Modeling anticipated changes in numbers of SARS-CoV-2 infections within communities due to immunization campaigns [version 2; peer review: 2 approved]

Abstract

Background: As SARS-CoV-2 spread in early 2020, uncertainty about the scope, duration, and impact of the unfolding outbreaks caused numerous countries to interrupt many routine activities, including health services. Because immunization is an essential health service, modeling changes in SARS-CoV-2 infections among communities and health workers due to different vaccination activities was undertaken to understand the risks and to inform approaches to resume services. Methods: Agent-based modeling examined the impact of Supplemental Immunization Activities (SIAs) delivery strategies on SARS-CoV-2 transmission in communities and health workers for six countries capturing various demographic profiles and health system performance: Angola, Ecuador, Lao PDR, Nepal, Pakistan, and Ukraine. Results: Urban, fixed-post SIAs during periods of high SARS-CoV-2 prevalence increased infections within the community by around 28 [range:0-79] per 1000 vaccinations. House-to-house SIAs in mixed urban and rural contexts may import infections into previously naïve communities. Infections are elevated by around 60 [range:0-230] per 1000 vaccinations, but outcomes are sensitive to prevalence in health workers and SIA timing relative to peak. Conclusions: Incremental increases in SARS-CoV-2 infection due to SIAs was small and in proportion to overall prevalence. Younger populations experience lower transmission intensity and fewer excess infections per childhood vaccine delivered. Large rural populations have lower transmission intensity but face a greater risk of introduction of SARS-CoV-2 during an SIA.

Keywords

• SARS-CoV-2
• agent-based modeling
• vaccine delivery
• eng