Impact of asymptomatic COVID-19 carriers on pandemic policy outcomes

Abstract

This paper provides a mathematical model that makes it clearly visible why the underestimation of r, the fraction of asymptomatic COVID-19 carriers in the general population, may lead to a catastrophic reliance on the standard policy intervention that attempts to isolate all confirmed infectious cases. The SE(A+O)R model with infectives separated into asymptomatic and ordinary carriers, supplemented by a model of the data generation process, is calibrated to standard early pandemic datasets for two countries. It is shown that certain fundamental parameters, critically r, are unidentifiable with this data. A general analytical framework is presented that projects the impact of different types of policy intervention. It is found that the lack of parameter identifiability implies that some, but not all, potential policy interventions can be correctly predicted. In an example representing Italy in March 2020, a hypothetical optimal policy of isolating confirmed cases that aims to reduce the basic reproduction number R ₀ of the outbreak from 4.4 to 0.8 assuming r = 0, only achieves 3.8 if it turns out that r = 40%.

Keywords: COVID-19; Infectious disease model; Non-pharmaceutical intervention; Pre-symptomatic; SIR model.

Fig. 1

Simulation and official data of daily new cases (DNC) for Italy and Canada in the pre-policy calibration period [T₁, T₂ + 5].

Fig. 2

ITALY: The model prediction showing the effect of the maximal isolation policy if implemented at the policy time T₂ (March 10, 2020). Here we fix the pre-policy ratio ρ = α^A/α^O = 4, the transmission reduction factor f = 0.9 and the confirmation factor φ^O = 0.9, and plot over the period [T₁, T₃] for varying r. The first graph shows the logarithm of the actual cumulative cases consisting of all symptomatic and asymptomatic infections plus removed cases. The second graph shows the logarithm of the confirmed daily new cases.

Fig. 3

ITALY: The model prediction showing the effect of the maximally protective garments if implemented at the policy time T₂ (March 10, 2020). Here we fix the policy factors v₃ = 0.85, e₃ = 0.95, and show that the pandemic curve over the period [T₁, T₃] is independent of r. These graphs are independent of ρ. The first graph shows the logarithm of the actual cumulative cases consisting of all symptomatic and asymptomatic infections plus removed patients. The second graph shows the logarithm of the confirmed daily new cases.

Fig. 4

The effect of isolation and protective garments compared: These graphs show the logarithm of the active confirmed cases in Italy at time T₃ corresponding to April 20, 2020. The left graph shows the dependence on the effort expended on isolation, v₁e₁ ∈ [0, 1], and on the asymptomatic rate r ∈ [0, 0.6]. The right graph shows the dependence on the effort expended on protective garments, v₃e₃ ∈ [0, 1], and on the asymptomatic rate r ∈ [0, 0.6] when φ = 1 and ρ = 4.

Fig. 5

This plot shows the effective R_0p after policy date T₂ in Italy as a function of e₁, e_GPP, when isolation is applied with effort e₁ and v₁ = 0.8, and general personal protection is applied with effort e_GPP and v_GPP = 0.91. The darkness of the red colour denotes the value of R_0p. Plot (a) shows results when r = 40%, (b) assumes r = 10%; both plots have ρ = 4.