A Decision Support System for Prioritised COVID-19 Two-dosage Vaccination Allocation and Distribution (preprint)

Global distribution of COVID-19 vaccines is one of the world's most challenging logistics tasks. A timely mass vaccination during a pandemic is a matter of life and death. This study proposes a decision support system (DSS) that integrates GIS, analytics, and simulation methods to help develop a priority-based distribution of COVID-19 vaccines in a large urban setting. The methodology applies novel hierarchical heuristic-simulation procedures to create a holistic algorithm for prioritising the process of demand allocation and optimising vaccine distribution. The Melbourne metropolitan area in Australia with a population of over five million is used as a case study. Three vaccine supply scenarios, namely limited, excessive, and disrupted supply, were formulated to operationalise a two-dose vaccination program. Vaccine distribution with hard constraints were simulated and then further validated with sensitivity analyses. The results show that vaccines can be prioritised to society's most vulnerable segments and distributed using the current logistics network with 10 vehicles. Compared with other vaccine distribution plans with no prioritisation, such as equal allocation of vaccines to local government areas based on population size or one on a first-come-first-serve basis, the plans generated by the proposed DSS ensure prioritised vaccination of the most needed and vulnerable population. The aim is to curb the spread of the infection and reduce mortality rate more effectively. They also achieve vaccination of the entire population with less logistical resources required. As such, this study contributes to knowledge and practice in pandemic vaccine distribution and enables governments to make real-time decisions and adjustments in daily distribution plans. In this way any unforeseen disruptions in the vaccine supply chain can be coped with.

Modeling Vaccine Allocations in the COVID-19 Pandemic: A Case Study in Australia (preprint)

Timely allocation and distribution of COVID-19 vaccines on a large scale is a highly complex, dynamic, and context-specific task. The focus of this research on optimizing vaccine allocation is on the downstream part of a COVID-19 vaccine supply chain. Previous research on vaccine supply chains and pandemic supply chains has not fully incorporated the multitude of factors and underlying constraints affecting a vaccine supply chain which can be optimized to mitigate the risk of infection. An effective model is needed to conceptualize the process of the downstream vaccine supply chain to ensure efficient coordination and timely distribution of vaccines to the population. This paper develops a mathematical model to support vaccine allocation decisions based on exposure risk, susceptibility rate, and operational constraints including capacity of medical centers, vaccine stocks, and transshipment capacity. Our conceptual model integrates a centralized booking system, risk profiling and prioritization, and a vaccine distribution system, to develop an effective vaccine allocation model based on the parameters of the total population susceptible to COVID-19 and the density-based exposure risk in the catchment of each medical center. We have incorporated the possibility of transshipment between medical centers and a variety of different vaccine package sizes. Using the state of Victoria, Australia as a case study, we applied the proposed model to test different scenarios of vaccine allocation and distribution. This research proposes specific guidelines for COVID-19 vaccine distribution and makes recommendations on how healthcare providers and government entities should work together to establish more efficient logistical capabilities.