.

The antibody tests used widely during the Covid-19 pandemic usually assess the development of the immune response to the virus in patients by detecting the presence of three types of antibodies (eg, IgG, IgM, and IgA) that the body produces in response to the infection. It is important to highlight that the immune response tests do not achieve the same detection rate as viral genome diagnoses in early infection, as the body needs time to respond to the antigenic viral invasion.

Covid-19 and Digital Health: The Track and Trace System 

“A lot of people hear about digital health,” continued Istepanian, “but what is it? In a nutshell, digital health is a reframing of ICT for healthcare domains.” Istepanian identified the four domains of ICT for healthcare as telemedicine, telehealth, e-health, and mobile health (mHealth) [1,2]. Much of the response to Covid-19 has also incorporated recent advances in mobile health, integrated with AI and big data analytics. The smartphone-centric mHealth applications model identified by Istepanian is integral to the coordinated response and the epidemiological monitoring of the infection rates and the population spread of Covid-19. In 2020, many mobile health tools and solutions had evolved to include more powerful technologies, including big data analytics and AI. From the public health perspective, this meant more digital tools for tracking, early testing and detection, surveillance, and monitoring of Covid-19 infections.

Big data strategies were important in supplementing the protective measures surrounding the national Covid-19 response. The main protective measures implemented to limit the spread of the virus – lockdowns, mass testing, and mandatory mask-wearing – were reinforced by digital tracking and tracing systems. The ubiquity of smartphones in contemporary society made this approach viable: through the contact track and trace app, key codes could be exchanged using Bluetooth. If a contact became symptomatic and tested positive for Covid-19, a QR code was uploaded from the tester that allows the app to upload all of the pseudonyms it has broadcast.

Many countries worldwide had implemented their own track and trace systems: some mandatory systems like in China and elsewhere are still in use. However, many of these digital mitigation, tracking and surveillance tools, used in both the developed and developing countries, were either widely inapplicable or unable to effectively lower the level of Covid-19 infections. This was the case of the development of the NHS digital Test and Trace App in the UK [2].

Mobile Health Data Analytics and Clinical Complexities

According to Istepanian, in the context of mHealth data there are four kinds of analytics. These are descriptive, diagnostic, predictive, and prescriptive. “Mobile and digital health tools for tracking, early testing, detection, surveillance, and monitoring of Covid-19 infection rates are very important in mitigating its impact.” Contact tracing was an example of big health data analysis in action through non-invasive monitoring [3].

Long Covid has also received an increased amount of focus and coverage as the longer-term impact of Covid-19 is still widely understudied and most of the long-term impacts of this disease remain unknown. Many patients in UK and worldwide who have recovered from Covid-19 infection are still reporting lasting effects of the infection, or have had the usual symptoms for far longer than expected. Known symptoms of Long Covid include breathing difficulties, prolonged tiredness, reduced muscle function, an impaired ability to perform vital everyday tasks, and mental health problems such as PTSD, anxiety, and depression.

Future Applications of Digital & Mobile Health Technologies for Health Monitoring

Istepanian indicated the potential for the utilisation of the vast amount of Long Covid health data for the development of new monitoring platforms, enabling personalised interactions for acute healthcare provision. “Low-cost testing and monitoring using mHealth 2.0 technologies are vital to develop an understanding of the complex interactions and the unknown epidemiological aspects of the SARS-CoV-2 virus,” added Istepanian. This principle also applies to increases in transmission resulting from newer mutations such as the Omicron variant and its sub-variants.

“Mobile health needs to be considered as an integral part of any future digital public health policy,” Istepanian concluded. This will support and enable effective and efficient solutions to combat C-19 and future pandemics by developing novel but cost-effective pre-emptive and shielding digital tools. “It is very important that, as we are understanding more about the disease, we use mHealth and its advanced digital tools systems to understand the long-term impact of Covid-19.” There is an urgent need for more innovative solutions that can be realised effectively and deployed successfully to combat this pandemic and prevent the next one.

Visit our PharmaTec portal to learn more about the latest developments in combating Covid-19 and other major healthcare challenges. If you’d like to register your interest in our upcoming Pharma Data & SmartLabs UK events, which will offer two days of technical presentations, case studies and knowledge-sharing, click here.


  1. Istepanian, R.S.H.; Woodard, B. M-Health: Fundamentals and Applications; John Wiley-IEEE Press: Hoboken, NJ, USA, 2017.
  2. Istepanian RSH. Mobile Health (m-Health) in Retrospect: The Known Unknowns. Int J Environ Res Public Health. 2022 Mar 22;19(7):3747. doi: 10.3390/ijerph19073747. PMID: 35409431; PMCID: PMC8998037.
  3. Istepanian RSH, Al-Anzi T. m-Health 2.0: New perspectives on mobile health, machine learning and big data analytics. Methods, 2018 Dec 1;151:34-40. doi: 10.1016/j.ymeth.2018.05.015. Epub 2018 Jun 8. PMID: 29890285.