Optimizing Spatial Allocation of COVID‐19 Vaccine by Agent‐Based Spatiotemporal Simulations

Abstract

Optimizing allocation of vaccine, a highly scarce resource, is an urgent and critical issue during fighting against on‐going COVID‐19 epidemic. Prior studies suggested that vaccine should be prioritized by age and risk groups, but few of them have considered the spatial prioritization strategy. This study aims to examine the spatial heterogeneity of COVID‐19 transmission in the city naturally, and optimize vaccine distribution strategies considering spatial prioritization. We proposed an integrated spatial model of agent‐based model and SEIR (susceptible‐exposed‐infected‐recovered). It simulated spatiotemporal process of COVID‐19 transmission in a realistic urban context. Individual movements were represented by trajectories of 8,146 randomly sampled mobile phone users on December 28, 2016 in Guangzhou, China, 90% of whom aged 18–60. Simulations were conducted under seven scenarios. Scenarios 1 and 2 examined natural spreading process of COVID‐19 and its final state of herd immunity. Scenarios 3–6 applied four vaccination strategies (random strategy, age strategy, space strategy, and space & age strategy), and identified the optimal vaccine strategy. Scenario 7 assessed the most appropriate vaccine coverage. The results demonstrates herd immunity is heterogeneously distributed in space, thus, vaccine intervention strategies should be spatialized. Among four strategies, space & age strategy is substantially most efficient, with 7.7% fewer in attack rate and 44 days longer than random strategy under 20% vaccine uptake. Space & age strategy requires 30%–40% vaccine coverage to control the epidemic, while the coverage for a random strategy is 60%–70% as a comparison. The application of our research would greatly improves the effectiveness of the vaccine usability.

Spatial Analysis of COVID-19 Vaccination: A Scoping Review

“…Using mobile phone trajectory data, Zhou et al [55] used an integrated spatial model of an agent-based model and SEIR to examine the spatial heterogeneity of COVID-19 transmission in China. They also optimized vaccine distribution strategies considering spatial prioritization.…”

“…Root-Bernstein [57] D, R 3 Whitehead, Scott [49] D, R 3 Whitehead, Carr [50] D, R 3 Bauer, Zhang [38] D, R 3 Chakraborty, Sharma [26] D, R 3 Qunaibi, Basheti [33] D, R 3 Lei [58] D 1 Zhou, Zhou [55] D, R, N, M 7 Ali, Rahman [24] D, R 3 Hu, Wang [29] D 1 Grauer, Löwen [28] N, M 4 Krzysztofowicz and Osi ńska-Skotak [23] D, N, M 5…”

Measuring COVID-19 Vaccination Coverage to Support Healthcare Equity Decision-Making in Urban Areas

“…However, the accessibility for vaccination at a centre proposed as a pilot COVID-19 vaccination programme in Hamilton, Ontario, Canada found that the selected sites did not serve the rural and urban residents appropriately; moreover, the associated cost of travel time was anticipated to be disproportionally borne by lower-income urban populations and rural residents (21). Another study conducted in China compared four optimal vaccine distribution scenarios, including random strategy, age strategy, space strategy as well as space and age strategy finding that 30-40% vaccine coverage was needed to control the epidemic under the space and age strategy, while 60-70% vaccine coverage was required for a random strategy (22).…”

COVID-19 vaccine prioritization based on district classification in Yogyakarta Province, Indonesia

“…However, Krzysztofowicz and Skotak (2021) have proposed an approach using geographical information systems (GIS) to evaluate the proximity of vaccination sites to elderly people's houses. Zhou et al (2021) modelled vaccine distribution by comparing various strategies, such as those done randomly or based on age, spatial availability as well as space and age together. They found that among these four strategies, vaccine distribution considering age and a spatial approach effectively lowered transmission (flattening the curve) in situations with limited access to vaccines.…”