Impact of age-selective vs non-selective physical-distancing measures against coronavirus disease 2019: a mathematical modelling study

Abstract

Background: There is a real possibility of successive COVID-19-epidemic waves with devastating consequences. In this context, it has become mandatory to design age-selective measures aimed at achieving an optimal balance between protecting public health and maintaining a viable economic activity.

Methods: We programmed a Susceptible, Exposed, Infected, Removed (SEIR) model in order to introduce epidemiologically relevant age classes into the outbreak-dynamics analysis. The model was fitted to the official death toll and calculated age distribution of deaths in Wuhan using a constrained linear least-squares algorithm. Subsequently, we used synthetic location-specific and age-structured contact matrices to quantify the effect of age-selective interventions both on mortality and on economic activity in Wuhan. For this purpose, we simulated four different scenarios ranging from an absence of measures to age-selective interventions with stronger physical-distancing measures for older individuals.

Results: An age-selective strategy could reduce the death toll by >30% compared with the non-selective measures applied during Wuhan's lockdown for the same workforce. Moreover, an alternative age-selective strategy could allow a 5-fold increase in the population working on site without a detrimental impact on the death toll compared with the Wuhan scenario.

Conclusion: Our results suggest that age-selective-distancing measures focused on the older population could have achieved a better balance between COVID-19 mortality and economic activity during the first COVID-19 outbreak in Wuhan. However, the implications of this need to be interpreted along with considerations of the practical feasibility and potential wider benefits and drawbacks of such a strategy.

Keywords: Covid-19; SEIR; age-selective; mathematical model; physical distancing; shielding.

Figure 1

Age-dependent SEIR-model diagram. Individuals are classified as Susceptible (S), Exposed (E), Infected (I) and Removed (R). The flow between compartments is governed by the force of infection $\lambda$, the infection rate $\sigma$ and the removal rate $\gamma$. The force of infection results from the mixing between infected and susceptible individuals, which is driven by the contact matrix $\mathit{C}$ modelling the underlying social-contact patterns and by the probability of infection transmission $\tau .$

Figure 2

Wuhan age-structured location-specific contact matrices for the simulated control measures. The normal contact patterns at work, in the rest of the locations and across all locations are shown in panels (A)–(C) (Scenario 1: no physical-distancing measures), whereas panel (D) displays the total number of daily contacts per individuals of each age group. The contact matrices for the three physical-distancing scenarios considered in this work are shown in panels (E)–(P). Note the numerical differences between the scale bars for each scenario.

Figure 3

Comparison of our model against the officially reported number of deaths and age distribution. The number of deceased individuals are shown as a function of age according to the officially reported data from the National Health Commission of the People’s Republic of China distributed according to Verity et al. vs the results of our model for Scenario 2 following the fitting procedure described in this section.

Figure 4

Effect of Wuhan lockdown on the outbreak control through social-distancing measures. Outbreak dynamics in the absence of control measures (Scenario 1, (A)–(E)) vs the Wuhan lockdown (Scenario 2, (F)–(J)). Daily infected individuals per age group in 2D ((A) and (F)) and 3D ((B) and (G)) over time. (C) and (H) Aggregated daily (blue) and cumulated (orange) infected individuals over time. (D) and (I) Cumulated infections by age group. (E) and (J) Cumulated deceased individuals by age group.

Figure 5

Alternative age-specific control measures aiming at diminishing the number of deaths for the same workforce as in the Wuhan lockdown (Scenario 3, (A)–(E)) or at finding a balanced trade-off between them (Scenario 4, (F)–(J)). Daily infected individuals per age group in 2D ((A) and (F)) and 3D ((B) and (G)) over time. (C) and (H) Aggregated daily (blue) and cumulated (orange) infected individuals over time. (D) and (I) Cumulated infections by age group. (E) and (J) Cumulated deceased individuals by age group.

Figure 6

Control-measures comparison in terms of cumulated infected individuals (A), cumulated deceased individuals (B), daily contacts at work per individual by age group (C) and relative death toll vs relative workforce (D). The bars in (A) and (B) represent the age-specific total number of infected and deceased individuals per population group, respectively. (C) Daily work contacts per individual by age group. (D) Relative death toll vs the relative workforce taking the Wuhan lockdown as a reference. The same colour legend is used through (A)–(D): the Wuhan lockdown is depicted in blue, whereas the alternative control measures simulated in Scenarios 3 and 4 are shown in orange and yellow. The Wuhan lockdown obviously yields 100% in both relative parameters shown in (D), but it has been kept just as a reference.