A model-based evaluation of the efficacy of COVID-19 social distancing, testing and hospital triage policies

Abstract

A stochastic compartmental network model of SARS-CoV-2 spread explores the simultaneous effects of policy choices in three domains: social distancing, hospital triaging, and testing. Considering policy domains together provides insight into how different policy decisions interact. The model incorporates important characteristics of COVID-19, the disease caused by SARS-CoV-2, such as heterogeneous risk factors and asymptomatic transmission, and enables a reliable qualitative comparison of policy choices despite the current uncertainty in key virus and disease parameters. Results suggest possible refinements to current policies, including emphasizing the need to reduce random encounters more than personal contacts, and testing low-risk symptomatic individuals before high-risk symptomatic individuals. The strength of social distancing of symptomatic individuals affects the degree to which asymptomatic cases drive the epidemic as well as the level of population-wide contact reduction needed to keep hospitals below capacity. The relative importance of testing and triaging also depends on the overall level of social distancing.

Fig 1. Graphical model summary.

(A) Example of the two-layer interaction network used in this study. The private, small-world network (red edges) is shown on top of the public, fully-connected network (gray edges). Low-risk (blue circles) and high-risk (black squares) individuals are distinguished. (B) Illustration of the stochastic transmission model with compartments S = susceptible, E = exposed, A = asymptomatic, I = symptomatic, H = hospitalized, R = recovered, D = deceased. Individuals in I and H may receive a positive test (green “tested” oval). Edges that are influenced by policy decisions are colored: red = social distancing, green = testing, blue = hospital triage. Branching probabilities at E, I and H are risk-group dependent and the edge of the respectively more likely transition is thicker. (C) Illustration of the time-dependent transmission rate of an exposed individual increasing until peak viral shedding, which coincides with transition to compartment I (if symptomatic) or A (otherwise). (D) Average care per person (blue solid line) and total care provided (red dashed line) by a health care system with a capacity threshold of 100% operating at a certain level of (over)capacity. Once the capacity threshold is reached, the average care per person is $1/\sqrt{\text{hospital}\text{capacity}}$.

Fig 2. Impact of social distancing on COVID-19-related mortality.

Effect of population-wide private and public contact reduction on the average proportion dead (A, red) and the average percentage of averted deaths (B, green). The latter is computed by comparison with no contact reduction. Results are shown for communities with k private and k public average contacts per day, for different values of k.

Fig 3. Impact of asymptomatic cases.

(A) Effect of the proportion of asymptomatic cases (y-axis) and the contact reduction by symptomatic cases (x-axis) on the percentage of infections caused by asymptomatic cases. (B) Effect of two unknowns on the proportion dead: the proportion of asymptomatic cases (y-axis), and their relative transmission rate (compared to that of symptomatic; x-axis). Two scenarios are considered: low (< 10%; left panel) and high (> 90%; right panel) contact reduction by symptomatic individuals.

Fig 4. Peak hospital capacity and the effect of triaging policies.

(A) Effect of population-wide contact reduction and additional contact reduction of symptomatic individuals on the projected peak hospital capacity. To the left of the hospital capacity threshold (red line), not all hospitalized individuals receive perfect care, resulting in longer recovery times and higher mortality. (B) Relative efficacy of four hospital triage policies at varying degrees of outbreak severity summarized by the average proportion of deaths (x-axis). Efficacies are computed by pairwise comparison of the projected death count with the imperfect-but-equal-care scenario.

Fig 5. Impact of testing policies on COVID-19-related mortality.

(A) Effect of increased numbers of tests (x-axis) and processing delays (y-axis) on the average percentage of averted deaths (compared to no testing; left-most column). (B) Effect (linear regression) of the average contact reduction by individuals with positive test results on the percentage of averted deaths under an efficient testing scenario (blue box lower right in A) and an inefficient testing scenario (orange box upper left in A). (C) Impact of policies regarding testing prioritization of symptomatic individuals on the average percentage of averted deaths (compared to the worst policy). The primary policy decision involves which risk group to prioritize (low-risk (green) or high-risk (orange)). The secondary policy decision involves who to test first within each risk group (newly symptomatic (solid lines) or first-symptomatic (dashed lines)). The results are stratified for three different levels of additional contact reduction due to symptoms (subplots) as well as for varying levels of additional contact reduction of high-risk individuals (x-axis). Linear regression fits are shown.

Fig 6. Interactions of the three policy domains on COVID-19-related deaths.

The level of social contact reduction (x-axis) is plotted against combinations of testing policies (solid lines, gold: maximum testing, blue: no testing) and triage policies (dashed lines, circles: treat least severely infected first; solid lines, crosses: imperfect but equal care). Y-values are averages across 1,000 parameter choices with 100 replicates each. (A) Policy choices versus proportion dead (y-axis). (B) Relative effects of policy choices in reducing the proportion dead (y-axis), compared to the worst-case scenario of no testing and imperfect-but-equal-care triage policy.