In a recently published study in natural biotechnologyresearchers evaluated the role of smartphone apps during the 2019 coronavirus disease (COVID-19) pandemic.
Smartphone apps have been widely used for tracing, tracking and educating the public about COVID-19. While there are major concerns about privacy and data security, this points to the usefulness of apps in understanding infection outbreaks, individual screening and contact tracing.
In the present study, researchers reviewed and evaluated large digital app projects according to outbreak epidemiology, individual screening, and contact tracing.
The team divided the COVID-19 epidemiology into (1) monitoring of active user participants, (2) population-level tracking of passive users, (3) individual risk assessment, and (4) viral disease prediction. Participatory surveillance was conducted using telephone and text-based surveys to obtain syndromic surveillance data in locations where web-based applications were not available.
Various syndrome reporting platforms, including Flu Near You in the US, InfluenzaNet in Europe, and Reporta in Mexico, enabled citizen scientists to report flu-like symptoms on a reporting platform that is either web-based or app-based. Such reporting has shown promise in correlating the timing and magnitude of viral disease activity.
Because there was a significant overlap between COVID-19 and influenza symptoms, several of the above apps also tracked COVID-19. Another app-based platform from Brazil obtained syndromic data from a total of 861 participants and found that the data collected matched the temporal and spatial trends observed with traditional COVID-19 surveillance methods. This platform also identified communities to prioritize for testing and improved surveillance in regions without health facilities.
Passive crowdsourcing of outbreak data from social media, web queries, and large-scale data generated by lay media could provide earlier warning signals than traditional surveillance methods. Healthmap’s Outbreaks Near Me platform monitored, organized and visualized the place and time when the infectious disease outbreak was reported globally via electronic media. This enabled near real-time visualization and identification of clusters of infection cases reported by the media in a region, helping public health responders spot new outbreaks faster than traditional measures.
Individual risk assessment
Several apps, including the Safer Covid app, provided users with individual risk information, taking into account age, activity type and location. The health code monitoring app from China categorized people into three classifications according to their risk level based on mining location, contact details and payment platform. Individuals belonging to the high-risk categories have been denied access to certain public places, transportation systems and buildings. Such individual risk assessments could also improve the use of non-pharmaceutical interventions (NPIs), including mask wearing, increased testing, social distancing or stay-at-home measures.
Individual screening with symptom checkers
The symptom checker apps have been divided into active or passive depending on the need for user interaction.
These apps required frequent active interaction with the app as the participant regularly reported symptoms. Constant reliance on user reports led to survey fatigue, resulting in smaller than expected sample sizes, dwindling user engagement, and participant bias. These factors limited the app’s ability to draw meaningful conclusions about local trends related to COVID-19 infections.
Passive screening obtained data from wearables used by participants to detect COVID-19 or other viral diseases. Such screening required minimal user involvement. Initial studies demonstrated the potential of these apps for understanding ambulatory physiology and identifying subclinical forms of the viral disease. A study app called Digital Engagement and Tracking for Early Control and Treatment (DETECT) used a hybrid active and passive approach using data collected by Fitbit or another wrist sensor linked to data from either Google Fit or Apple HealthKit was, along with symptom questionnaires.
The team found that analysis of resting heart rate (RHR) in symptomatic COVID-19 patients in the DETECT cohort showed an average initial increase in RHR followed by transient bradycardia. This was followed by a sustained relative tachycardia, which resolved nearly three months after symptom onset.
A University of Oxford study has shown that contact tracing could potentially mitigate COVID-19 outbreaks. Smartphones made contact tracing possible due to their ability to detect proximity between people using technologies such as Bluetooth low energy systems. In addition, global positioning systems, internet protocol addresses, proximity to cell towers, and international identification numbers for mobile devices could enable geolocation of specific individuals.
According to the authors, further research is essential to examine the effectiveness of COVID-19 apps.