Latin hypercube sampling method3/14/2024 Our procedure cuts down on simulation time and helps us perform a more comprehensive analysis of the influential parameters in the cholera model, than would be possible otherwise. To test the effectiveness of our procedure, we examine the sensitive parameters in a deterministic ordinary differential equations cholera model having seven human compartments and two bacterial compartments. The sampling method is often used to construct computer experiments or for Monte Carlo integration.LHS was described by Michael McKay of Los Alamos National Laboratory in 1979. In this thesis, we couple the optimal control numerical procedure to the LHS/PRCC procedure and perform a simultaneous examination of the effects of all the LHS parameter on the objective functional value. Latin hypercube sampling (LHS) is a statistical method for generating a near-random sample of parameter values from a multidimensional distribution. Despite the usefulness of LHS/PRCC sensitivity analysis in studying the sensitivity of a model to the parameter values used in the model, no study has been done that fully integrates Latin Hypercube sampling with optimal control analysis. Latin Hypercube Sampling/Partial Rank Correlation Coefficient (LHS/PRCC) sensitivity analysis is an efficient tool often employed in uncertainty analysis to explore the entire parameter space of a model.
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