Pandemic burden in low-income settings and impact of limited and delayed interventions: A granular modelling analysis of COVID-19 in Kabwe, Zambia

Abstract

Objectives: Pandemic response in low-income countries (LICs) or settings often suffers from scarce epidemic surveillance and constrained mitigation capacity. The drivers of pandemic burden in such settings, and the impact of limited and delayed interventions remain poorly understood. Methods: We analysed COVID-19 seroprevalence and all-cause excess deaths data from the peri-urban district of Kabwe, Zambia between March 2020 and September 2021 with a novel mathematical model. Data encompassed three consecutive waves caused by the wild-type, Beta and Delta variants. Results: Across all three waves, we estimated a high cumulative attack rate, with 78% (95% credible interval [CrI] 71-85) of the population infected, and a high all-cause excess mortality, at 402 (95% CrI 277-473) deaths per 100,000 people. Ambitiously improving health care to a capacity similar to that in high-income settings could have averted up to 46% (95% CrI 41-53) of accrued excess deaths, if implemented from June 2020 onward. An early and accelerated vaccination rollout could have achieved the highest reductions in deaths. Had vaccination started as in some high-income settings in December 2020 and with the same daily capacity (doses per 100 population), up to 68% (95% CrI 64-71) of accrued excess deaths could have been averted. Slower rollouts would have still averted 62% (95% CrI 58-68), 54% (95% CrI 49-61) or 26% (95% CrI 20-38) of excess deaths if matching the average vaccination capacity of upper-middle-, lower-middle- or LICs, respectively. Conclusions: Robust quantitative analyses of pandemic data are of pressing need to inform future global pandemic preparedness commitments.

Keywords

• COVID-19
• Pandemic preparedness
• Equity
• Mathematical modelling
• Low-income