Pandemic Resource Allocation Network

Outbreak Dynamics

Transmission rate β 0.35

Travel restriction / quarantine 30%

Supply Logistics

Daily shipping capacity 240 units

Allocation strategy

Live Readouts

Day

Active infected

—

Cumulative stockout-days

Node stock healthy (>50%)

Node stock low (<50%)

Node in stockout

Active outbreak halo

How it works

Eight regions sit on a ring around a central supply hub. Each region i runs its own SIR compartmental model, coupled to its two ring neighbours by a travel term that carries infection between regions:

dS_i/dt = -β·S_i·I_i/N - τ·(S_i - avg S of neighbours)
dI_i/dt =  β·S_i·I_i/N + τ·(I_j + I_k - 2·I_i) - γ·I_i
dR_i/dt =  γ·I_i

τ (the travel-coupling rate) is scaled by 1 − quarantine strength, so tighter travel restriction slows how fast an outbreak in one region seeds its neighbours — at the cost of nothing else; this sim treats it as a free dial exactly like the source article's containment layer.

Demand at region i is proportional to its currently-infected population:

D_i(t) = c · I_i(t)      (c = supply units consumed per active case per day)
stock_i(t+1) = max(0, stock_i(t) + shipment_i − D_i(t))

Each day the hub has a fixed shipping capacity to distribute across all eight regions using the selected strategy:

Equal split — capacity divided evenly regardless of need.
Proportional to demand — each region gets capacity × (D_i / ΣD), so bigger outbreaks get bigger shipments.
Greedy — highest need first — regions are ranked by stock/demand ratio (most starved first) and filled to a healthy buffer until capacity runs out, exactly the triage rule a real logistics cell would use under scarcity.

A region enters stockout whenever demand exceeds stock + shipment for that day; the readout counts stockout-days summed across all regions and all time, which is the single number a strategy is trying to minimise.

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