English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We focus on the problem of uncertainty informed allocation of medicalresources (vaccines) to heterogeneous populations for managing epidemic spread.We tackle two related questions: (1) For a compartmental ordinary differentialequation (ODE) model of epidemic spread, how can we estimate and integrateparameter uncertainty into resource allocation decisions? (2) How can wecomputationally handle both nonlinear ODE constraints and parameteruncertainties for a generic stochastic optimization problem for resourceallocation? To the best of our knowledge current literature does not fullyresolve these questions. Here, we develop a data-driven approach to representparameter uncertainty accurately and tractably in a novel stochasticoptimization problem formulation. We first generate a tractable scenario set byestimating the distribution on ODE model parameters using Bayesian inferencewith Gaussian processes. Next, we develop a parallelized solution algorithmthat accounts for scenario-dependent nonlinear ODE constraints. Ourscenario-set generation procedure and solution approach are flexible in thatthey can handle any compartmental epidemiological ODE model. Our computationalexperiments on two different non-linear ODE models (SEIR and SEPIHR) indicatethat accounting for uncertainty in key epidemiological parameters can improvethe efficacy of time-critical allocation decisions by 4-8%. This improvementcan be attributed to data-driven and optimal (strategic) nature of vaccineallocations, especially in the early stages of the epidemic when the allocationstrategy can crucially impact the long-term trajectory of the disease.

Uncertainty Informed Optimal Resource Allocation with Gaussian Process based Bayesian Inference