JMIR Publications

ABSTRACT

Background:

In December 2019 a novel corona virus (SARS-CoV-2) appeared and rapidly spread around the world causing the disease COVID-19. To contain the virus, much hope is placed on participatory surveillance using mobile applications, such as automated digital contact tracing. However, the broad adoption of such technologies is considered an important prerequisite for associated interventions to be effective. Data security and privacy aspects are a critical factor for adoption and privacy risks of solutions developed often need to be balanced against their functionalities. This is reflected by an intensive discussion in the public and the scientific community about privacy-preserving approaches.

Objective:

Our objective is to inform the current discussion and to support the development of solutions providing an optimal balance between privacy protection and pandemic control. To this end, we present a systematic analysis of existing literature on citizen-centered surveillance solutions collecting individual-level spatial data. Our main hypothesis was that there are dependencies between the following dimensions: (1) the specific diseases focused on, (2) use cases supported, (3) technology utilized to collect spatial data and (4) data protection measures implemented.

Methods:

We searched PubMed and IEEE Xplore with a search string combining terms from the area of infectious disease management with terms describing spatial surveillance technologies to identify studies published between 2010 and 2020. After a two-step eligibility assessment process, 27 articles were selected for final analysis. We collected data on the four dimensions described as well as metadata, which we then analyzed by calculating univariate and bivariate frequency distributions.

Results:

We identified four different use cases, which focused on individual surveillance and public health (most common: digital contact tracing). We found that the solutions described were highly specialized with 89% of the articles covering one use case only. Moreover, we identified eight different technologies utilized for collecting spatial data (most common: Global Positioning System, or GPS, receivers) and five different diseases covered (most common: COVID-19). Finally, we also identified six different data protection measures (most common: pseudonymization). As hypothesized, we revealed relationships between the dimensions. We found that for highly infectious diseases such as COVID-19 the most common use case is automated contact tracing based on relative positions (i.e. contacts) typically based on Bluetooth technology. For managing vector-borne diseases, use cases require absolute positions, which are typically measured using GPS. However, absolute spatial data is also important for additional use cases relevant to the management of other infectious diseases.

Conclusions:

We see a large potential for future solutions supporting multiple use cases by combining different technologies (e.g. Bluetooth and GPS). For this to be successful, however, adequate privacy-protection measures must be implemented. Technologies currently utilized along this axis do likely not offer enough protection. We therefore recommend that future solutions should consider the use of modern privacy-enhancing techniques, e.g. from the area of secure multiparty computing and differential privacy.