Combatting SARS-CoV-2 With Digital Contact Tracing and Notification: Navigating Six Points of Failure

Effectiveness of digital contact tracing interventions for COVID-19: A systematic scoping review

OBJECTIVES

Digital contact tracing (DCT) interventions have been deployed at unprecedented scale during COVID-19. However, no comprehensive appraisal of the evidence exists to date regarding their effectiveness. We aimed to systematically review the global literature for a holistic understanding of DCT effectiveness during COVID-19, and to identify factors that enabled or hindered its effectiveness.

STUDY DESIGN

Systematic scoping review.

METHODS

We searched six databases for peer-reviewed literature relevant to the evaluation of DCT interventions during COVID-19 (January 2024) (CRD42021268586). We compiled implemented DCT interventions from grey literature. Effectiveness appraisals, different operationalizations, measurements, and definitions of DCT effectiveness, as well as associated factors were synthesized qualitatively. Study quality was assessed using the Mixed Methods Appraisal Tool. We followed Cochrane and PRISMA guidance.

RESULTS

We identified 133 studies evaluating 121 different DCT implementations. Seventy-three (60 %) studies found DCT to be effective, mostly when evaluating epidemiological impact metrics. Public trust emerged as crucial for DCT to be effective, which requires high and enforceable data safety and privacy standards, clear and transparent communication, high accuracy and reliability of the intervention, and an acceptance-enhancing implementation approach of other pandemic response measures by public health authorities more broadly. Most evaluations took place in high rather than low-resource settings.

CONCLUSION

While technical performance matters, DCT effectiveness primarily depends on a relatively small number of non-technical drivers centred around public trust. DCT should only be implemented as integrated part of a broader public health framework. Our findings hold important insights for the design, implementation, and evaluation of other digital technology for pandemic response.

Pandemic preparedness: On the efficacy of non-pharmaceutical interventions in COVID-19 and about approaches to predict future pandemic viruses

With three major viral pandemics over the last 100 years, namely the Spanish flu, AIDS and COVID-19 each claiming many millions of lives, pandemic preparedness has become an important issue for public health. The economic, social and political consequences of the upheaval caused by such pandemics also represent a major challenge for governments with respect to sustainable development goals. The field of pandemic preparedness is vast and the current article can only address selected aspects. The article looks first backwards and addresses the question of the efficacy of non-pharmaceutical interventions (NPI) on the trajectory of the COVID-19 pandemic. The article looks then forward by asking to what extent viral candidates for future pandemics can be predicted by virome analyses from metagenome and transcriptome sequencing, by focusing on the virome from specific animal species and using ecological and epidemiological data about spillover viral infections in veterinary and human medicine. As a comprehensive overview on pandemic preparedness is beyond the capacity of a single reviewer, only selected topics will be discussed using recent key scientific publications. Since COVID-19 has not run its course, a computational program able to predict the future evolution of SARS-CoV-2 is mentioned that could assist proactive mRNA vaccine developments against possible future variants of concern. Ending the COVID-19 epidemic necessitates mucosal vaccines that can suppress the transmission of SARS-CoV-2 and therefore this article closes by discussing a promising and versatile protein nanoparticle experimental vaccine approach for inhalation that does not depend on needles nor a cold chain for distribution.

Epidemic exposure risk assessment in digital contact tracing: A fuzzy logic approach

Background

Bluetooth low energy (BLE)-based contact-tracing applications were widely used during the COVID-19 pandemic. However, the use of only the received signal strength feature for proximity calculations may not be adaptable to different virus variants or scalable for other potential epidemic diseases.

Objective

This study presents a novel framework in regard to evaluating and classifying personal exposure risk that considers both contact features, which include distance and length of contact, and environment features, which include crowd size and the number of recently infected cases in the environment. The framework utilizes a fuzzy expert system that is adaptable to different virus variants.

Methods

The proposed method was tested on two viruses with different close contact features, which used four membership functions and 256 fuzzy rule sets.

Results

The proposed framework classified personal exposure risks into four classes, which include low, medium, high, and too high risk. The empirical results showed that the fuzzy logic-based approach reduced the number of false positive cases and demonstrated better accuracy and precision than the current BLE-only approaches.

Conclusions

The proposed framework provides a more practical and adaptable method in regard to assessing exposure risks in real-world scenarios. It has the potential to be scalable and adaptable to different virus variants and other potential epidemic diseases by considering both contact and environment features. These findings may be useful in order to develop more effective digital contact-tracing applications and policies.