Designing an Agent-Based Model for a City-Level Simulation of

COVID-19 Spread in Cyprus

Philip Fayad

, Stylianos Hadjipetrou

, Georgios Leventis

1,3 c

, Dimitris Kavroudakis

and Phaedon Kyriakidis

1,3 e

Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol, Cyprus

Department of Geography, University of the Aegean, Mytilene, Greece

Eratosthenes Centre of Excellence, Limassol, Cyprus

phaedon.kyriakidis@cut.ac.cy

Keywords: ABM, Design, COVID-19, Spread, Spatial, Simulation, Cyprus.

Abstract: To date, several epidemiological agent-based models have been developed to study the spread of the highly

infectious coronavirus (SARS-COV) disease in different countries. However, no extensive effort has been

implemented for the Republic of Cyprus. In this research, we present the design framework of the EPIMO-

LCA agent-based model that respects the SEIR epidemiological model and attempts to simulate human

mobility to predict the spread of COVID-19 at a city-level of detail. More specifically, we fully describe the

three main model components (agents, environment and interactions) and explain all anticipated

functionalities, processes, input and output elements. The agent-based model envisaged is expected to

contribute to a better understanding of the interactions between intervention measures and disease spread for

the city of Larnaca, the Republic of Cyprus, and beyond.

1 INTRODUCTION

A pressing need for understanding the behaviour of

epidemiological diseases has emerged in light of the

recent COVID-19 (SARS-CoV-2) experience.

Having spread all over the world, leaving millions of

human losses behind, coronavirus 2 has posed

significant challenges to humankind at every level.

Cyprus was no exception, counting more than 1,500

deaths and 600,000 (>50% of the total population)

infections (World Health Organization, 2023). The

particularly high human-to-human inapparent

transmission rate in combination with the severe

implications that COVID-19 may have on human

health, and especially the vulnerable group of people,

have led to an urgent need of seeking ways to limit

the virus spread. Intermittent non-pharmaceutical

interventions (NPIs) have proven able to reduce the

infection spread rate (Buhat et al., 2020) but may, on

https://orcid.org/0000-0002-1442-8361

https://orcid.org/0000-0002-8808-3319

https://orcid.org/0000-0003-2850-4342

https://orcid.org/0000-0001-5782-3049

https://orcid.org/0000-0003-4222-8567

the other hand, have significant impact on the

socioeconomic aspects of human life in the medium-

and long-term (Novakovic & Marshall, 2022). The

above highlight the importance of forming effective

policies and taking better-informed and timely

decisions in regard to prevention, control and

mitigation of COVID-like viruses spread (Cui et al.,

2006). In this context, radical advances on

epidemiological (disease spread) modelling have

marked the aftermath of the recent pandemic to

support scientists and decision makers in

understanding the underlying mechanisms driving the

spread of the infection.

Intricate relationships between social and physical

processes, including the transmission of infectious

diseases, have recently been at the focal point of

spatial sciences and geography. Indeed, modelling of

human-environment interactions enables insights into

the spatial dynamics of these relationships, leading to

218

Fayad, P., Hadjipetrou, S., Leventis, G., Kavroudakis, D. and Kyriakidis, P.

Designing an Agent-Based Model for a City-Level Simulation of COVID-19 Spread in Cyprus.

DOI: 10.5220/0012054000003546

In Proceedings of the 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2023), pages 218-224

ISBN: 978-989-758-668-2; ISSN: 2184-2841

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

improved decision making and addressing of

complex challenges. Mathematical models,

representing simple or complex abstractions of

mobility and interactions among individuals and

populations, have been central to infectious disease

response and decision-making process (Bachar et al.,

2021; Hethcote, 1989, 2000; Kifle & Obsu, 2022;

Vytla et al., 2021). This is particularly useful in the

case of infectious diseases towards increasing our

understanding on the drivers of transmission (Crooks

& Hailegiorgis, 2014; Merler et al., 2015; Willem,

2015) and non-linear causal effects and providing the

ability to simulate future scenarios (Gomez et al.,

2021; Kerr et al., 2021; Kyriakidis et al., 2021;

Shastry et al., 2022; Silva et al., 2020). Among the

most widely used computational models, agent-based

models (ABMs) have become popular due to their

inherent ability to model and simulate mobility

transitions of autonomous agents within complex

systems (Mehdizadeh et al., 2022) based on a set of

behavioural rules guiding their interactions

(Bonabeau, 2002). This particular modelling

architecture is widely applied in fields where

simplifying complexity is crucial, like economics

(Heckbert et al., 2010), mobility (Loraamm, 2020)

and supply chain (Chen et al., 2013). Therefore, it is

only fitting that such modelling approach be utilised

in the field of epidemiology. In relative terms, agents

can be individuals, groups, organizations, or even

non-human entities able to interact with each other

and their environment in various ways, depending on

the specific model. Consequently, each agent can be

described by individual properties but also given a

certain status at each discrete time step, as described

in the following section. The bottom-up approach of

ABMs allows for the realistic simulation of

interactions among individuals both in space and time

which in turn could give insights to population-scale

patterns (Tracy et al., 2018) of the phenomenon under

study.

A plethora of ABMs, drawn from the extensive

data gathered for the needs of modelling COVID-19

transmission, has been developed during the last two

years. The majority is built upon dynamic equations

linking compartments; those being susceptible (S),

exposed (E), infected (I), and recovered (R). As the

letters S, E, I, R represent, the health status of a given

human population within a dynamic infectious

disease context, health experts and scientists should

always regard any pandemic situation within a tight

temporal context so as to accomplish its minimum

possible spread, while achieving better forecasts

(Yang et al., 2021) for the future.

Different variations of the so called SEIR model,

have been extensively used in an ABM context to

assess planned interventions used to combat COVID-

(Altun et al., 2021; Kim & Cho, 2022; Taghizadeh

& Mohammad-Djafari, 2022). A systematic review

on agent-based social simulation of Covid-19 is given

by Lorig et al. (2021) while Kong et al. (2022)

provide a scoping review of the compartmental

structures used in the dynamic models developed for

COVID-19 spread. Application examples include

Covasim (Kerr et al., 2021), an ABM model to project

COVID-19 dynamics and interventions, COVID-

ABS (Silva et al., 2020) developed in an attempt to

simulate health and economic effects of interventions

and CityCOVID (Ozik et al., 2021), incorporating

behavior and social interaction in a real-case scenario

in Chicago.

Figure 1: Diagram of a typical S.E.I.R Model.

In cases where the adoption of SEIR models is

needed to allow health stakeholders and officials to

extract concrete conclusions and make predictions,

proper model calibration is imperative in order to

understand both the static and dynamic nature of the

phenomenon under study. Towards that end, Ajbar et

al. (2021), answering to the critical -for such

understanding and for the model calibration as well-

inverse modelling problem, identified their model

parameters using real time data of Saudi Arabia and

subsequently used the computed values to analyse the

behavior of the model. Over time, SEIR models

showcased their advantages rendering them

important tools for policy makers and governments

when coupled with network-driven dynamics being

capable of predicting with high accuracy any

epidemic peaks taking into account external factors

such as the virus transmission over air (Liu et al.,

2020). Although, SEIR models might be considered

adequate for modelling and forecasting other

Designing an Agent-Based Model for a City-Level Simulation of COVID-19 Spread in Cyprus

219

diseases, as far as COVID-19 is concerned, Moein et

al. (2021) showed that a more complex approach that

takes into account mortality rates and hospital

capacity as well should also be considered when

attempting to make a pandemic forecasting feasible.

Building upon the need for a more complex

approach to COVID-19 modelling, the present study

introduces an ABM to simulate the spread of the

disease based on human mobility and evaluate the

impact of governmental countermeasures, with a

focus on the Republic of Cyprus as the study area. In

addition to simulating the distribution of future cases

and deaths, the model is designed to be able to predict

the possible outcomes after the implementation of

strong governmental countermeasures, thus, allowing

the evaluation of such actions in preventing COVID-

19 spread locally.

To the best of the authors’ knowledge, no similar

studies have been conducted for the particular region

of interest (Larnaca, Cyprus) that utilise agent-based

modelling to study the spread of COVID-19. Spatial

behavior is based on the Human Mobility Schedule

(HMS) and is designed to be incorporated to the

model through a questionnaire survey that represents

the human mobility of Cypriot citizens after each

iteration of the ABM simulations.

2 METHODOLOGY

To simulate the spread of COVID-19 using the agent-

based approach, we identify NetLogo software as the

most widely used open-source solution capable to

represent and analyse a model of this capacity. This

section describes the main logic of EPIMO-LCA

agent-based model and explains its functionalities,

processes, properties, input and output elements.

The Human Mobility Schedule (HMS) is a crucial

component of the model as it contains all the

necessary information regarding mobility behavior

(how and when the agents move based on age group)

on an hourly basis. The aim of the HMS is to describe

the main mobility activities of individuals in Cyprus

according to their age during a typical day. It is based

on the analysis of real data resulted from a two-part

questionnaire survey addressed to Cypriot citizens. In

the first part of the questionnaire, demographic

information (gender, age, region of residence and

occupation) as well as mobility characteristics (means

of transport, mobility type, frequency and distance)

are requested. The second part asks for the

completion of a mobility schedule indicating the

person's indicative location/activity per hour within a

typical day.

2.1 Agents

Based on their age, there are three principal types

(breeds) of agents in the EPIMO-LCA model. Each

bread describing a specific group of people, moves

differently according to the HMS: 1) Mostly-out

agents (age: 18-64) represent the group of people that

mainly move from-to their work office, 2) Students

(age: 6-24) represent the group of people that mainly

move from-to educational institutions and 3) Mostly-

at-home agents (age: 18+) represent the group of

people that stay mostly at home (unemployed, elderly

and work-from-home individuals).

Table 1: Agent properties.

No Property Type Function

1 Age numeric

Initial setup of

population % by age

group

Chronic

disease

True/

False

Initial setup of

population % with

increased chance of

mortality

3 Mask wear

True/

False

Initial setup of

population % that

respect the mask-

use measure

Social

distance

True/

False

Initial setup of

population % that

respect the social

distance measure

5 Immune

True/

False

Initial setup of

population % that

are immune

(natural, vaccination

or medicine)

6 Healthy

True/

False

Initial setup of

population % that

are healthy

7 Infected

True/

False

Initial setup of

population %, and

later after being

exposed

8 Susceptible

True/

False

Agents that are in

close proximity with

an infected agent

9 Exposed

True/

False

Agents at high risk

to be infected

(incubation period)

10 Recovered

True/

False

After the pass of 14

days being infected

11 Deceased

True/

False

After being infected

and if at high risk

SIMULTECH 2023 - 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

220

Agents are defined by a set of eleven properties

(table 1). Properties 1 to 7 are set ahead of model

initialization (model parameters), while properties 6

to 11 are dynamic and change as the agents are

interacting with each other, describing the state of the

agent.

2.2 Actions and Behaviours

At the start of the simulation, agents are assigned one

of the 3 states (Healthy, Immune or Infected)

depending on the parameters initially set. After model

initialization, agents move using the road network

interacting with each other and gradually evolve to 4

more states (Susceptible, Exposed, Recovered,

Deceased). Depending on their state, agents behave

as follows. Healthy, Immune, Susceptible, Exposed

and Recovered agents continue to move based on

their breed and according to HMS. Immune and

Recovered agents stay in this state forever and cannot

transmit the virus nor get infected. Therefore, re-

infection is not possible. Infected agents can also

continue to move (according to HMS) or implement

the stay-at-home quarantine rule, depending on the

initial parameter set.

Moving progressively from one state to another,

there are five intermediate stages where agents evolve

1) from Healthy-to-Susceptible, after being in close

proximity and in contact with an infected agent, 2)

from Susceptible-to-Exposed, after in direct contact

with an Infected agent and if at high risk of infection

(no mask use, no social distancing, no immunity), 3)

from Exposed-to-Infected, after the pass of n days

(incubation period), 4) from Infected-to-Recovered,

after the pass of 14 days being infected and, lastly, 5)

from Infected-to-Deceased, after the pass of three

days and if at high risk of mortality (age group,

chronic disease).

2.3 Environment

Agents interact in a spatial environment using

the road network of the city of Larnaka, Cyprus. Each

time-step (tick) represents one hour of human

activity. Moreover, buildings are also mapped and

used as origin - destination for moving agents.

More specifically, these buildings correspond to high-

risk areas for virus transmission and are represented

as points (centroids) in space with different colours

based on building type (residential, offices, health

centers, educational institutions, shopping centers,

etc) and represented with various buffer sizes

depending on crowd capacity.

Having one of the highest car ownership rates in

the world (629+ cars per 1000 inhabitants), Cyprus'

residents rely heavily on private car commuting

(Obrien, 2022). Based on this fact, in our simulation

we don’t include any public transportation parameters

assuming all agents use private cars with an average

speed of 50km/h (speed limit in urban areas).

2.4 The Proposed ABM

Initially, the agents are randomly distributed in

residential buildings and as the model starts, they

begin to move (according to HMS) to other buildings.

As a result, while respecting each building crowd

capacity, agents are continuously gathering in closed

limited areas and interact with each other. Thus, the

virus begins to spread and emerge. The simulation

continues until all agents are either Healthy, Immune,

Infected, Deceased or Recovered. In our proposed

model, the simulation of COVID-19 spread adheres

to the following process (figure 2):

1. Agents are created according to setup

parameters (Initialization of the model).

2. Agents are assigned with a colour based on

their initial state (Healthy - green, Immune -

grey and Infected - red).

3. Agents are allocated randomly in space,

initially within residential areas while

respecting crowd capacity.

4. Healthy and Immune agents move according

to HMS. Infected agents also move

depending on preset rule (if 14-day home

quarantine parameter is disabled).

5. Non-immune and non-infected agents that

are in close proximity to infected agents turn

to yellow colour (susceptible).

6. Susceptible agents at low risk of infection

turn green colour (healthy).

7. Susceptible agents at high risk of infection

turn orange colour (exposed).

8. Exposed agents turn red colour (infected)

after n days (n*24 ticks).

9. Infected agents at high risk of mortality turn

black colour (deceased).

10. Infected agents at low risk turn blue colour

(recovered) after 14 days (336 ticks).

11. The Simulation stops when all agents are

either healthy, immune, infected, deceased

or recovered.

Designing an Agent-Based Model for a City-Level Simulation of COVID-19 Spread in Cyprus

221

Figure 2: The EPIMO-LCA model flow chart.

Through NetLogo’s user interface, specific

demographic and epidemiological input parameters

can be easily adjusted and countermeasure policies

can be enabled or disabled (mask use, vaccination,

lockdown, etc.) in the simulation. More specifically,

the user using a slider can define the number of: a)

the total population size (ranging from 10-600), b)

people initially infected (% of total population), c)

immune people (% of total population), d) people

with chronic diseases (% of total population), e)

people in each age group (% of total population), f)

mortality rate, g) recovery rate, h) hospital

capacity/beds (ranging from 10-600), i) people that

adhere to social distancing (% of total population), j)

people vaccinated or on medication (% of total

population). Additionally, the user using a switch can

enable or disable important parameters regarding: k)

mandatory mask use, l) shops closure (entertainment,

restaurants, bars, shopping) and m) full lockdown

enforcement.

2.5 Data

All the required data are obtained from freely

available open repositories (OpenStreetMap and

National Open Data Portal of Cyprus - data.gov.cy)

and from local governmental authorities (Department

of Land and Surveys and Statistical Service of the

Republic of Cyprus). Additionally, the latest

CORINE Land Cover (CLC2018) product by the

Copernicus Land Monitoring Service is considered an

important dataset for the determination of land use

areas at an 100m spatial resolution using Sentinel-2

and Landsat-8 satellite data. All these data are

necessary for the development of the agent-based

model and additionally will be also used for the

purposes of data analysis and model validation.

2.6 Expected Results and Validation

The proposed ABM is expected to contribute to the

better understanding of the emergence and course of

the dangerous virus in the community while

considering the effects of important coping policies

as well as human mobility behaviours for the

simulation of the disease spread at a city-level. It aims

to help experts and decision makers to combat future

epidemic and pandemic events. By showcasing all the

critical statistics and graphs (number of healthy,

immune, susceptible, exposed, infected, recovered

and deceased people) in real time, the model aims to

be an efficient tool for the prediction of virus spread,

the evaluation of the important coping measures and

the estimation of the possible consequences.

To validate the effectiveness of the model, four

different case scenarios will be simulated for

comparison with real data. The case scenarios

concern specific key time periods (lockdown,

mandatory mask use, etc) during the COVID-19

(2020-2022) pandemic in Cyprus. For each

simulation, the coping policies as well as

demographic and epidemiological data will be

simulated using real data as input parameters.

Simulation results will be then compared with the

actual situation (real number of infections, people

immune, deaths, etc) that the Republic of Cyprus

experienced. In this way, we can evaluate the

effectiveness of the model.

3 CONCLUSIONS

Respecting the concept of the SEIR epidemiological

model, in this research we presented the design

framework of an ΑΒΜ for COVID-19. The "EPIMO-

SIMULTECH 2023 - 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

222

LCA" model is designed with the goal to simulate

human mobility behavior in order to predict future

outcomes (spatial distribution of cases and deaths) at

a city-level of detail, while also considering important

governmental countermeasures (like mandatory mask

use, lockdown enforcement, etc.). In our proposed

design we specified the types and properties of the

agents, their actions as well as the environment that

they will interact during the simulation. Additionally,

we identified the need for data concerning human

spatial mobility behavior as such data does not exist

for Cyprus. This is why we suggest the

implementation of a questionnaire survey. The results

of this survey will be analysed and lead to the

development of the HMS with the scope to be

integrated in the model as an immediate future step.

In this way we can produce a representative activity

schedule that describe the mobility of the Cypriot

citizens (per age group) during a typical day. Thus,

the HMS is a critical component as it defines the way

that the agents will move during the ΑΒΜ

simulations. After the actual development of the

model, the validation process will follow using real

data and specific case scenarios. Once the

methodology and all parameters are finalized, the

model can be expanded and parameterized for the rest

of the districts of Cyprus (Limassol, Nicosia, etc.).

ACKNOWLEDGEMENTS

The PhD study titled “Agent-based modelling and

simulation of human mobility in the context of

infectious disease spread”(epimogeo-covid.cut.ac.cy)

in the field of “Epidemiological Monitoring with the

use of Geoinformatics” is conducted at the

Department of Civil Engineering and Geomatics at

the Cyprus University of Technology. The PhD

research has received scholarship grants from the

State Scholarships Institution of Cyprus

(cyscholarships.gov.cy) and the Sylvia Ioannou

charitable foundation (sylviaioannoufoundation.org).

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