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Abstract
Access to COVID-19 vaccines on the global scale has been drastically hindered by structural socio-economic disparities. Here, we develop a data-driven, age-stratified epidemic model to evaluate the effects of COVID-19 vaccine inequities in twenty lower middle and low income countries (LMIC) selected from all WHO regions. We investigate and quantify the potential effects of higher or earlier doses availability. In doing so, we focus on the crucial initial months of vaccine distribution and administration, exploring counterfactual scenarios where we assume the same per capita daily vaccination rate reported in selected high income countries. We estimate that more than 50% of deaths (min-max range: [54−94%]) that occurred in the analyzed countries could have been averted. We further consider scenarios where LMIC had similarly early access to vaccine doses as high income countries. Even without increasing the number of doses, we estimate an important fraction of deaths (min-max range: [6−50%]) could have been averted. In the absence of the availability of high-income countries, the model suggests that additional non-pharmaceutical interventions inducing a considerable relative decrease of transmissibility (min-max range: [15−70%]) would have been required to offset the lack of vaccines. Overall, our results quantify the negative impacts of vaccine inequities and underscore the need for intensified global efforts devoted to provide faster access to vaccine programs in low and lower-middle-income countries.
Global COVID-19 vaccine distribution has been inequitable. In this mathematical modelling study, the authors estimate the proportion of deaths that could have been averted in twenty low- and lower-middle-income countries if vaccines had been more widely available early in the pandemic.
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1 University of Greenwich, Networks and Urban Systems Centre, London, UK (GRID:grid.36316.31) (ISNI:0000 0001 0806 5472); ISI Foundation, Turin, Italy (GRID:grid.418750.f) (ISNI:0000 0004 1759 3658)
2 Northeastern University, Laboratory for the Modeling of Biological and Socio-technical Systems, Boston, USA (GRID:grid.261112.7) (ISNI:0000 0001 2173 3359)
3 Emory University, Department of Biostatistics and Bioinformatics, Atlanta, USA (GRID:grid.189967.8) (ISNI:0000 0001 0941 6502)
4 University of Florida, Department of Biostatistics, College of Public Health and Health Professions, Gainesville, USA (GRID:grid.15276.37) (ISNI:0000 0004 1936 8091)
5 Fred Hutchinson Cancer Center, Seattle, USA (GRID:grid.270240.3) (ISNI:0000 0001 2180 1622); University of Washington, Department of Biostatistics, Seattle, USA (GRID:grid.34477.33) (ISNI:0000000122986657)
6 Northeastern University, Laboratory for the Modeling of Biological and Socio-technical Systems, Boston, USA (GRID:grid.261112.7) (ISNI:0000 0001 2173 3359); Queen Mary University, School of Mathematical Sciences, London, UK (GRID:grid.4868.2) (ISNI:0000 0001 2171 1133)