Developing a NetLogo simulation model to analyze the spread of an infectious disease among a populat

Project requirements

Artificial life of robotics. Creating a program and code using Nlogo software.

In this project you must develop a model in order to ascertain the spread of an infectious disease among a population of agents within a particular timeframe. The purpose of this model is to analyse the following aspects of a disease:

• Assess the impact of agents staying local within a designated region vs free movement

• Assess the impact of social distancing between agents vs not social distancing

• Assess the impact of individuals who are infected self-isolating vs not self-isolating

Since this is meant to provide a relatively realistic representation of a real world situation, the criteria for this model is clearly outlined below.You must design simulation in NetLogo that adheres to the following requirements:

The following code must be placed at the beginning of your model (copy and paste this in):

breed [humans human]

globals [

;Variables to be ued for tutor marking

student_id

student_name

student_score

student_feedback

;Variables for your analysis

most_effective_measure

least_effective_measure

population_most_affected

population_most_immune

self_isolation_link

population_density

total_infected_percentage

green_infected_percentage

blue_infected_percentage

total_deaths

green_deaths

blue_deaths

total_antibodies_percentage

green_antibodies_percentage

blue_antibodies_percentage

]

humans-own[

infected_time

antibodies

]

Your submitted file must not use any of the following commands:

clear-all

clear-globals

clear-ticks

clear-drawing

clear-all-plots

clear-output

stop

show

print

write

The model must have a function called setup_agents that initiates the following actions in your model when called:

Create a population of humans that are set to the color yellow, size 1, the shape “green person” and are randomly placed on the area of green patches. The number of humans created must be determined by the variable green_population. All of these agents must be initialised with their own antibodies variable set to 0.

Create a population of humans that are set to the color yellow, size 1, the shape “blue person” and are randomly placed on the area of blue patches. The number of humans created must be determined by the variable blue_population. All of these agents must be initialised with their own antibodies variable set to 0.

Create a number of infected individuals in each population (green and blue) determined by the global variable initially_infected. For example if initially_infected is set to 10 there would be 10 individuals in the green population infected and 10 individuals in the blue population infected.

The setup_agents function must not call the setup_world function or the run_model function.

The model must have a function called run_model that initiates the following actions in your model when called:

The tick command must be called to add 1 to the tick counter.

Human Behaviours

The human agents in your model must behave according to the following parameters:

The speed at which humans move must be set to 0.2 per step taken and they must always move in a forwards direction (relative to their heading).

Agent setup

The orientation (heading) of humans must be set randomly when wandering but must be limited to a range of 40 degrees (20 to the left and 20 to the right) per step taken, the only exception to this is when avoiding other agents, in this instance a maximum turn of 90 degrees is permitted

Humans must follow the rules of the global variables travel_restrictions, social_distancing and self_isolation as follows:

If the travel_restrictions variable is set to true the agent must stay within its own region (blue for blue people, green for green people), if the agent is not within its own region (i.e. the variable was changed mid-simulation) the agents must take the shortest route back to their own region following any other conditions instated (i.e. social_distancing or self_isolation).

If the social_distancing variable is set to true the agent must keep a minimum distance of 1 patch between itself and another agent when moving (i.e. check to see if another agent is in front before moving forward, a strategy to avoid collisions).

If the self_isolation variable is set to true the agent must stop moving and turn orange to indicate it is in self-isolation when infected after the limit undetected_period (i.e. the illness_duration - undetected_period).

Agents become infected when they are within a radius of 1 of another agent that is infected that is not in self-isolation and does not have antibodies.

The probability of agents becoming infected by coming into contact with other infected agents must be determined by the global variable infection_rate which must range from 0 to 100 (0 = 0% chance of becoming infected, 100 = 100% chance of becoming infected).

Agents who are infected must turn red to indicate this and also set their infected_time variable (stored in the agents own variable) to the value stated in the global variable illness_duration and reduce by 1 per tick to indicate the passing of time of the illness.

The survival rate of an agent must be dictated by the variable survival_rate (i.e. if survival_rate is set to 80 this would mean that 80% of the time agents survive the illness)

If an agent survives an infection at the end of the infected_time the agent must develop antibodies (stored in the agents own variable called antibodies) which must be set according to the global variable immunity_duration and reduce by 1 per tick to indicate the reduction of antibodies over time. An agent that has antibodies must turn black while they have antibodies as a visual representation.

Whilst run_model is being called (whist your model is running) you must update the following global variables in real-time (after each tick):

The global variable total_infected_percentage must show the percentage of the current total living population that are infected.

The global variable green_infected_percentage must show the percentage of the current green living population that are infected.

The global variable blue_infected_percentage must show the percentage of the current blue living population that are infected.

The global variable total_deaths must be update on every tick and show the number of people who have died in total.

The global variable green_deaths must be update on every tick and show the number of green people who have died in total.

The global variable green_deaths must be update on every tick and show the number of blue people who have died in total.

The global variable total_antibodies_percentage must show the percentage of the current total population that are have antibodies and are immune to infection.

The global variable green_antibodies_percentage must show the percentage of the current green population that are have antibodies and are immune to infection.

The global variable blue_antibodies_percentage must show the percentage of the current blue population that are have antibodies and are immune to infection.