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_priors.m
    path of file: ./repos/spm/toolbox/DEM
    
    the code of the file until where you have to start completion: function [P,C,str,rfx] = spm_COVID_priors
% Generate prior expectation and covariance log parameters
% FORMAT [pE,pC,str,rfx] = spm_COVID_priors
% 
% pE          - prior expectation (structure)
% pC          - prior covariances (structure)
% str.factor  - latent or hidden factors
% str.factors - levels of each factor
% str.outcome - outcome names (see spm_COVID_gen)
% str.names   - parameter names
% str.field   - field names of random effects
% rfx         - indices of random effects
%
% This routine assembles the (Gaussian) and priors over the parameters of a
% generative model for COVID-19. This generative model is based upon a mean
% field approximation to ensemble of population dynamics, in which four
% marginal distributions are coupled through probability transition
% matrices. The marginal distributions correspond to 4 factors; namely,
% location, infection, symptom and testing (LIST) states. The parameters of
% this model determine the initial (probability) states and the transitions
% among the states that show certain conditional independences.
%
% These parameters can either be interpreted in terms of the probability of
% moving from one state to another of a given factor, conditioned on
% another. Alternatively, in some instances (specifically, staying in the
% same state),the parameters can be thought of as log transformed rate
% constants or inverse time constants.
%
% All the parameters of this generative model are log scale parameters. In
% other words, the parameters are non-negative but are  encoded in terms of
% their logarithms. This means that priors over parameters can be specified
% in terms of a prior expectation and covariance and Gaussian assumptions
% (i.e., lognormal priors over scale parameters).
%__________________________________________________________________________
% Copyright (C) 2020 Wellcome Centre for Human Neuroimaging

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

% sources and background
%--------------------------------------------------------------------------
% https://jamanetwork.com/journals/jama/fullarticle/2761044
% https://www.statista.com/chart/21105/number-of-critical-care-beds-per-100000-inhabitants/
%--------------------------------------------------------------------------
% The NHS also maintains critical care beds for patients who are seriously
% ill and require constant support. Unlike most other categories of
% hospital bed, the total number of critical care beds has increased in
% recent years. In 2011/12 there were around 5,400 critical care beds, by
% 2019/20 this had risen to 5,900 (NHS England 2019b) (Figure 5). Of these,
% around 70 per cent are for use by adults and the remainder for children
% and infants.
%--------------------------------------------------------------------------
% https://www.gov.uk/guidance/coronavirus-covid-19-information-for-the-public
%
% As of 9am on 28 March 2020, a total of 120,776 people have been tested,
% of which 103,687 were confirmed negative and 17,089 were confirmed
% positive.
%--------------------------------------------------------------------------
% As of 5pm on 27 March 2020, 1,019 patients in the UK who tested positive
% for coronavirus (COVID-19) have died.
%--------------------------------------------------------------------------

% parameter names (where %** denotes fixed effects)
%==========================================================================
names{1}  = 'initial cases'; %**
names{2}  = 'population size';
names{3}  = 'initial proportion';
names{4}  = 'P(work | home)';
names{5}  = 'social distancing';
names{6}  = 'bed availability';  

    
function [P,C,str,rfx] = spm_COVID_priors
% Generate prior expectation and covariance log parameters
% FORMAT [pE,pC,str,rfx] = spm_COVID_priors
% 
% pE          - prior expectation (structure)
% pC          - prior covariances (structure)
% str.factor  - latent or hidden factors
% str.factors - levels of each factor
% str.outcome - outcome names (see spm_COVID_gen)
% str.names   - parameter names
% str.field   - field names of random effects
% rfx         - indices of random effects
%
% This routine assembles the (Gaussian) and priors over the parameters of a
% generative model for COVID-19. This generative model is based upon a mean
% field approximation to ensemble of population dynamics, in which four
% marginal distributions are coupled through probability transition
% matrices. The marginal distributions correspond to 4 factors; namely,
% location, infection, symptom and testing (LIST) states. The parameters of
% this model determine the initial (probability) states and the transitions
% among the states that show certain conditional independences.
%
% These parameters can either be interpreted in terms of the probability of
% moving from one state to another of a given factor, conditioned on
% another. Alternatively, in some instances (specifically, staying in the
% same state),the parameters can be thought of as log transformed rate
% constants or inverse time constants.
%
% All the parameters of this generative model are log scale parameters. In
% other words, the parameters are non-negative but are  encoded in terms of
% their logarithms. This means that priors over parameters can be specified
% in terms of a prior expectation and covariance and Gaussian assumptions
% (i.e., lognormal priors over scale parameters).
%__________________________________________________________________________
% Copyright (C) 2020 Wellcome Centre for Human Neuroimaging

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

% sources and background
%--------------------------------------------------------------------------
% https://jamanetwork.com/journals/jama/fullarticle/2761044
% https://www.statista.com/chart/21105/number-of-critical-care-beds-per-100000-inhabitants/
%--------------------------------------------------------------------------
% The NHS also maintains critical care beds for patients who are seriously
% ill and require constant support. Unlike most other categories of
% hospital bed, the total number of critical care beds has increased in
% recent years. In 2011/12 there were around 5,400 critical care beds, by
% 2019/20 this had risen to 5,900 (NHS England 2019b) (Figure 5). Of these,
% around 70 per cent are for use by adults and the remainder for children
% and infants.
%--------------------------------------------------------------------------
% https://www.gov.uk/guidance/coronavirus-covid-19-information-for-the-public
%
% As of 9am on 28 March 2020, a total of 120,776 people have been tested,
% of which 103,687 were confirmed negative and 17,089 were confirmed
% positive.
%--------------------------------------------------------------------------
% As of 5pm on 27 March 2020, 1,019 patients in the UK who tested positive
% for coronavirus (COVID-19) have died.
%--------------------------------------------------------------------------

% parameter names (where %** denotes fixed effects)
%==========================================================================
names{1}  = 'initial cases'; %**
names{2}  = 'population size';
names{3}  = 'initial proportion';
names{4}  = 'P(work | home)';
names{5}  = 'social distancing';
names{6}  = 'bed availability';  
names{7}  = 'contacts: home';
names{8}  = 'contacts: work';
names{9}  = 'transmission strength';
names{10} = 'infected period';
names{11} = 'contagious period';
names{12} = 'incubation period';
names{13} = 'P(ARDS | symptoms)';
names{14} = 'symptomatic period';
names{15} = 'time in CCU';
names{16} = 'P(fatality | CCU)';
names{17} = 'P(survival | home)';
names{18} = 'track and trace'; %**
names{19} = 'testing latency'; %**
names{20} = 'test delay'; %**
names{21} = 'test selectivity'; %**
names{22} = 'sustained testing'; %**
names{23} = 'baseline testing'; %**
names{24} = 'immune period'; %**
names{25} = 'exempt period'; %**
names{26} = 'resistance'; %**
names{27} = 'innate immunity'; %**

% random effects (i.e., effects that are common in countries)
%--------------------------------------------------------------------------
rfx       = 2:17;

% latent or hidden factors
%--------------------------------------------------------------------------
factors   = {'Location','Infection','Symptoms','Testing'};

factor{1} = {'home','work','hospital','removed','isolated'};
factor{2} = {'susceptible','infected','infectious','Ab +ve','Ab -ve'};
factor{3} = {'none','symptoms','ARDS','deceased'};
factor{4} = {'untested','waiting','PCR +ve','PCR -ve'};

% labels or strings for plotting
%--------------------------------------------------------------------------
str.outcome = {'Death rate',...
               'New tests',...
               'CCU occupancy',...
               'ERR',...
               'Herd immunity',...
               'Tests',...
               'Contagion risk (%)',...
               'Prevalence {%}',...
               'FTTI target',...
               'New cases'};
str.factors = factors;
str.factor  = factor;
str.names   = names;

% cut and paste to see the effects of changing different prior expectations
%xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
if false
    pE    = spm_COVID_priors; M.T = 365;
    [Y,X] = spm_COVID_gen(pE,M,1:3);
    u     = exp(pE.cap + pE.N)*1e6;
    spm_COVID_plot(Y,X,[],u)
end