covid19-supermarket-abm 0.0.1

Agent-based model for Covid-19 transmission in supermarkets

Agent-based model for COVID-19 transmission in supermarkets.

This code accompanies the paper "COVID-19 transmission in supermarkets using agent-based modelling".

Install

> pip install covid19-supermarket-abm

Example

In the example below, we use the example data included in the package to simulate a day in the fictitious store given the parameters below.

from covid19_supermarket_abm.utils.load_example_data import load_example_store_graph, load_example_paths
from covid19_supermarket_abm.path_generators import get_path_generator
from covid19_supermarket_abm.simulator import simulate_one_day

# Set parameters
config = {'arrival_rate': 2.55,  # Poisson rate at which customers arrival
           'traversal_time': 0.2,  # mean wait time per node
           'num_hours_open': 14,  # store opening hours
           'infection_proportion': 0.0011,  # proportion of customers that are infectious
         }

# load synthetic data
zone_paths = load_example_paths()
G = load_example_store_graph()

# Create a path generator function which feeds our model with customer paths
path_generator_function, path_generator_args = get_path_generator(G, zone_paths)

# Simulate a day and store results in results
results_dict = simulate_one_day(config, G, path_generator_function, path_generator_args)

We can examine the results that are stored in the results_dict.

> print(list(results_dict.keys()))

['num_cust', 'num_S', 'num_I', 'total_time_with_infected', 'num_contacts_per_cust', 'num_cust_w_contact', 'mean_num_cust_in_store', 'max_num_cust_in_store', 'num_contacts', 'shopping_times', 'mean_shopping_time', 'num_waiting_people', 'mean_waiting_time', 'store_open_length', 'df_num_encounters', 'df_time_with_infected', 'total_time_crowded']

See below for their description.

Key	Description
num_cust	Total number of customers
num_S	Number of susceptible customers
num_I	Number of infected customers
total_time_with_infected	Total exposure time
num_contacts_per_cust	Number of contacts with infectious customers per susceptible customer
num_cust_w_contact	Number of susceptible customers which have at least one contact with an infectious customer
mean_num_cust_in_store	Mean number of customers in the store during the simulation
max_num_cust_in_store	Maximum number of customers in the store during the simulation
num_contacts	Total number of contacts between infectious customers and susceptible customers
df_num_encounters	Dataframe which contains the the number of encounters with infectious customers for each node
shopping_times	Array that contains the length of all customer shopping trips
mean_shopping_time	Mean of the shopping times
num_waiting_people	Number of people who are queueing outside at every minute of the simulation (when the number of customers in the store is restricted)
mean_waiting_time	Mean time that customers wait before being allowed to enter (when the number of customers in the store is restricted)
store_open_length	Length of the store's opening hours
df_time_with_infected	Dataframe containing the exposure time per node
total_time_crowded	Total time that nodes were crowded (when there are more than thres number of customers in a node. Default value of thres is 3)

Questions?

This is work in progress, but feel free to ask any questions by raising an issue or containg me directly under [e-mail].