Awad Susanne F, Musuka Godfrey, Mukandavire Zindoga, Froass Dillon, MacKinnon Neil J, Cuadros Diego F
Infectious Disease Epidemiology Group, Weill Cornell Medicine-Qatar, Cornell University, Doha 24144, Qatar.
World Health Organization Collaborating Centre for Disease Epidemiology Analytics on HIV/AIDS, Sexually Transmitted Infections, and Viral Hepatitis, Weill Cornell Medicine-Qatar, Cornell University, Doha 24144, Qatar.
Vaccines (Basel). 2021 Oct 25;9(11):1242. doi: 10.3390/vaccines9111242.
Geospatial vaccine uptake is a critical factor in designing strategies that maximize the population-level impact of a vaccination program. This study uses an innovative spatiotemporal model to assess the impact of vaccination distribution strategies based on disease geospatial attributes and population-level risk assessment. For proof of concept, we adapted a spatially explicit COVID-19 model to investigate a hypothetical geospatial targeting of COVID-19 vaccine rollout in Ohio, United States, at the early phase of COVID-19 pandemic. The population-level deterministic compartmental model, incorporating spatial-geographic components at the county level, was formulated using a set of differential equations stratifying the population according to vaccination status and disease epidemiological characteristics. Three different hypothetical scenarios focusing on geographical subpopulation targeting (areas with high versus low infection intensity) were investigated. Our results suggest that a vaccine program that distributes vaccines equally across the entire state effectively averts infections and hospitalizations (2954 and 165 cases, respectively). However, in a context with equitable vaccine allocation, the number of COVID-19 cases in high infection intensity areas will remain high; the cumulative number of cases remained >30,000 cases. A vaccine program that initially targets high infection intensity areas has the most significant impact in reducing new COVID-19 cases and infection-related hospitalizations (3756 and 213 infections, respectively). Our approach demonstrates the importance of factoring geospatial attributes to the design and implementation of vaccination programs in a context with limited resources during the early stage of the vaccine rollout.
地理空间疫苗接种情况是设计能使疫苗接种计划在人群层面产生最大影响的策略的关键因素。本研究使用一种创新的时空模型，基于疾病的地理空间属性和人群层面的风险评估来评估疫苗接种分配策略的影响。为了验证概念，我们改编了一个空间明确的新冠病毒模型，以研究在美国俄亥俄州新冠疫情早期阶段，对新冠疫苗推出进行假设性地理空间靶向接种的情况。这个人群层面的确定性 compartmental 模型，纳入了县级的空间地理成分，是使用一组根据疫苗接种状况和疾病流行病学特征对人群进行分层的微分方程来构建的。研究了三种不同的假设情景，重点是地理亚人群靶向接种（感染强度高与低的地区）。我们的结果表明，在全州范围内平等分配疫苗的疫苗接种计划能有效避免感染和住院（分别为 2954 例和 165 例）。然而，在疫苗公平分配的情况下，高感染强度地区的新冠病例数仍将居高不下；累计病例数仍超过 30000 例。最初针对高感染强度地区的疫苗接种计划在减少新冠新病例和与感染相关的住院方面影响最为显著（分别为 3756 例和 213 例感染）。我们的方法证明了在疫苗推出早期资源有限的情况下，将地理空间属性纳入疫苗接种计划的设计和实施中的重要性。
