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beta006_multiNPI_0vax_hicom.py
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beta006_multiNPI_0vax_hicom.py
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"""
Author: Hali Hambridge
This code was run in parallel on a computing cluster using the following bash command:
python beta006_multiNPI_0vax_hicom.py $SLURM_ARRAY_TASK_ID
Dependencies: pandas, networkx, numpy, matplotlib, itertools, time
-------------------
Parameter Settings
-------------------
Transmission probability (beta) = 0.006157 per 5 minute exposure, roughly R0 ~ 3.0
Testing Frequencies: every 3, 7, 14, 28 days and symptomatic only
Proportion Vaccinated: 0%
Probability of External Infection: iid normal(loc = 0.002, scale = 0.0001),
roughly 1-2 people infected by outside source each day in a fully susceptible population,
corresponds to high community transmission scenario in paper
Proportion Mask Wearing: 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%
Proportion Social Distancing: 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%
-------------------
File Outputs
-------------------
beta006_multiNPI_0vax_hicom_0.csv
beta006_multiNPI_0vax_hicom_1.csv
beta006_multiNPI_0vax_hicom_2.csv
beta006_multiNPI_0vax_hicom_3.csv
beta006_multiNPI_0vax_hicom_4.csv
"""
import sys
import pandas as pd
import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
import itertools
import time
from testing_freq import *
from utils import *
# Parse command line arguments
TASKID = int(sys.argv[1])
myseed = TASKID*50000
print('TASKID: ', TASKID)
print('myseed: ', myseed)
sim_name = 'beta006_multiNPI_0vax_hicom'
test_freqs = [0, 3, 7, 14, 28]
time_step = 86400 # one day
nreps = 20
# Read Copenhagen Network Study data
bt = pd.read_csv('bt_data_clean.csv', header = 0, names = ['timestamp','user_a', 'user_b', 'rssi'])
# Construct adjacency matrices
adj_mats, df = construct_adj_mat(bt, time_step = time_step, data_loops = 3, dist_thres = -75)
# Set parameters for simulations
disease_params = dict()
disease_params['asymp'] = 0.3 # 30% remain symptom free for the duration, other pre-symptomatic
disease_params['sigma_a'] = 1/3 # average incubation period is 3 days
disease_params['sigma_s'] = 1/3
disease_params['gamma_a'] = 1/7 # mild to moderate infectious no longer than 10 days (per CDC)
disease_params['gamma_s'] = 1/12 # severe illness infectious no longer than 20 days after symptom onset
disease_params['n_time_day'] = 1
test_params = dict()
test_params['spec'] = 0.99
test_params['symp_test_delay'] = gen_symp_test_delay(n_nodes = adj_mats.shape[1], univ_delay = 3)
test_params['time_dep'] = True
test_params['time_dep_type'] = 'W'
# % of people seeking testing at each time step, even though not sick -- this is about 3 people per day
test_params['false_symp'] = 0.005
quar_params = dict()
quar_params['quar_delay'] = gen_quar_delay(n_nodes = adj_mats.shape[1], univ_delay = 1)
quar_params['quar_len'] = 10 # 10 day quarantine
# Create the beta scenarios
sd_props = np.linspace(0, 1, 11)
fm_props = np.linspace(0, 1, 11)
vax_props = np.linspace(0, 0, 1)
beta_scenarios = list(itertools.product(sd_props, fm_props, vax_props))
# Create empty df for output
df_out = pd.DataFrame()
# Loop through each of the beta scenarios
for scenario in beta_scenarios:
p_sd = scenario[0]
p_fm = scenario[1]
p_vax = scenario[2]
"""
RUN SIMULATION FOR TESTING SCENARIOS
"""
# Loop through each of the testing frequencies to consider
for tf in test_freqs:
# Set the testing frequency
test_params['test_freq'] = tf
# Run simulation with testing and isolation
for i in range(nreps):
# Set the parameters that are probabilistic
rs = np.random.RandomState(myseed)
disease_params['ext_inf'] = rs.normal(loc = 0.002, scale = 0.0001, size = 1) # about 1-2 people infected by outside source each day in a fully susceptible population
while disease_params['ext_inf']<0:
disease_params['ext_inf'] = rs.normal(loc = 0.002, scale = 0.0001, size = 1)
disease_params['init_status'] = gen_init_status(n_nodes = adj_mats.shape[1], asymp = disease_params['asymp'], n_init_inf = 1, n_init_rec = int(adj_mats.shape[1]*p_vax), seed = myseed)
disease_params['beta'] = gen_trans_prob_NPI(n_nodes = adj_mats.shape[1], base_beta = 0.006157, p_sd = p_sd, p_fm = p_fm, seed = myseed)
test_params['nc_schd'] = rs.normal(loc = 0.025, scale = 0.01, size = 1) # Percent non-compliant with scheduled testing
while test_params['nc_schd']<0:
test_params['nc_schd'] = rs.normal(loc = 0.025, scale = 0.01, size = 1) # Percent non-compliant with scheduled testing
test_params['nc_symp'] = rs.normal(loc = 0.25, scale = 0.1, size = 1) # Percent non-compliant with symptomatic testing
while test_params['nc_symp']<0:
test_params['nc_symp'] = rs.normal(loc = 0.25, scale = 0.1, size = 1) # Percent non-compliant with symptomatic testing
quar_params['quar_comp'] = gen_quar_comp(n_nodes = adj_mats.shape[1], seed = myseed)
# Instantiate the simulation class
testin = TestFreq(adj_mats, disease_params, test_params, quar_params)
# Run the simulation
(ia_nodes_byt, is_nodes_byt, test_pos_schd_byt, test_pos_symp_byt, q_schd_byt, q_symp_byt) = testin.sim_spread_test(seed = myseed)
# Flatten the results
flat_ia = [x for l in ia_nodes_byt for x in l]
flat_is = [x for l in is_nodes_byt for x in l]
# Save the results
tmpdf = pd.DataFrame.from_dict({'rep': [i+1], 'test_freq': [tf], 'p_sd': [p_sd], 'p_fm': [p_fm], 'p_vax': [p_vax], 'cum_uniq_inf': [len(set(flat_ia + flat_is))], 'ext_inf_ct': [testin.ext_ict]})
df_out = df_out.append(tmpdf, ignore_index = True)
# Update the seed
myseed +=1
# Save out the pandas dataframe results
df_out.to_csv(sim_name + '_' + str(TASKID) + '.csv', index = False)