Python SIRモデル作成中のType Error

前提

Pythonで通常のSIRモデルを拡張し，ネットワーク上のエージェントを対象としたモデルを作成しています

実現したいこと

発生したエラーの原因の理解と解決

発生している問題・エラーメッセージ

TypeError: 'int' object is not iterable

該当のソースコード①

society.py

# coding: utf-8 import networkx as nx class Agent: """ state = ['S', 'I', 'R'] (S: Susceptible, I: Infectious, R: Recovered) """ def __init__(self): self.state = 'S' self.neighbors_id = 0 def generate_agents(num_agent, average_degree): network = nx.barabasi_albert_graph(num_agent, average_degree//2) agents = [Agent() for agent_id in range(num_agent)] #エージェントのリスト for agent_id, agent in enumerate(agents): agent.nebghbors_id = list(network[agent_id]) return agents def count_state_fraction(agents): """ 各状態のエージェントの割合 """ fs = len([agent for agent in agents if agent.state == 'S'])/len(agents) fi = len([agent for agent in agents if agent.state == 'I'])/len(agents) fr = 1 - fs -fi return fs, fi, fr def count_num_i(agents): """ 感染状態のエージェントの数 """ num_i = len([agent for agent in agents if agent.state == 'I']) return num_i

該当のソースコード②

epidemics.py

# coding: utf-8 import random as rnd import society def initialize_state(agents, num_initial_infected_agents=1): """ 初期保有者をランダムに決定 """ initial_infected_agent_id = rnd.sample(k = num_initial_infected_agent) for i, agent in enumerate(agents): if i in initial_infected_agent_id: agent.state = 'I' else: agent.state = 'S' def disease_spreading(agents, beta, gammma): """ SIRダイナミクスの計算("I"状態のエージェントがいなくなるまで) """ for day in range(1, 10000): state_changeable_agents = [agent for agent in agents if agent.state in ['S', 'I']] next_states = ['S' for i in range(len(state_changeable_agents))] for i, agent in enumerate(state_changeable_agents): if agent.state == 'S': num_infected_neighbors = len([agents[agent_id] for agent_id in agent.neighbors_id if agents[agent_id].state == 'I']) if rnd.random() <= beta*num_infected_neighbors: next_states[i] = 'I' else: pass elif agent.state == 'I': if rnd.random() <= gammma: next_states[i] = 'R' else: next_states[i] = 'I' # 状態の更新 for agent, next_state in zip(state_changeable_agents, next_states): agent.state = next_state fs, fi, fr = society.count_state_fraction(agents) num_i = society.count_num_i(state_changeable_agents) print(f'Day:{day}, Fs:{fs:2f}, Fi:{fi:2f}, Fr:{fr:.2f}') if num_i == 0: print('spreading finished') break return fs, fi, fr

該当のソースコード③

main.py

import numpy as np import pandas as pd import random as rnd import society import epidemics def main(): """ メイン処理 """ ### Calcualtion setting ### num_agent = 81306 # エージェントの総数 average_degree = 43 # 平均次数 beta = 0.07 # 感染率 gamma = 0.6 # 回復率 max_season = 100 num_initial_infected_agents = 1 agents = society.generate_agents(num_agent, average_degree) result = pd.DataFrame({'FES':[]}) fes_hist = [] for season in range(1, max_season+1): fs, fim, fi, fr = epidemics.disease_spreading(agents, beta, gamma) fes_hist.append(fr) new_result = pd.DataFrame([fes_eq], columns = ['FES']) result = result.append(new_result) print(f'Season finished with FES: {fes_eq:.2f}') result.to_csv(f'result.csv') if __name__=='__main__': main()

試したこと

エラーの検索と似た事例の調査

補足情報（FW/ツールのバージョンなど）

jupyter notebook

https://qiita.com/Keyskey/items/8e0d7a67b222a7866154
コードは上記のウェブサイトを参考にしました．
ゲーム理論の部分を省き，SIRモデルとしての完成を目指しています．