The Mobile Health Moment: Understanding Mobility Patterns and Infection Control
Infectious diseases affect billions of people each year and are one of the leading causes of death worldwide, accounting for an estimated 14.7 million deaths per year – this is equivalent to roughly one third of all deaths in 2001 (Bloom and Cadarette 2019). The current COVID-19 outbreak has managed to infect more people and in a significantly shorter amount of time than the closely related 2003 SARS coronavirus outbreak (Shi et al 2020).
Some of the key contributing factors have been the growth in people’s ability to travel both locally and internationally, as well as the significant rise in human migration. International travel has increased from 25.9 million person trips in 2009 to 38.9 million in 2019 (IATA 2020). In Wuhan for example, it is estimated that over 5 million residents left the city before the travel ban on 23 January 2020 (Chen et al 2020).
Understanding human mobility patterns thus has to be a core focus in research, policy or clinical pursuits related to infectious disease management. However, as evidenced by the current COVID-19 pandemic, implementation of infectious disease management measures can present societal and communication challenges, resulting in responses ranging from denial and protest to the implementation of blunt measures to community uncertainty on the best ways to prevent and manage infection. Transmission and the ability of an epidemic/pandemic to establish and spread in a population is influenced by both pathogen-specific factors and human population-level factors. For instance, susceptibility to infection, population density and patterns of movement of people (both globally and locally), coupled with public health interventions are all considered key variables (Madhav et al. 2017).
Interventions to reduce the risk of transmission due to the movement of people and their contacts include modification of behaviour, use of protective equipment, limitations of contacts, treatment, and prophylaxis to minimise shedding. Unfortunately, many of these interventions are challenging to effectively implement, measure and monitor at individual and community levels with traditional methods and technologies. The use of mobile and wireless technologies to support the achievement of health objectives (mHealth) has clear potential to enhance these interventions and transform the field of infectious disease prevention and management across the globe. New technologies, particularly those that are able to offer improved insights into people’s movement and behaviour will be critical to support effective management of infectious disease.
The movement and behaviour of people determines, in part, the rate at which a contagion is spread, making tracking and moderating movement a critical containment lever. Importantly, infectious diseases can be particularly problematic and difficult to contain when they feature asymptomatic and pre-symptomatic transmission (as is the case with COVID-19). As a result, transmission can actively occur in the community without awareness on the part of the infectious host. Leading indicators are therefore needed to help individuals, organisations and communities make evidence-based and dynamic decisions to help prevent and manage the spread of infectious diseases.
This is in contrast with traditional approaches that rely on ‘after-the-fact’ methods such as contact tracing, which (despite their clear and fundamental role) are by definition lag indicators given the incubation and pre-symptomatic periods that characterise infectious diseases. As such, reliance on ‘after-the-fact’ methods should not be the sole area of focus.
Advanced mHealth technologies can provide the necessary leading indicators in the form of, for example, risk scores at the individual level that could be based on the interaction (in time and space) of each person with hotpots of potential transmission. The factors to be considered should include the type of locations visited, the length of time spent at those locations, the purpose of those visits, the time of day and day of the week of each event, and the routes and forms of travel used. In short, detailed understanding of people’s movement can help infer individual and community transmission risk.
Such technologies could also include recommendation engines to suggest behaviour change alternatives that reduce the time spent in ‘hotspots’ (e.g., visiting an area with high levels of community transmission and detected active cases, as well as high concentrations of people) and safe travel options that concurrently minimise exposure risk and maximise physical activity. A recent international review of the application of mHealth technology to mitigate the effects of the COVID-19 pandemic concluded that mHealth solutions are well-suited to enable the creation of dynamic risk management solutions that empower individuals to make safer personal choices and organisations to proactively influence workforce and community decisions (Adans-Dester et al 2020).
In summary, mHealth solutions that accurately understand people’s movement and behavioural patterns, as well as those of their contacts, and proximity to high risk transmission environments (at the time when transmission is most likely) should be included in the tool set for health systems. The underlying technologies already exist and their incorporation into mHealth digital platforms can offer significant benefits for the prevention and management of infection risk.
Furthermore, if these technologies can be used to provide safe personalised movement and behaviour recommendations for individuals that are clear, logical and easy to action, they will help to reduce people’s level of anxiety, uncertainty and mental health concerns. However, consumer trust and interaction with these technologies may pose a barrier to adoption and need to be addressed to promote effective implementation. To alleviate these concerns, mHealth technologies need to ensure that advice provided to individuals is personalised, as people respond better and are more likely to engage when messaging is specific to their individual circumstances and preferences.
Jose Mantilla
Founder, Motus Science
David Dembo
mHealth Leader, Motus Science
Simon Tucker
Infectious Disease Scientist, Motus Science
References
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6447676/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7228706
https://www.iata.org/en/iata-repository/pressroom/fact-sheets/fact-sheet—industry-statistics
https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30421-9/fulltext
