MeshScope-Scenario: A Seeded Monte Carlo Framework for Probabilistic Assessment of ICU and HCU Capacity Shortfall in Japan’s Secondary Medical Areas, Incorporating Inter-Zone Transfer and Seasonal Surge
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Background
Descriptive mapping of intensive care unit (ICU) and high care unit (HCU) capacity across Japan’s secondary medical areas (SMAs) characterizes where beds exist, but medical planning also requires answers to prospective questions: how likely is a capacity shortfall under demand surge, which assumptions drive that risk, and how much protection do inter-zone transfer arrangements provide. No openly available tool addresses these questions at the SMA level, the geographic unit at which Japanese medical plans are written.
Methods
We developed MeshScope-Scenario, a probabilistic capacity–demand framework operating on the MeshScope-Region platform. For a selected SMA, observed inputs (notified ICU/HCU beds from the Hospital Bed Function Reports; resident population) are combined with four explicitly flagged assumption parameters — effective staffed-bed rate, concurrent severe-care demand per 100,000 population, surge multiplier, and net cross-boundary inflow — each with a user-specified distribution. A seeded Monte Carlo engine (deterministic reproduction under a fixed seed) estimates the distribution of bed shortfall; interventions are compared under common random numbers. Parameter dependence is introduced by a Gaussian copula with automatic positive-semidefinite correction; global sensitivity is quantified by Sobol first-order and total-order indices (Saltelli sampling, Jansen estimators) alongside a deterministic one-at-a-time tornado analysis. A two-zone extension transfers unmet demand to the nearest ICU-holding SMA using road-network travel times measured in MeshScope-Region, with a transfer time limit and an acceptance cap; because both zones share the same systemic draws, correlated exhaustion of donor capacity under surge (“shared-fate” risk) is represented structurally. A seasonal layer applies twelve monthly surge multipliers and reports the distribution of annual maximum shortfall and month-specific shortfall probabilities. The engine is a dependency-free pure-function module verified by 40 statistical tests.
Results
The framework reproduces identical output under identical seed and input; a flat seasonal profile reproduces the non-seasonal model exactly; copula factorization error is below 10 −9 ; and 10,000 iterations across three intervention variants complete in approximately 50 ms in a standard browser, permitting fully interactive use. Applied to three archetypal SMAs from the observed FY2024 supply map (seed 42, 20,000 iterations, demand prior 5 per 100,000), an ICU-zero zone with a transfer partner 48 minutes away has shortfall probability 66.0% (P50 4.8, P90 20.7 beds); a median metropolitan zone, 32.7% (P90 5.9), with Sobol indices ranking demand density and surge dominant; a high-supply zone shows zero shortfall up to approximately 2.5× surge. For the ICU-zero zone, independent-donor reasoning credits the transfer arrangement with a 2.27-bed reduction in expected shortfall, of which shared-fate correlation removes 93%; the probability of severe shortfall under the arrangement equals that with no arrangement at all, while a committed pool of five donor beds (6% of donor effective supply) restores a 13-point reduction and more than halves the arrangement’s correlation exposure.
Conclusions
MeshScope-Scenario extends SMA-level capacity mapping from description to prospective risk assessment. All demand-side inputs are declared assumptions with adjustable distributions rather than estimates presented as fact; the framework’s value is to make the consequences of those assumptions, and their interaction with observed supply, explicit, reproducible, and inspectable for planning deliberation.
