Rural prioritization may increase the impact of COVID-19 vaccines in Sub-Saharan Africa due to ongoing internal migration: A modeling study

Abstract

How COVID-19 vaccine is distributed within low- and middle-income countries has received little attention outside of equity or logistical concerns but may ultimately affect campaign impact in terms of infections, severe cases, or deaths averted. In this study we examined whether subnational (urban-rural) prioritization may affect the cumulative two-year impact on disease transmission and burden of a vaccination campaign using an agent-based model of COVID-19 in a representative Sub-Saharan Africa country setting. We simulated a range of vaccination strategies that differed by urban-rural prioritization, age group prioritization, timing of introduction, and final coverage level. Urban prioritization averted more infections in only a narrow set of scenarios, when internal migration rates were low and vaccination was started by day 30 of an outbreak. Rural prioritization was the optimal strategy for all other scenarios, e.g., with higher internal migration rates or later start dates, due to the presence of a large immunological naive rural population. Among other factors, timing of the vaccination campaign was important to determining maximum impact, and delays as short as 30 days prevented larger campaigns from having the same impact as smaller campaigns that began earlier. The optimal age group for prioritization depended on choice of metric, as prioritizing older adults consistently averted more deaths across all of the scenarios. While guidelines exist for these latter factors, urban-rural allocation is an orthogonal factor that we predict to affect impact and warrants consideration as countries plan the scale-up of their vaccination campaigns.

Microsimulation based quantitative analysis of COVID-19 management strategies

“…For instance, differential equation driven analysis of vaccination strategies or other intervention policies is feasible and widely used [19,20], but the number of compartments is limited and predominantly organised by age or serostatus. Another strategy is provided by stochastic, agent-based models (ABMs), where agents, corresponding to individuals, move and transmit the infection among each other [21][22][23]. ABMs can easily handle subgroups, complex scenarios and also provide indications regarding the geographic spread of the pandemics.…”