Bidirectional contact tracing could dramatically improve COVID-19 control

Abstract

Contact tracing is critical to controlling COVID-19, but most protocols only "forward-trace" to notify people who were recently exposed. Using a stochastic branching-process model, we find that "bidirectional" tracing to identify infector individuals and their other infectees robustly improves outbreak control. In our model, bidirectional tracing more than doubles the reduction in effective reproduction number (R_eff) achieved by forward-tracing alone, while dramatically increasing resilience to low case ascertainment and test sensitivity. The greatest gains are realised by expanding the manual tracing window from 2 to 6 days pre-symptom-onset or, alternatively, by implementing high-uptake smartphone-based exposure notification; however, to achieve the performance of the former approach, the latter requires nearly all smartphones to detect exposure events. With or without exposure notification, our results suggest that implementing bidirectional tracing could dramatically improve COVID-19 control.

Fig. 1. Forward-only and bidirectional contact tracing and digital exposure notification.

a Notifying people exposed to known cases (black) and isolating them (green) can prevent further transmission, but will miss asymptomatic and undiagnosed cases (gray) and descendants. b Bidirectional tracing also notifies and tests potential infectors, enabling isolation of additional cases. c Manual contact tracing requires individuals to share recent contacts with health authorities. d In digital exposure notification, smartphones broadcast rotating pseudorandom “chirps” and record those emitted by nearby devices. e Individuals diagnosed with COVID-19 can “opt-in” by uploading broadcasted chirps to a diagnosis server. All devices frequently check the server and alert the user if the calculated exposure exceeds a threshold set by the local health authority. In hybrid manual+digital systems, human tracers would seek to identify contacts without smartphones.

Fig. 2. Comparing forward and bidirectional contact tracing at R₀ = 2.5.

a Mean R_eff achieved by manual tracing with 2-day and 6-day manual tracing windows. b Neighbor-averaged contour plot, showing mean R_eff achieved by bidirectional manual tracing as a function of the probability of trace success and the width of the manual tracing window. c Neighbor-averaged contour plot, depicting R_eff achieved by digital exposure notification in the absence of manual tracing as a function of smartphone coverage and data-sharing, assuming 90% probability of trace success. d Mean R_eff achieved using hybrid manual+digital tracing, assuming 90% data sharing and 53% or 80% smartphone coverage. e, f Neighbor-averaged contour plots, depicting R_eff achieved by hybrid tracing as a function of smartphone coverage and data-sharing, assuming 90% probability of trace success and a e 2-day or f 6-day manual tracing window. All panels assume median disease parameters (Table 1). “Probability of trace success” refers to trace attempts that are not otherwise blocked by environmental transmission or fragmentation of the digital network.

Fig. 3. Bidirectional tracing under reduced ascertainment and test sensitivity.

a Mean R_eff achieved by different tracing strategies as a function of the percentage of symptomatic cases that can be identified by health authorities on the basis of symptoms alone, assuming 70% test sensitivity. b Mean R_eff achieved by different tracing strategies as a function of test sensitivity, assuming 50% ascertainment of symptomatic cases. c As in (b), but allowing contact tracing to be initiated from symptomatic cases on the basis of symptoms alone (i.e., without a positive test result). All panels assume 90% probability of trace success, a 6-day manual tracing window, high (80%) smartphone coverage, and median disease parameters (Table 1). Isolation on the basis of symptoms can dampen the outbreak even in the absence of tracing.

Fig. 4. Effect of R₀ and disease parameters on performance.

(top row) Mean R_eff achieved and (bottom row) mean % of outbreaks controlled by different tracing strategies as a function of the basic reproduction number R₀, assuming (left) median, (middle) optimistic or (right) pessimistic disease parameters (Table 1), assuming 50% ascertainment of symptomatic cases, 70% test sensitivity, 90% probability of trace success, a 6-day manual tracing window, and high (80%) smartphone coverage (Table 1). Error bars in the bottom row represent 95% credible intervals across 1000 runs under a uniform beta prior. Isolation on the basis of symptoms can dampen the outbreak at low R₀, even in the absence of tracing.

Fig. 5. Performance of different tracing strategies relative to current practice.

Mean effective reproduction number obtained under (left) median, (middle) optimistic, and (right) pessimistic scenarios (Table 1), assuming an R₀ of 2.5 and a 90% baseline probability of trace success across 1000 runs. Blue double dagger symbols indicate conditions roughly corresponding to current practice in most regions. Low and high uptake correspond to 53% and 80% of cases, respectively, having chirp-enabled smartphones. Without tracing, forward and bidirectional are equivalent.