Comparative Impact of Individual Quarantine vs. Active Monitoring of Contacts for the Mitigation of COVID-19: a modelling study

Abstract

Background: Voluntary individual quarantine and voluntary active monitoring of contacts are core disease control strategies for emerging infectious diseases, such as COVID-19. Given the impact of quarantine on resources and individual liberty, it is vital to assess under what conditions individual quarantine can more effectively control COVID-19 than active monitoring. As an epidemic grows, it is also important to consider when these interventions are no longer feasible, and broader mitigation measures must be implemented.

Methods: To estimate the comparative efficacy of these case-based interventions to control COVID-19, we fit a stochastic branching model to reported parameters for the dynamics of the disease. Specifically, we fit to the incubation period distribution and each of two sets of the serial interval distribution: a shorter one with a mean serial interval of 4.8 days and a longer one with a mean of 7.5 days. To assess variable resource settings, we consider two feasibility settings: a high feasibility setting with 90% of contacts traced, a half-day average delay in tracing and symptom recognition, and 90% effective isolation; and low feasibility setting with 50% of contacts traced, a two-day average delay, and 50% effective isolation.

Findings: Our results suggest that individual quarantine in high feasibility settings where at least three-quarters of infected contacts are individually quarantined contains an outbreak of COVID-19 with a short serial interval (4.8 days) 84% of the time. However, in settings where this performance is unrealistically high and the outbreak continues to grow, so too will the burden of the number of contacts traced for active monitoring or quarantine. When resources are prioritized for scalable interventions such as social distancing, we show active monitoring or individual quarantine of high-risk contacts can contribute synergistically to mitigation efforts.

Interpretation: Our model highlights the urgent need for more data on the serial interval and the extent of presymptomatic transmission in order to make data-driven policy decisions regarding the cost-benefit comparisons of individual quarantine vs. active monitoring of contacts. To the extent these interventions can be implemented they can help mitigate the spread of COVID-19.

Keywords: coronavirus; disease control; emerging infectious disease; isolation; nonpharmaceutical interventions; outbreak; symptom monitoring.

Figure S1:. Parameters fit to serial interval scenario 1

Univariate histograms and bivariate heatmaps for each of three input parameters in serial interval scenario 1: the time offset between the latent and incubation periods (T_OFFSET); maximum duration of infectiousness (d_INF); and time of relative peak infectiousness (β_τ). Convergence by sequential monte carlo (SMC) in iteration 7 with median Kolmogorov-Smirnov test statistic KS = 0.116.

Figure S2:. Parameters fit to serial interval scenario 2

Univariate histograms and bivariate heatmaps for each of three input parameters in serial interval scenario 2: the time offset between the latent and incubation periods (T_OFFSET); maximum duration of infectiousness (D_INF); and time of relative peak infectiousness (β_τ). Convergence by sequential monte carlo (SMC) in iteration 7 with median Kolmogorov-Smirnov test statistic KS = 0.066.

Figure 1:. Simulated daily growth of infections and individuals under quarantine

Daily count of cumulative infections (red), truly infected contacts currently under quarantine (blue), uninfected contacts currently under quarantine assuming 1:1 ratio of uninfected to infected contacts traced (dark green, p=0.5), and uninfected contacts currently under quarantine assuming 9:1 ratio of uninfected to infected contacts traced (green, p=0.1). Model assumes interventions begin at a cumulative case count of 1,000, a low feasibility setting, R₀ = 2.2, and a mean serial interval of 4.8 days.

Figure 2:. Effective reproductive number under active monitoring and individual quarantine

The effective reproductive number under active monitoring (x axis) and individual quarantine (y axis) increases with the basic reproductive number (colors) and in low feasibility settings (squares) compared to high feasibility settings (triangles) in serial interval scenario 1 (A) and scenario 2 (B). Equivalent control under individual quarantine and active monitoring would follow the y=x identify line.

Figure 3:. Impact of presymptomatic infectiousness on effective reproductive number

The effective reproductive number under active monitoring (yellow) and individual quarantine (blue) decreases as the extent of onset of infectiousness gets later with respect to the onset of symptoms in a high feasibility setting holding R₀ constant at 2.2. An offset of −2 days indicates infectiousness precedes symptoms by 2 days, an offset of 0 days indicates onset of both simultaneously, and an offset of 1 day indicates infectiousness onset occurs 1 day after symptom onset.

Figure 4:. Impact of proportion of contacts traced on effective reproductive number

The effective reproductive number under active monitoring (yellow) and individual quarantine (blue) increases as the proportion of contacts traced decreases, assuming a mean serial interval of 4.8 days, and R₀ = 2.2. Intervention parameters other than fraction of contacts traced are set to the high feasibility setting

Figure 5:. Synergistic effect of social distancing and interventions targeted by contact tracing

Active monitoring (yellow) and individual quarantine (blue) of 10%, 50%, and 90% of contacts provide an incremental benefit over social distancing for serial interval scenario 1. Intervention parameters other than the fraction of contacts traced are set to the high feasibility setting.