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_US.m
    path of file: ./repos/spm/toolbox/DEM
    
    the code of the file until where you have to start completion: function [Y,X] = spm_COVID_US(P,M,U)
% Generate predictions and hidden states of a multi-region COVID model
% FORMAT [Y,X] = spm_COVID_US(P,M,U)
% P   - model parameters
% M   - model structure (requires M.T - length of timeseries)

    
function [Y,X] = spm_COVID_US(P,M,U)
% Generate predictions and hidden states of a multi-region COVID model
% FORMAT [Y,X] = spm_COVID_US(P,M,U)
% P   - model parameters
% M   - model structure (requires M.T - length of timeseries)
% U   - number of output variables [default: 2] or indices e.g., [4 5]
%
% Y(:,1) - number of new deaths
% Y(:,2) - number of new cases
% Y(:,3) - CCU bed occupancy
% Y(:,4) - working days
% Y(:,5) - herd immunity
% Y(:,6) - ...
%
% X{i}    - (M.T x 4) marginal densities over four factors for region i
% location   : {'home','out','CCU','morgue'};
% infection  : {'susceptible','infected','infectious','immune'};
% clinical   : {'asymptomatic','symptoms','ARDS','deceased'};
% diagnostic : {'untested','waiting','positive','negative'}
%
% This function returns data Y and their latent states or causes X, given
% the parameters of a generative model. This model is a mean field
% approximation based upon population or density dynamics with certain
% conditional depend