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A decision support system for prioritised COVID-19 vaccine allocation & distribution

A GIS-driven, optimisation-and-simulation decision support system that decides who to vaccinate first, how many doses to send where, and how vehicles should deliver them - under limited, excessive and disrupted supply. Demonstrated on Greater Melbourne.

Transportation Research Part E · 2022 Two-dosage scheduling GIS + Metaheuristics + DES Greater Melbourne case study
5M+
Metro population modelled
3
Supply scenarios
2
Dose regimen
174d
Campaign horizon
Live model

Prioritise → allocate → deliver, in motion

LIVE MODEL · GREATER MELBOURNE · SA1 PRIORITY SURFACE Distribution depot 174d campaign TWO-DOSE SCHEDULE Dose 1 ✓ Dose 2 reserved lowhigh priority delivery route
Live model
The pipeline, animated. A risk-based priority surface over Greater Melbourne (green→red = low→high vaccination priority) drives dose allocation; vehicles run two-dose delivery from the depot to the highest-priority SA1 clusters while the 174-day coverage ring fills. A schematic of the paper’s GIS + optimisation + simulation loop.
Overview

The problem, in brief

When vaccine supply is scarce and demand is everywhere, two questions decide outcomes: who gets vaccinated first, and how do limited doses physically reach them. This project tackles both at once for a two-dose regimen, where a second dose must be reserved and delivered on schedule.

The system layers spatial health-risk mapping over an optimisation engine and a discrete-event simulation, so planners can test a policy before committing real doses, vehicles and staff to it. The case study covers the Greater Melbourne Metropolitan Area at SA1 resolution.

“Decide the priority, optimise the plan, then stress-test it in simulation - before a single dose moves.”
Spatial location of Greater Melbourne by SA1 boundaries with COVID-19 rates
Study area
Spatial setting. Greater Melbourne Metropolitan Area by SA1 boundaries, with Empirical Bayesian Smoothed COVID-19 case rates and test-centre locations.
Method

A four-stage decision pipeline

1

GIS risk mapping

Population health criteria (age, comorbidities, exposure) fused into a spatial priority surface.

2

Priority ranking

Fuzzy multi-criteria scoring converts risk layers into ranked vaccination priority areas.

3

Optimisation

Hybrid genetic algorithm allocates doses and builds two-dose delivery & routing plans.

4

Simulation

Discrete-event model checks throughput, staffing and resilience under each scenario.

Criteria maps for vaccination priority
Fig. C.2
Risk criteria layers. Spatial distribution of the health & demographic criteria feeding the priority model - smoking, cancer, obesity, heart disease, hypertension, diabetes, respiratory disease, ageing and population density.
Results

What the model shows

Priority areas for vaccination
Fig. 3
Vaccination priority surface. IDW-interpolated fuzzy priority map, its defuzzified 1–5 ranking, and the top-priority demand distribution across SA1 centroids - the spatial answer to who first.
Scenario I daily vaccination coverage
Fig. F.2
Daily vaccination coverage (Scenario I). Progression from Day 1 to Day 174 - unmet demand steadily converts to first- and second-dose coverage as the campaign runs, with supply-level and vehicle-activity trends alongside.
Scenario I daily vaccine distribution routing plan
Fig. F.4
Distribution routing. Daily vehicle routing plans across the metro network on representative days, with truck counts and load statistics.
Simulation performance indicators and staff levelling
Fig. F.1
Simulation & staffing. Performance indicators before vs. after staff-levelling optimisation - served demand up, queue and lead times down.
Scenarios

Three supply regimes tested

Scenario I

Limited supply

Scarce doses - strict prioritisation decides who is protected first.

Scenario II

Excessive supply

Ample doses - the bottleneck shifts to staffing, throughput and logistics.

Scenario III

Disruption

Supply shock mid-campaign - the plan is stress-tested for resilience & recovery.

Key findings

Highlights

▲

Faster protection of the vulnerable

Risk-based prioritisation front-loads coverage of the highest-risk populations.

⇄

Second-dose reliability

Two-dose scheduling reserves and delivers follow-up doses on time.

★

Staffing is the real bottleneck

Under ample supply, workforce levelling - not doses - drives throughput.

♻

Resilient under disruption

Re-optimisation recovers coverage quickly after a supply shock.

◎

Shorter delivery routes

Optimised routing keeps vehicle distance and loads efficient across the metro.

✎

Decision support, not just a model

Planners test a policy end-to-end before committing real resources.

Team

Authors

SSShahrooz Shahparvari
BHBehnam Hassanizadeh
AMAlireza Mohammadi
BKBehzad Kiani
KLKwok Hung Lau
PCPrem Chhetri
BABabak Abbasi
How to cite

Shahparvari, S., Hassanizadeh, B., Mohammadi, A., Kiani, B., Lau, K.H., Chhetri, P., & Abbasi, B. (2022). A decision support system for prioritised COVID-19 two-dosage vaccination allocation and distribution. Transportation Research Part E: Logistics and Transportation Review, 159, 102598.

🔗 doi.org/10.1016/j.tre.2021.102598

About this tool

Research: COVID-19 Vaccine DSS

The concept

A research showcase of a decision-support system for vaccine distribution and wastage reduction.

How it works

Cold-chain, expiry and demand uncertainty drive allocation and replenishment decisions.

Winning strategy

Synchronising supply with administration capacity is what cuts wastage.

minimise wastage s.t. cold-chain and demand constraints
Vaccine logisticsCold chainWastageDSS