MiTepid 0.0.4

Installation

In your command prompt or bash, simply type:

pip install mitepid

Or you can install it from repo if you want to have the latest (untested) updates.

Basic Usage

The code should work well under Python 3.x. There is a script coming with the code called script_main.py which can be used as an example on how to run the code.

The following code snippet simulates the spread of COVID-19 in Germany. It assumed initially, 1 in 100,000 in all age groups in Germany are infective. Two scenarios are studied. One is an uncontained case, which is then used as a reference scenario for the next case study. In the second one, it is assumed COVID-19 spreads uncontained for 60 days, then the country goes under lockdown for 60 days, and then switches between uncontained and lockdown every month. The resulting plots for each case saved under sample_outputs subfolder in the current working directory.

To see a list of defined policies and countries, look into policy.py module. Or you can run mitepid.str_policy_info().

The optimized contact rates are calculated for nine age groups 0-10, 10-20, 20-30, …, 70-80, and 80+. The advantage of this approach is that we can use the optimized contact rates in any population with a known age-structure. Hence, in order to simulate the model for any country, its age distribution in the above-mentioned age-groups should be defined in the code, more specifically in mit_epid.get_pop_distr() function. A few are already defined.

#%% general variables
country = 'Germany'
t_end = 541
x00 = 1e-5
x0_vec=[x00, x00, x00, x00, x00, x00, x00, x00, x00,]


dir_source_mat = Path(Path(__file__).resolve().parent)
file_data_opt_SIR = Path(dir_source_mat, 'Optimised_B', 'SIR_B_opt_normalised.mat')
dir_save_plots_main = Path(Path(__file__).resolve().parent, 'sample_output')

# loading optimised contact rates
file_data_opt_SIS = Path(dir_source_mat, 'Optimised_B', 'SIS_B_opt_normalised.mat')
file_data_opt_SIR = Path(dir_source_mat, 'Optimised_B', 'SIR_B_opt_normalised.mat')
B_opt_SIS = get_B_policy(file_data_opt_SIS, country, 'Uncontained', )
B_opt_SIR = get_B_policy(file_data_opt_SIR, country, 'Uncontained', )

# age groups, used as legends in plots
age_groups = ['[0-10]',  '[10-20]', '[20-30]', '[30-40]',
              '[40-50]', '[50-60]', '[60-70]', '[70-80]', '80+',]
Ng = len(age_groups)

# recovery rate (if on average, an individual is infectious for 20 days, then alpha = 1/20 = 0.05)
alpha = load_mat(file_data_opt_SIR, 'alpha')
alpha_vec = np.ones((Ng, 1)) * alpha
# inhibition rate (if on average, an individual is in Latent (E) compartment for 2 days, then alpha = 1/2 = 0.5)
# used in SEIR and not SIR/SIS models
Gamma = np.zeros((Ng,Ng))
np.fill_diagonal(Gamma, alpha_vec)
#
Sigma = Gamma *10


model_type = 'SIR'
#%% reference policy: uncontained
# the uncontained policy is used as the reference model for all epid_sim object defined later on.
# that means in the plots of epid_obj, uncontained policy solution is also plotted as a reference.
policy_name = 'Uncontained'
list_t_switch = [0,]
all_containment_list = ['Uncontained',]
ref_obj = epid_sim(model_type=model_type,
                    B=B_opt_SIR,
                    Gamma=Gamma,
                    Sigma=Sigma,
                    dir_save_plots_main=dir_save_plots_main,
                    country='Germany',
                    policy_list=all_containment_list,
                    policy_switch_times=list_t_switch,
                    x0=x0_vec,
                    t_end=t_end,
                    str_policy=policy_name,
                    group_labels=age_groups,)
ref_obj.plot_agg()
ref_obj.plot_strat()
ref_obj.plot_strat_multiax()

#%% Uncontained_then_switching_R0
list_t_switch = [0, 60, 120, 150, 180, 210, 240, 270, 300, 330,]
all_containment_list = ['Uncontained',
                        'Lockdown',
                        'R0_is_1',
                        'Uncontained',
                        'Lockdown',
                        'R0_is_1',
                        'Uncontained',
                        'Lockdown',
                        'R0_is_1',
                        'Uncontained',
                         ]
policy_name = 'Uncontained_then_switching_Lockdown_R0'

epid_obj = epid_sim(model_type=model_type,
                    B=B_opt_SIR,
                    Gamma=Gamma,
                    Sigma=Sigma,
                    dir_save_plots_main=dir_save_plots_main,
                    country=country,
                    policy_list=all_containment_list,
                    policy_switch_times=list_t_switch,
                    x0=x0_vec,
                    t_end=t_end,
                    str_policy=policy_name,
                    group_labels=age_groups,
                    ref_class=ref_obj)

epid_obj.plot_agg()
epid_obj.plot_strat()
epid_obj.plot_strat_multiax()
#%% Uncontained_then_switching_Lockdown
# this policy inluceds siwtching etween uncontained and lockdown scenarios.

list_t_switch = [0, 90, 120, 150, 180, 210, 240, 270, 300, 330, 360, ]
all_containment_list  = ['Uncontained',
                        'Lockdown',
                        'Uncontained',
                        'Lockdown',
                        'Uncontained',
                        'Lockdown',
                        'Uncontained',
                        'Lockdown',
                        'Uncontained',
                        'Lockdown',
                        'Uncontained',
                       ]

policy_name = 'Uncontained_then_switching_Lockdown'
epid_obj = epid_sim(model_type=model_type,
                    B=B_opt_SIR,
                    Gamma=Gamma,
                    Sigma=Sigma,
                    dir_save_plots_main=dir_save_plots_main,
                    country=country,
                    policy_list=all_containment_list,
                    policy_switch_times=list_t_switch,
                    x0=x0_vec,
                    t_end=t_end,
                    str_policy=policy_name,
                    group_labels=age_groups,
                    ref_class=ref_obj)
epid_obj.plot_agg()
epid_obj.plot_strat()
epid_obj.plot_strat_multiax()

Compatibility

This code is tested under Python 3.8, and should work well for all current versions of Python 3.

Licence

GNU General Public License (Version 3).

Author

MiTepid is maintained by Vahid Samadi Bokharaie.