You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: spm_COVID_ci.m
    path of file: ./repos/spm/toolbox/DEM
    
    the code of the file until where you have to start completion: function [S,CS,Y,C] = spm_COVID_ci(Ep,Cp,Z,U,M)
% Graphics for coronavirus simulations - with confidence intervals
% FORMAT [S,CS,Y,C] = spm_COVID_ci(Ep,Cp,Z,U,M)
% Ep     - posterior expectations
% Cp     - posterior covariances
% Z      - optional empirical data
% U      - outcomes to evaluate [default: 1:3]
% M      - model
%
% S      - posterior expectation of cumulative outcomes
% CS     - posterior covariances of cumulative outcomes
% Y      - posterior expectation of outcomes
% C      - posterior covariances of outcomes
%
% This routine evaluates a trajectory of outcome variables from a COVID
% model and plots the expected trajectory and accompanying Bayesian
% credible intervals (of 90%). If empirical data are supplied, these will
% be overlaid on the confidence intervals. By default, 128 days
% are evaluated. In addition, posterior and prior expectations are provided
% in a panel.
%
% A single panel is plotted if one output in U is specified
%
% Although the covid model is non-linear in the parameters, one can use a
% first-order Taylor expansion to evaluate the confidence intervals in
% terms of how the outcomes change with parameters. This, in combination
% with the well-known overconfidence of variational inference, usually
% requires a slight inflation of uncertainty. Here, the posterior

    
function [S,CS,Y,C] = spm_COVID_ci(Ep,Cp,Z,U,M)
% Graphics for coronavirus simulations - with confidence intervals
% FORMAT [S,CS,Y,C] = spm_COVID_ci(Ep,Cp,Z,U,M)
% Ep     - posterior expectations
% Cp     - posterior covariances
% Z      - optional empirical data
% U      - outcomes to evaluate [default: 1:3]
% M      - model
%
% S      - posterior expectation of cumulative outcomes
% CS     - posterior covariances of cumulative outcomes
% Y      - posterior expectation of outcomes
% C      - posterior covariances of outcomes
%
% This routine evaluates a trajectory of outcome variables from a COVID
% model and plots the expected trajectory and accompanying Bayesian
% credible intervals (of 90%). If empirical data are supplied, these will
% be overlaid on the confidence intervals. By default, 128 days
% are evaluated. In addition, posterior and prior expectations are provided
% in a panel.
%
% A single panel is plotted if one output in U is specified
%
% Although the covid model is non-linear in the parameters, one can use a
% first-order Taylor expansion to evaluate the confidence intervals in
% terms of how the outcomes change with parameters. This, in combination
% with the well-known overconfidence of variational inference, usually
% requires a slight inflation of uncertainty. Here, the posterior
% covariance is multiplied by a factor of four.
%__________________________________________________________________________
% Copyright (C) 2020 Wellcome Centre for Human Neuroimaging

% Karl Friston
% Copyright (C) 2008-2022 Wellcome Centre for Human Neuroimaging

% default: number of outcomes to evaluate
%--------------------------------------------------------------------------
if nargin < 4, U = 1:3; end