In [1]:
from random import choice, sample
from copy import deepcopy
from scipy.stats import bernoulli
from collections import Counter, defaultdict
from source.host import Host
from source.virus import Virus
import matplotlib.pyplot as plt
import numpy as np
import networkx as nx
import seaborn as sns
sns.set_style('white')
sns.set_context('talk')
np.random.seed(45) # for reproducibility
%load_ext autoreload
%autoreload 2
%matplotlib inline
Host and Virus.Host:colourimmunityvirusesexpiry_timealive_timeVirus:seg1colorseg2colorinfection_timeexpiry_time
In [2]:
hosts = []
n_hosts = 1000
for i in range(n_hosts):
if i < n_hosts / 2:
hosts.append(Host(color='blue'))
else:
hosts.append(Host(color='red'))
In [3]:
# Pick 5 red hosts and 5 blue hosts at random, and infect it with a virus of the same color.
blue_hosts = [h for h in hosts if h.color == 'blue']
blue_hosts = sample(blue_hosts, 10)
blue_virus = Virus(seg1color='blue', seg2color='blue')
for h in blue_hosts:
h.viruses.append(blue_virus)
red_hosts = [h for h in hosts if h.color == 'red']
red_hosts = sample(red_hosts, 10)
red_virus = Virus(seg1color='red', seg2color='red')
for h in red_hosts:
h.viruses.append(red_virus)
In [4]:
p_immune = 1E-3 # 1 = always successful even under immune pressure
# 0 = always unsuccessful under immune pressure.
p_replicate = 0.95 # probability of replication given that a host is infected.
p_contact = 1 - 1E-1/n_hosts # probability of contacting a host of the same color.
p_same_color = 0.99 # probability of successful infection given segment of same color.
p_diff_color = 0.9 # probability of successful infection given segment of different color.
# Set up number of timesteps to run simulation
n_timesteps = 100
# Set up a defaultdict for storing data
data = defaultdict(list)
In [5]:
# Run simulation
for t in range(n_timesteps):
# First part, clear up old infections.
for h in hosts:
h.increment_time()
h.remove_old_viruses()
h.remove_immune_viruses()
# Step to replicate viruses present in hosts.
infected_hosts = [h for h in hosts if h.is_infected()]
for h in infected_hosts:
if bernoulli.rvs(p_replicate): # we probabilistically allow replication to occur
h.replicate_virus()
# Step to transmit the viruses present in hosts.
infected_hosts = [h for h in hosts if h.is_infected()]
num_contacts = 0
for h in infected_hosts:
same_color = bernoulli.rvs(p_contact)
if same_color:
new_host = choice([h2 for h2 in hosts if h2.color == h.color])
num_contacts += 0
else:
new_host = choice([h2 for h2 in hosts if h2.color != h.color])
num_contacts += 1
virus = h.viruses[-1] # choose the newly replicated virus every time.
# Determine whether to transmit or not.
p_transmit = 1
### First, check immunity ###
if virus.seg1color in new_host.immunity:
p_transmit = p_transmit * p_immune
elif virus.seg1color not in new_host.immunity:
pass
### Next, check seg1.
if virus.seg1color == new_host.color:
p_transmit = p_transmit * p_same_color
else:
p_transmit = p_transmit * p_diff_color
### Finally, check seg2.
if virus.seg2color == new_host.color:
p_transmit = p_transmit * p_same_color
else:
p_transmit = p_transmit * p_diff_color
# Determine whether to transmit or not, by using a Bernoulli trial.
transmit = bernoulli.rvs(p_transmit)
# Perform transmission step
if transmit:
new_host.viruses.append(virus)
# # Capture data in the summary graph.
# if virus.is_mixed():
# G.edge[h.color][new_host.color]['mixed'] += 1
# else:
# G.edge[h.color][new_host.color]['clonal'] += 1
else:
pass
### INSPECT THE SYSTEM AND RECORD DATA###
num_immunes = 0 # num immune hosts
num_infected = 0 # num infected hosts
num_blue_immune = 0 # num blue immune hosts
num_red_immune = 0 # num red immune hosts
num_uninfected = 0 # num uninfected hosts
num_mixed = 0 # num mixed viruses
num_original = 0 # num original colour viruses
num_red_virus = 0 # num red viruses
num_blue_virus = 0 # num blue viruses
for h in hosts:
if len(h.immunity) > 0:
num_immunes += 1
if h.is_infected() > 0:
num_infected += 1
if 'blue' in h.immunity:
num_blue_immune += 1
if 'red' in h.immunity:
num_red_immune += 1
if not h.is_infected():
num_uninfected += 1
for v in h.viruses:
if v.is_mixed():
num_mixed += 1
else:
if v.seg1color == 'blue' and v.seg2color == 'blue':
num_blue_virus += 1
elif v.seg1color == 'red' and v.seg2color == 'red':
num_red_virus += 1
num_original += 1
# Record data that was captured
data['n_immune'].append(num_immunes)
data['n_infected'].append(num_infected)
data['n_blue_immune'].append(num_blue_immune)
data['n_red_immune'].append(num_red_immune)
data['n_uninfected'].append(num_uninfected)
data['n_mixed'].append(num_mixed)
data['n_original'].append(num_original)
data['n_red_virus'].append(num_red_virus)
data['n_blue_virus'].append(num_blue_virus)
data['n_contacts'].append(num_contacts)
### INSPECT THE SYSTEM ###
In [6]:
# Reassortment successful in establishing infection or not?
plt.plot(data['n_red_virus'], color='red', label='red')
plt.plot(data['n_blue_virus'], color='blue', label='blue')
plt.plot(data['n_original'], color='black', label='original')
plt.plot(data['n_mixed'], color='purple', label='mixed')
plt.ylabel('Number of Viruses')
plt.xlabel('Time Step')
plt.title('Viruses')
plt.legend()
Out[6]:
In [43]:
np.array_equal(np.array(data['n_mixed']), np.zeros(100))
Out[43]:
In [44]:
np.where(np.array(data['n_mixed']) == np.max(data['n_mixed']))[0] - np.where(np.array(data['n_original']) == np.max(data['n_original']))[0]
Out[44]:
In [61]:
plt.plot(data['n_infected'], color='green', label='infected')
plt.plot(data['n_immune'], color='purple', label='immune')
plt.plot(data['n_blue_immune'], color='blue', label='blue immune')
plt.plot(data['n_red_immune'], color='red', label='red immune')
plt.ylabel('Number')
plt.xlabel('Time Steps')
plt.title('Hosts')
plt.legend()
Out[61]:
In [62]:
plt.plot(data['n_contacts'], color='olive', label='contacts')
plt.title('Contact Frequency')
np.where(np.array(data['n_contacts']) >= 1)[0]
Out[62]:
In [47]:
import pandas as pd
pd.DataFrame(data)
Out[47]:
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