Department of Industrial and Management System Engineering, University of South Florida, Tampa, Florida, USA.
Miller School of Medicine, University of Miami, Miami, Florida, USA.
BMC Med Res Methodol. 2021 Dec 5;21(1):272. doi: 10.1186/s12874-021-01458-9.
Approval of novel vaccines for COVID-19 had brought hope and expectations, but not without additional challenges. One central challenge was understanding how to appropriately prioritize the use of limited supply of vaccines. This study examined the efficacy of the various vaccine prioritization strategies using the vaccination campaign underway in the U.S.
The study developed a granular agent-based simulation model for mimicking community spread of COVID-19 under various social interventions including full and partial closures, isolation and quarantine, use of face mask and contact tracing, and vaccination. The model was populated with parameters of disease natural history, as well as demographic and societal data for an urban community in the U.S. with 2.8 million residents. The model tracks daily numbers of infected, hospitalized, and deaths for all census age-groups. The model was calibrated using parameters for viral transmission and level of community circulation of individuals. Published data from the Florida COVID-19 dashboard was used to validate the model. Vaccination strategies were compared using a hypothesis test for pairwise comparisons.
Three prioritization strategies were examined: a minor variant of CDC's recommendation, an age-stratified strategy, and a random strategy. The impact of vaccination was also contrasted with a no vaccination scenario. The study showed that the campaign against COVID-19 in the U.S. using vaccines developed by Pfizer/BioNTech and Moderna 1) reduced the cumulative number of infections by 10% and 2) helped the pandemic to subside below a small threshold of 100 daily new reported cases sooner by approximately a month when compared to no vaccination. A comparison of the prioritization strategies showed no significant difference in their impacts on pandemic mitigation.
The vaccines for COVID-19 were developed and approved much quicker than ever before. However, as per our model, the impact of vaccination on reducing cumulative infections was found to be limited (10%, as noted above). This limited impact is due to the explosive growth of infections that occurred prior to the start of vaccination, which significantly reduced the susceptible pool of the population for whom infection could be prevented. Hence, vaccination had a limited opportunity to reduce the cumulative number of infections. Another notable observation from our study is that instead of adhering strictly to a sequential prioritizing strategy, focus should perhaps be on distributing the vaccines among all eligible as quickly as possible, after providing for the most vulnerable. As much of the population worldwide is yet to be vaccinated, results from this study should aid public health decision makers in effectively allocating their limited vaccine supplies.
新型 COVID-19 疫苗的获批带来了希望和期待，但也带来了额外的挑战。其中一个核心挑战是如何合理优先使用有限的疫苗供应。本研究使用正在美国进行的疫苗接种活动，评估了各种疫苗优先排序策略的效果。
该研究开发了一个细粒度的基于代理的模拟模型，用于模拟 COVID-19 在各种社会干预措施下的社区传播，包括全面和部分关闭、隔离和检疫、使用口罩和接触者追踪以及疫苗接种。该模型使用疾病自然史的参数以及美国一个拥有 280 万居民的城市社区的人口统计和社会数据进行了填充。该模型跟踪所有年龄组的感染、住院和死亡的每日人数。该模型使用病毒传播和个体社区流通水平的参数进行了校准。使用来自佛罗里达州 COVID-19 仪表板的已发表数据对模型进行了验证。使用假设检验对两两比较比较了疫苗接种策略。
研究考察了三种优先排序策略：CDC 推荐的一个变体、年龄分层策略和随机策略。还将疫苗接种与不接种方案进行了对比。研究表明，在美国使用辉瑞/生物技术和 Moderna 开发的疫苗开展的 COVID-19 疫苗接种运动：1）减少了 10%的累计感染人数；2）通过将每月新增报告病例数控制在 100 以下，帮助疫情在更早的时候得到缓解，与不接种相比，大约提前了一个月。优先排序策略的比较表明，它们对大流行缓解的影响没有显著差异。
COVID-19 疫苗的开发和批准速度比以往任何时候都快。然而，根据我们的模型，疫苗接种对减少累计感染的影响被发现是有限的（如上所述为 10%）。这种有限的影响是由于感染的爆发式增长所致，这种增长发生在疫苗接种开始之前，这大大减少了可以预防感染的人群的易感人群。因此，疫苗接种减少了累计感染人数的机会有限。本研究的另一个值得注意的观察结果是，与其严格遵守顺序优先排序策略，不如将重点放在尽快将疫苗分配给所有符合条件的人，同时为最脆弱的人群提供疫苗。由于世界上还有许多人尚未接种疫苗，本研究的结果应有助于公共卫生决策者有效分配他们有限的疫苗供应。
