Equitable access to COVID-19 vaccines makes a life-saving difference to all countries

Abstract

Despite broad agreement on the negative consequences of vaccine inequity, the distribution of COVID-19 vaccines is imbalanced. Access to vaccines in high-income countries (HICs) is far greater than in low- and middle-income countries (LMICs). As a result, there continue to be high rates of COVID-19 infections and deaths in LMICs. In addition, recent mutant COVID-19 outbreaks may counteract advances in epidemic control and economic recovery in HICs. To explore the consequences of vaccine (in)equity in the face of evolving COVID-19 strains, we examine vaccine allocation strategies using a multistrain metapopulation model. Our results show that vaccine inequity provides only limited and short-term benefits to HICs. Sharper disparities in vaccine allocation between HICs and LMICs lead to earlier and larger outbreaks of new waves. Equitable vaccine allocation strategies, in contrast, substantially curb the spread of new strains. For HICs, making immediate and generous vaccine donations to LMICs is a practical pathway to protect everyone.

Fig. 1. Illustration of the integrated mathematical model.

a, The multistrain model. A linear strain space and local movement by a one-direction stepwise mutation are considered. M denotes the number of possible strains; μ_m denotes the mutation probability per infection. b, The SVEIRD model. Susceptible individuals (S) become vaccinated (V) at a vaccination rate determined by the global vaccine allocation strategy. Vaccinated individuals become susceptible after losing vaccinal immunity. Exposed individuals ($E_m^S$ and $E_m^V$) are those infected by strain m and are divided into two classes, either with or without vaccinal immunity. Exposed individuals first become infectious ($I_m^S$ and $I_m^V$) and then transition to either the recovered state (R) or the deceased state (D). For simplicity, we assume that co-infection is not possible and recovered individuals are immune to the disease. c, The SVEIRD-based metapopulation model. Due to travel restrictions, infectious and deceased individuals do not move between countries.

Fig. 2. Impacts of equitable and inequitable vaccine allocation strategies on epidemic dynamics.

a–f, Time series of the prevalence (a–c) and the cumulative mortality rate (d–f) in HICs under different global vaccine allocation strategies. g–l, Time series of the prevalence (g–i) and the cumulative mortality rate (j–l) in LMICs under different global vaccine allocation strategies. Three prioritization criteria for allocation are adopted: the population size (left), prevalence (middle) and mortality rate (right). The dashed lines in each panel indicate the time when the pandemic ends if the corresponding prioritization criterion is adopted (time exceeding five years is not presented). The dashed line and the solid line referring to the same global vaccine allocation strategy are represented by the same colour. The transmissibility and severity of each strain and the vaccine efficacy against each strain are shown in Supplementary Fig. 2. Parameter values: M = 5, μ₁ = 5.6 × 10⁻³, θ = 0.2 and λ = 5 × 10² (λ is the decrease rate of the probability of emergence of new and more dangerous strains). Source data

Fig. 3. Emergence of new strains under equitable and inequitable vaccine allocation strategies.

a–c, Area plots of the fraction of daily new cases produced by different strains. d–f, The ratio between the number of new cases produced by different strains and the world population. The plots are based on the equitable (left), inequitable and χ = 0.8 (middle), and inequitable and χ = 0.9 (right) vaccine allocation strategies. All results are based on the prioritization criterion of population size. The inset in d is a zoomed-in version of d. Parameter values: M = 5, μ₁ = 5.6 × 10⁻³, θ = 0.2 and λ = 5 × 10². Source data

Fig. 4. Impacts of different allow-donation vaccine allocation strategies on epidemic dynamics.

a,c, Fraction of HICs (a) and LMICs (c) benefiting from donations. b,d, Average lives saved by vaccine donations as the share of the national population in HICs (r_H, b) and LMICs (r_L, d). Please refer to the Methods for the details of r_H and r_L. e, Fraction of HICs donating vaccines. f, Total number of donated vaccines. g,h, Prevalence in HICs (g) and LMICs (h) under different vaccine allocation strategies. The dashed lines indicate the time when the pandemic ends. Countries with larger population sizes are prioritized for vaccination. Parameter values: M = 5, μ₁ = 5.6 × 10⁻³, θ = 0.2 and λ = 5 × 10². Source data

Fig. 5. Impact of donating vaccines to neighbouring LMICs.

a–d, A country (the black node) and its 1-hop (a), 2-hop (b), 3-hop (c) and 4-hop (d) neighbours on the global mobility network (Supplementary Fig. 27) constructed on the basis of the air traffic data. e–j, Cumulative mortality rate in HICs (e–g) and LMICs (h–j) over time if HICs donate vaccines to only their 1-hop, 2-hop, 3-hop and 4-hop LMIC neighbours under scenarios where δ = 0.46 and I_thre = 8 × 10⁻⁵ (e and h), δ = 0.6 and I_thre = 6 × 10⁻⁵ (f and i), and δ = 0.8 and I_thre = 4 × 10⁻⁵ (g and j). The dashed lines indicate the time when the pandemic ends. Countries with larger population sizes are prioritized for vaccination. Parameter values: M = 5, μ₁ = 5.6 × 10⁻³, θ = 0.2 and λ = 5 × 10². Source data