covid_ode.py 7.86 KB
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import optparse
import time
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import h5py
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import matplotlib.pyplot as plt
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import tensorflow as tf
import yaml
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from covid.model import CovidUKODE
from covid.rdata import *
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def sanitise_parameter(par_dict):
    """Sanitises a dictionary of parameters"""
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    par = ['epsilon', 'beta1', 'beta2', 'nu', 'gamma']
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    d = {key: np.float64(par_dict[key]) for key in par}
    return d
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def sanitise_settings(par_dict):
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    d = {'start': np.datetime64(par_dict['start']),
         'end': np.datetime64(par_dict['end']),
         'time_step': float(par_dict['time_step']),
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         'holiday': np.array([np.datetime64(date) for date in par_dict['holiday']]),
         'bg_max_time': np.datetime64(par_dict['bg_max_time'])}
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    return d
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def seed_areas(N, names, age_group=8, num_la=152, num_age=17, n_seed=30.):
    areas = ['Inner London',
             'Outer London',
             'West Midlands (Met County)',
             'Greater Manchester (Met County)']

    names_matrix = names['Area.name.2'].to_numpy().reshape([num_la, num_age])

    seed_areas = np.in1d(names_matrix[:, age_group], areas)
    N_matrix = N.reshape([num_la, num_age])  # LA x Age

    pop_size_sub = N_matrix[seed_areas, age_group]  # Gather
    n = np.round(n_seed * pop_size_sub / pop_size_sub.sum())

    seeding = np.zeros_like(N_matrix)
    seeding[seed_areas, age_group] = n  # Scatter
    return seeding


def sum_age_groups(sim):
    infec = sim[:, 2, :]
    infec = infec.reshape([infec.shape[0], 152, 17])
    infec_uk = infec.sum(axis=2)
    return infec_uk


def sum_la(sim):
    infec = sim[:, 2, :]
    infec = infec.reshape([infec.shape[0], 152, 17])
    infec_uk = infec.sum(axis=1)
    return infec_uk


def sum_total_removals(sim):
    remove = sim[:, 3, :]
    return remove.sum(axis=1)


def final_size(sim):
    remove = sim[:, 3, :]
    remove = remove.reshape([remove.shape[0], 152, 17])
    fs = remove[-1, :, :].sum(axis=0)
    return fs


def write_hdf5(filename, param, t, sim):
    with h5py.File(filename, "w") as f:
        dset_sim = f.create_dataset("simulation", sim.shape, dtype='f')
        dset_sim[:] = sim
        dset_t = f.create_dataset("time", t.shape, dtype='f')
        dset_t[:] = t
        grp_param = f.create_group("parameter")
        for k, v in param.items():
            d_beta = grp_param.create_dataset(k, [1], dtype='f')
            d_beta[()] = v


def plot_total_curve(sim):
    infec_uk = sum_la(sim)
    infec_uk = infec_uk.sum(axis=1)
    removals = sum_total_removals(sim)
    times = np.datetime64('2020-02-20') + np.arange(removals.shape[0])
    plt.plot(times, infec_uk, 'r-', label='Infected')
    plt.plot(times, removals, 'b-', label='Removed')
    plt.title('UK total cases')
    plt.xlabel('Date')
    plt.ylabel('Num infected or removed')
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    plt.grid()
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    plt.legend()


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def plot_infec_curve(ax, sim, label, color):
    infec_uk = sum_la(sim)
    infec_uk = infec_uk.sum(axis=1)
    times = np.datetime64('2020-02-20') + np.arange(infec_uk.shape[0])
    ax.plot(times, infec_uk, '-', label=label, color=color)


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def plot_by_age(sim, labels, t0=np.datetime64('2020-02-20'), ax=None):
    if ax is None:
        ax = plt.figure().gca()
    infec_uk = sum_la(sim)
    total_uk = infec_uk.mean(axis=1)
    t = t0 + np.arange(infec_uk.shape[0])
    colours = plt.cm.viridis(np.linspace(0., 1., infec_uk.shape[1]))
    for i in range(infec_uk.shape[1]):
        ax.plot(t, infec_uk[:, i], 'r-', alpha=0.4, color=colours[i], label=labels[i])
    ax.plot(t, total_uk, '-', color='black', label='Mean')
    return ax


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def plot_by_la(sim, labels, t0=np.datetime64('2020-02-20'), ax=None):
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    if ax is None:
        ax = plt.figure().gca()
    infec_uk = sum_age_groups(sim)
    total_uk = infec_uk.mean(axis=1)
    t = t0 + np.arange(infec_uk.shape[0])
    colours = plt.cm.viridis(np.linspace(0., 1., infec_uk.shape[1]))
    for i in range(infec_uk.shape[1]):
        ax.plot(t, infec_uk[:, i], 'r-', alpha=0.4, color=colours[i], label=labels[i])
    ax.plot(t, total_uk, '-', color='black', label='Mean')
    return ax


def draw_figs(sim, N):
    # Attack rate
    N = N.reshape([152, 17]).sum(axis=0)
    fs = final_size(sim)
    attack_rate = fs / N
    print("Attack rate:", attack_rate)
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    print("Overall attack rate: ", np.sum(fs) / np.sum(N))
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    # Total UK epidemic curve
    plot_total_curve(sim)
    plt.xticks(rotation=45, horizontalalignment="right")
    plt.savefig('total_uk_curve.pdf')
    plt.show()

    # TotalUK epidemic curve by age-group
    fig, ax = plt.subplots(1, 2, figsize=[24, 12])
    plot_by_la(sim, la_names, ax=ax[0])
    plot_by_age(sim, age_groups, ax=ax[1])
    ax[1].legend()
    plt.xticks(rotation=45, horizontalalignment="right")
    fig.autofmt_xdate()
    plt.savefig('la_age_infec_curves.pdf')
    plt.show()

    # Plot attack rate
    plt.figure(figsize=[4, 2])
    plt.plot(age_groups, attack_rate, 'o-')
    plt.xticks(rotation=90)
    plt.title('Age-specific attack rate')
    plt.savefig('age_attack_rate.pdf')
    plt.show()


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def doubling_time(t, sim, t1, t2):
    t1 = np.where(t == np.datetime64(t1))[0]
    t2 = np.where(t == np.datetime64(t2))[0]
    delta = t2 - t1
    r = sum_total_removals(sim)
    q1 = r[t1]
    q2 = r[t2]
    return delta * np.log(2) / np.log(q2 / q1)


def plot_age_attack_rate(ax, sim, N, label, color):
    Ns = N.reshape([152, 17]).sum(axis=0)
    fs = final_size(sim.numpy())
    attack_rate = fs / Ns
    ax.plot(age_groups, attack_rate, 'o-', color=color, label=label)


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if __name__ == '__main__':
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    parser = optparse.OptionParser()
    parser.add_option("--config", "-c", dest="config",
                      help="configuration file")
    options, args = parser.parse_args()
    with open(options.config, 'r') as ymlfile:
        config = yaml.load(ymlfile)
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    K_tt, age_groups = load_age_mixing(config['data']['age_mixing_matrix_term'])
    K_hh, _ = load_age_mixing(config['data']['age_mixing_matrix_hol'])

    T, la_names = load_mobility_matrix(config['data']['mobility_matrix'])
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    np.fill_diagonal(T, 0.)
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    N, n_names = load_population(config['data']['population_size'])
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    param = sanitise_parameter(config['parameter'])
    settings = sanitise_settings(config['settings'])
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    # Effect of cocooning on elderly (70+)
    def cocooning_model(cocooning_ratio=1.):
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        K1_tt = K_tt.copy()
        K1_hh = K_hh.copy()
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        K1_tt[14:, :] *= cocooning_ratio
        K1_tt[:, 14:] *= cocooning_ratio
        K1_hh[14:, :] *= cocooning_ratio
        K1_hh[:, 14:] *= cocooning_ratio
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        model = CovidUKODE(K1_tt, K_hh, T, N, settings['start'], settings['end'], settings['holiday'],
                           settings['bg_max_time'], 1)
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        seeding = seed_areas(N, n_names)  # Seed 40-44 age group, 30 seeds by popn size
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        state_init = model.create_initial_state(init_matrix=seeding)

        print('R_term=', model.eval_R0(param))
        #print('R_holiday=', model_holiday.eval_R0(param))
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        start = time.perf_counter()
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        t, sim, _ = model.simulate(param, state_init)
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        end = time.perf_counter()
        print(f'Complete in {end - start} seconds')

        dates = settings['start'] + t.numpy().astype(np.timedelta64)
        dt = doubling_time(dates, sim.numpy(), '2020-03-01', '2020-04-01')
        print(f"Doubling time: {dt}")
        return t, sim

    fig_attack = plt.figure()
    fig_uk = plt.figure()
    cocoon_ratios = [1.]
    for i, r in enumerate(cocoon_ratios):
        print(f"Simulation, r={r}", flush=True)
        t, sim = cocooning_model(r)
        plot_age_attack_rate(fig_attack.gca(), sim, N, f"{1 - r}", f"C{i}")
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        # fig_attack.gca().legend(title="Contact ratio")
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        plot_infec_curve(fig_uk.gca(), sim.numpy(), f"{1 - r}", f"C{i}")
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        # fig_uk.gca().legend(title="Contact ratio")
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    fig_attack.autofmt_xdate()
    fig_uk.autofmt_xdate()
    fig_attack.gca().grid(True)
    fig_uk.gca().grid(True)
    plt.show()
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    # if 'simulation' in config['output']:
    #     write_hdf5(config['output']['simulation'], param, t, sim)