Universal Shelter-in-Place Versus Advanced Automated Contact Tracing and Targeted Isolation

Abstract

Objective: To model and compare effect of digital contact tracing versus shelter-in-place on severe acute respiratory syndrome e coronavirus 2 (SARS-CoV-2) spread. Methods: Using a classical epidemiologic framework and parameters estimated from literature published between February 1, 2020, and May 25, 2020, we modeled two non-pharmacologic interventions d shelter-in-place and digital contact tracing d to curb spread of SARS-CoV-2. For contact tracing, we assumed an advanced automated contact tracing (AACT) application that sends alerts to individuals advising self-isolation based on individual exposure profile. Model parameters included percentage population ordered to shelter-in-place, adoption rate of AACT, and percentage individuals who appropriately follow recommendations. Under influence of these variables, the number of individuals infected, exposed, and isolated were estimated. Results: Without any intervention, a high rate of infection (>10 million) with early peak is predicted. Shelter-in-place results in rapid decline in infection rate at the expense of impacting a large population segment. The AACT model achieves reduction in infected and exposed individuals similar to shelterin-place without impacting a large number of individuals. For example, a 50% AACT adoption rate mimics a shelter-in-place order for 40% of the population and results in a greater than 90% decrease in peak number of infections. However, as compared to shelter-in-place, with AACT significantly fewer individuals would be isolated. Conclusion: Wide adoption of digital contact tracing can mitigate infection spread similar to universal shelter-in-place, but with considerably fewer individuals isolated.

Digital contact tracing, community uptake, and proximity awareness technology to fight COVID-19: a systematic review

“…The model indicated that a 61% uptake of the app would reduce the total number of new cases by almost 50%. Another study estimated that a 50% adoption rate of digital contact tracing results in a 90% decrease in the peak number of infections ( Nuzzo et al, 2020 ). López et al (2021) calibrated an agent-based model on the French population integrating demographic and social-contact data simulated how digital contact tracing impacted COVID-19 transmission.…”

“…Most of the studies sufficiently presented the applied models, and the related assumptions and parameters. However, some studies reported briefly or not at all the choice of the model ( Nakamoto et al, 2020 ; Nuzzo et al, 2020 ), the model assumptions ( Currie et al, 2020 ; Nakamoto et al, 2020 ; Nuzzo et al, 2020 ), the study parameters ( Barrat et al, 2020 ; Currie et al, 2020 ; Kim & Paul, 2021 ; Nakamoto et al, 2020 ; Nuzzo et al, 2020 ; Yasaka et al, 2020 ). Some studies did not describe the characteristics of the base-case population ( Bradshaw et al, 2021 ; Bulchandani et al, 2020 ; Currie et al, 2020 ; Ferretti et al, 2020 ; Kim & Paul, 2021 ; Kretzschmar & Rozhnova, 2020 ; Nakamoto et al, 2020 ; Nuzzo et al, 2020 ; Plank et al, 2020 ; Pollmann et al, 2020 ; Wilmink et al, 2020 ; Xia & Lee, 2020 ; Yasaka et al, 2020 ), while other studies described in a limited way the analytical methods that supported the evaluation of the results ( Currie et al, 2020 ; Kim & Paul, 2021 ; Kretzschmar & Rozhnova, 2020 ; Nuzzo et al, 2020 ; Plank et al, 2020 ; Wilmink et al, 2020 ; Yasaka et al, 2020 ).…”

Digital Contact Tracing Based on a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic: Case Study

“…Digital contact tracing uses electronic information to identify exposures to infection; it has the potential to address the limitations of traditional contact tracing, such as scalability, notification delays, recall errors, and contact identification in public spaces [ 10 , 11 ]. Current digital technology enables the continuous tracing of individuals and locations using mobile phones, GPS, wireless fidelity (Wi-Fi), and Bluetooth to record and trace the spatiotemporal trajectory of individuals to identify potential contacts [ 12 , 13 ]. These systems can help overcome the limitations of manual contact tracing [ 12 ].…”

“…Current digital technology enables the continuous tracing of individuals and locations using mobile phones, GPS, wireless fidelity (Wi-Fi), and Bluetooth to record and trace the spatiotemporal trajectory of individuals to identify potential contacts [ 12 , 13 ]. These systems can help overcome the limitations of manual contact tracing [ 12 ]. An empirical study proved that the widespread use of digital contact tracing can reduce the spread of epidemics, and far fewer individuals are placed in isolation when such systems are applied [ 12 ].…”

“…These systems can help overcome the limitations of manual contact tracing [ 12 ]. An empirical study proved that the widespread use of digital contact tracing can reduce the spread of epidemics, and far fewer individuals are placed in isolation when such systems are applied [ 12 ].…”