Studying the Impact of Transportation During Lockdown on the Spread

of COVID-19 Using Agent-Based Modeling

Shikha Bhat

∗ a

, Ruturaj Godse

∗ b

, Shruti Mestry

and Vinayak Naik

BITS Pilani, Goa, India

Keywords:

Agent-Based Simulation, COVID-19, Artiﬁcial Intelligence.

Abstract:

The COVID-19 pandemic has posed challenges for governments concerning lockdown policies and trans-

portation plans. The exponential rise in infections has highlighted the importance of managing restrictions on

travel. Previous research around this topic has not been able to scale and address this issue for India, given

its diversity in transportation networks and population across different states. In this study, we analyze the

patterns of the spread of infection, recovery, and death speciﬁcally for the state of Goa, India, for twenty-eight

days. Using agent-based simulations, we explore how individuals interact and spread the disease when travel-

ing by trains, ﬂights, and buses in two signiﬁcant settings - unrestricted and restricted local movements. Our

ﬁndings indicate that trains cause the highest spread of infection within the state, followed by ﬂights and then

buses. Contrary to what may be assumed, we ﬁnd that the effect of combinations of all modes of transport is

not additive. With multiple modes of transport activities, the cases rise exponentially faster. We present equiv-

alence points for the number of vehicles running per day in unrestricted and restricted movement settings, e.g.,

one train a day in unrestricted movement spreads the disease as eight trains a day in restricted movement.

1 INTRODUCTION

In recent years, the world has faced innumerable chal-

lenges posed by the Severe Acute Respiratory Syn-

drome CoronaVirus 2 (SARS-CoV-2), also known as

COVID-19. The disease spreads from one infected

individual to another through respiratory droplets and

physical contact. Due to a lack of awareness, re-

search, and interventions during the initial outbreak,

the virus rapidly spread to over a hundred countries

within three months, compelling the WHO to declare

a global pandemic on March 11, 2020.

To prevent a similar disease from spreading in this

fashion, we urgently need to study the disease spread

using epidemiological modeling techniques. Mathe-

matical models involve constant values that determine

only one outcome. In contrast, computer simulations

that consist of agent-based models are stochastic and

have a better chance of mimicking reality. An agent-

based model involves agents as autonomous entities

https://orcid.org/0000-0002-3989-2005

https://orcid.org/0000-0002-1091-4311

https://orcid.org/0000-0001-9672-1635

https://orcid.org/0000-0003-3637-2167

These authors contributed equally to this work.

and an environment in which the agents interact with

each other to portray any real-life system.

India was one of the most affected countries in the

world. Goa, a state in India, had a test positivity rate

of ﬁve times the national average. This paper presents

an agent-based simulation created in NetLogo soft-

ware (net, 1999) that helps us design an epidemio-

logical model incorporating spatial data of population

and transportation. Using the model, we estimate the

spread of COVID-19 in Goa through railway, road,

and airway transportation networks. The agents are

simulated as individuals interacting with each other as

they travel via the transportation networks and unfold

the spread of the infection with time. We simulate

the transportation networks with varying frequency,

introduce restrictions on local movements, and mea-

sure the rise in the population’s number of infections

and recoveries., Using the simulation, we get an in-

sight into speciﬁc details, such as an upper bound on

vehicles required to minimize the disease spread, the

rate of change of infections, and the impact of the

restriction on movement, i.e., lockdown. The model

will help the general public perceive the best-case to

worst-case scenarios under different degrees of move-

ment. Our study will help policymakers to propose

effective strategies to minimize the disease’s spread.

Bhat, S., Godse, R., Mestry, S. and Naik, V.

Studying the Impact of Transportation During Lockdown on the Spread of COVID-19 Using Agent-Based Modeling.

DOI: 10.5220/0011733400003393

In Proceedings of the 15th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2023) - Volume 1, pages 80-92

ISBN: 978-989-758-623-1; ISSN: 2184-433X

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

We validate the model by comparing it with real-

world data.

In the following sections, we describe past re-

search efforts to create pandemic models, clearly

lay out the problem statements we address, and the

methodology we use to approach the problem. Later

sections discuss the details of our implementation of

the methods, and we then present the results obtained

from the simulation model. The conclusion summa-

rizes the paper and discusses ways the presented work

can be extended further.

2 RELATED WORK

The COVID-19 global pandemic has urged re-

searchers to study the infection spread to imple-

ment appropriate lockdown policies and other non-

pharmaceutical interventions. Modeling the pan-

demic in a simulation, analyzing how the infection

spreads among people, and watching the patterns

emerge are effective ways to study the pandemic. Pre-

vious works can broadly be classiﬁed into determin-

istic and stochastic modeling. Our study uses agent-

based modeling, which is a sub-category of stochastic

modeling. These methods are detailed as follows.

2.1 Deterministic Modeling

Deterministic modeling attempts to study some part

(or form) of a real-life problem in mathematical

terms. Previously, there have been many studies

that mathematically model the COVID-19 outbreak.

These models are deterministic, ranging from simple

math (Tang and Wang, 2020) to nonlinear differential

equations (Iboi et al., 2020) to fractional order math-

ematical modeling (Ahmad et al., 2020). Determin-

istic epidemic models, such as the Susceptible, Ex-

posed, Infected, and Recovered (SEIR) model (Car-

cione et al., 2020)(Reiner et al., 2021), are useful tools

in epidemiology. However, despite the many advan-

tages of deterministic models, it can be difﬁcult to in-

clude realistic population networks in such models.

Thus, we usually use the SEIR techniques in com-

bination with some stochastic parameters to create a

better model of the real situation (Omar et al., 2021).

2.2 Stochastic Modeling

Purely mathematical modeling of a complex situation,

such as a pandemic, often cannot accurately represent

a real-world scenario. In the past, there have been ef-

forts to produce stochastic processes to quantitatively

reproduce the spread of a pandemic and the number

of infections.

Mainly three types of stochastic epidemic simula-

tions have been explored. The ﬁrst builds on mathe-

matical ODE deterministic models by using stochas-

tic differential equations (Zhang et al., 2020). The

second method is the Markov Chain Monte Carlo

method, where the probability of each possible event

is assessed at each time step, and one of the events is

chosen randomly (weighted by its probability of oc-

currence) (Chatterjee et al., 2020). Using a Monte

Carlo simulation model, Bartsch et al. (Bartsch et al.,

2022) compared what would happen if face masks

were used versus not used until the population gets

their ﬁnal vaccination. The third and ﬁnal approach

is agent-based models with stochastic parameters, in

which each agent behaves in certain ways to under-

stand the underlying movements and interactions be-

tween individuals. Wilder et al. (Wilder et al., 2020)

developed a stochastic SEIR agent-based model for

the spread of SARS-CoV2 and discusses the role of

age distribution and family structure in the model.

Our study uses agent-based modeling to understand

the spread of the disease among locals and passengers

traveling via different transportation methods.

Other aspects of our paper include the geospatial

representation of a state, restrictions on local move-

ment, and the effect of transportation networks on the

disease spread. Previously, Koo et al. (Koo et al.,

2022) created an agent-based model, GeoDEMOS-R,

incorporating a geographical, demographic, and epi-

demiological model of Singapore for respiratory dis-

eases. We simulate time-speciﬁc restrictions on the

movement in the lockdown scenario. We use QGIS

and NetLogo to simulate a geospatial environment of

Goa.

The research on the spread of COVID-19 through

transportation networks in India, while comparing the

restrictions on local movement, is limited. Talekar et

al. (Talekar et al., 2020) implemented cohort strate-

gies on top of a city-scale agent-based epidemic sim-

ulator. They mapped the agents on a geospatial net-

work and studied the effect of grouping agents to-

gether in travel in Mumbai railways. Our simulation-

based model treats each agent as an autonomous indi-

vidual living in restricted and unrestricted lockdown

scenarios. In our study, we model trains, ﬂights, and

buses separately and ﬁnd that they differ greatly in

terms of infection spread. Compared to their study of

the effect of creating cohorts on the spread via only

train, our study is broader. We focus on how we can

better the transportation and lockdown policies in a

state, using the information collected via the agent-

based simulation.

Studying the Impact of Transportation During Lockdown on the Spread of COVID-19 Using Agent-Based Modeling

3 PROBLEM STATEMENTS

In favor of allowing socio-economic activities, it is

essential to understand how the infection spreads via

the transport networks in the state. This will help in

planning efﬁcient rules and regulations in lockdown

and provide insight into predicting the future of the

outbreaks. To that end, the aims of the simulation

study are as follows.

1. Measure the cumulative number of infections

while varying the frequency of each mode of

transport

2. Measure the total number of recoveries while

varying the frequency of each mode of transport

3. Find an upper bound on the number of transport

vehicles allowed to run while keeping the maxi-

mum number of infections less than 20%

4. Measure the rate of change, as calculated in the

following equation, in the number of infections

for the initial days of the disease spread vis-

a-vis

the ﬁnal days

(b − a)

∗ 100 (1)

where a is the number of infected or recovered or

deceased people for a day, and b is the number of

infected or recovered or deceased people for the

following day

5. Compare different frequencies of transportation

during two scenarios of movements, which pro-

duce an equivalent number of infections within

twenty-eight days

6. Validate the model by comparing and correlating

it with real-world data

4 METHODOLOGY AND

IMPLEMENTATION

We describe our methodology and give details of im-

plementing our agent-based model using the data in

this section.

4.1 GIS Data Collection

Goa is connected to the other states of India by rail-

ways, airways, and roadways. We collect datasets

for these transportation networks in Goa from Open-

StreetMap using QGIS software (QGIS Development

Team, 2021) and generate desired shapeﬁles for the

model. These ﬁles contain vector point data for nine

railway stations along the Konkan railway route, one

Airport

Bus check post

Railway station

Railway Tracks

Roads

Legend

Figure 1: A map showing the railway stations, bus check-

posts, airport and transportation networks inside Goa.

airport at Dabolim, and four border-crossing points

on the national highways. We show the locations of

stops for all three modes in Figure 1.

We gather the rail, road, and air-related data (tra,

2022) to set up variable values such as the maxi-

mum number of trains or the number of passengers

onboard. The rail-related data includes trains’ num-

bers, routes, timings, and frequency. The airway-

related data consists of all the airlines operating in

Goa, with the timings and frequency of ﬂights. The

road-related data comprises the number of buses run-

ning from Goa to Maharashtra and Goa to Karnataka

via different entry points. We use the vector poly-

gon datasets for administrative boundaries to ensure

the agents move within the representative GIS space.

The data for COVID-19 statistics and the nationwide

lockdown policy timeline help us calibrate and vali-

date our model at the end.

4.2 Agent-Based Model

We elaborate on the three main simulation model

components - the agents, the environment in which

the agents live and travel, and the interaction between

the agents that result in disease transmission.

4.2.1 Agents

To simulate the COVID-19 disease’s spread among

humans, we create agents representing the human

population of Goa in the GIS space. We use a scaled-

down population of Goa to create the total number of

agents in each district of Goa. The people in the dis-

tricts can move around, get in and out of the trains,

ﬂights, or buses, get exposed to the disease, and in-

fect other people. We deﬁne the people currently in

the state as a class of persons and those traveling via

train, ﬂight, or bus as a class of passengers. The ’per-

ICAART 2023 - 15th International Conference on Agents and Artiﬁcial Intelligence

sons’ own a district variable that allows us to track

the district they belong to, and the ’passengers’ own

a vehicle variable, such as ﬂight or train number. The

passenger’s location changes with the vehicle’s loca-

tion.

4.2.2 Environment

This study focuses on creating an environment mim-

icking Goa’s transportation network via rail, road, and

air. We insert the vector datasets for administrative

boundaries, railway stations, airports, and highway

checkpoints to achieve that. The state boundaries

are loaded to ensure the conﬁnement of agents within

the GIS space. We deﬁne trains that use the Konkan

Railway route running through Goa from Pernem sta-

tion in the North Goa district to Loliem station in the

South Goa district. Dabolim airport denotes the loca-

tion for ﬂights to arrive and depart. Highway check

posts are the entry points at the Goa border to facili-

tate bus travel. We use the projected coordinate sys-

tem EPSG:7779 for the state of Goa.

4.2.3 Interaction

Our model employs the “SEIRD” framework model,

distinguished as Susceptible - Exposed - Infected -

Recovered - Deceased, demonstrating the disease pro-

gression in individual agents. Among all the different

types of epidemic modeling, the SEIRD framework

is the most extensive, keeping track of every stage.

We allow individuals to travel, and while they move,

they come in contact with each. Their interaction un-

folds the disease transmission. Each individual is as-

signed a variable that tracks her state of disease pro-

gression in terms of S-E-I-R-D. At the start of the sim-

ulation, some individuals are infected, and others are

all susceptible to the disease. As the individuals move

around and come in contact with others, they are ex-

posed to the disease, and after some time, given some

infection chances, they are infected with the disease.

After a recovery period, the infected individuals either

recover or continue to remain infected, depending on

the recovery chances. At this point, the chances of

death are also checked. If the infected individuals die,

they are removed from the simulation.

4.3 Implementation

We simulate all three modes of transport – trains,

ﬂights, and buses, individually and in combination.

For each mode, we initialize the vehicles to their start-

ing locations and let people in the district become

passengers that board these vehicles. We update the

vehicle’s and passenger’s positions every hour and

Initialise vehicle to its starting location

Start

Let some people in the district board the vehicle

Let the passengers in the vehicle interact with each

other according to the S-E-I-R-D model

Yes

Reached the last stop?

End

Move to next station

Update the position of the vehicle and the passengers

travelling in it

Let some passengers alight the vehicle

Figure 2: Flowchart for the simulation of one vehicle. Mul-

tiple such vehicles run in parallel, with different stations and

start times.

let the passengers interact with each other using the

SEIRD model. New passengers board the vehicle,

and the old passengers alight the vehicle at different

stations/ports. Then, the people who alight at these

stations travel home. The ﬂowchart explaining the

simulation course for a vehicle is shown in Figure 2.

We run multiple such vehicles simultaneously from

different stations and at different times, following the

transportation data obtained.

People outside the vehicles randomly mingle in

their districts and interact, spreading infection within

a speciﬁed radius. We build the simulations repre-

senting the movement of people in two ways. In the

ﬁrst scenario with unrestricted movement, we allow

the individuals to move within the state and utilize the

transportation networks for inter-state travel. The sec-

ond scenario with restricted movement allows inter-

state travel but restricts local movement. These are the

two movements allowed in India during the COVID-

19 period. We only monitor people in the state and

remove those traveling out of the state, via ﬂights and

buses, from our simulation.

Platform. We select the NetLogo software as our

modeling platform for its diverse characteristics and

Studying the Impact of Transportation During Lockdown on the Spread of COVID-19 Using Agent-Based Modeling

tools. The GIS extension provided by NetLogo en-

ables the model to accommodate vector as well as

raster GIS data. It an object-oriented agent-based

model, where each agent is an object. The moni-

tors, GIS space, and plots update at every tick, and

the results are tracked. A tick corresponds to an

hour in our simulation. We execute the model with-

out using GUI on a Red Hat Linux server. Behav-

iorSpace, an inbuilt tool in NetLogo, allows us to

execute multiple simulations in parallel by use of

threading. The BehaviorSpace-based simulation of

our model is called through the command line using a

NetLogo-headless batch ﬁle which executes each sim-

ulation for a different set of parameters, and the output

is exported as CSV ﬁles. We average the results over

three runs of the experiments and then plot graphs us-

ing MATLAB.

Input Parameters. To study each transport network’s

effect on the spread of the disease, we simulate the

model separately for railways, airways, and road-

ways, and then for all of the transport networks com-

bined. The numbers of vehicles and passengers are

extracted from real-world data. We downscale the

population of Goa to 1%, which results in 15, 000 in-

dividuals in the model. The number of people in each

vehicle is scaled down by a factor of 10. We simu-

late for twenty-eight days to capture the consequent

recoveries and fatalities. The parameters for the sim-

ulation, vehicles, and their respective values are given

in Table 1 and 2.

Table 1: Parameters for the agent-based simulation.

Parameter Value

Time period 28 days

Population 15,000

Chances of exposure 70%

Exposure radius 0.001 units (∼33.85 metres)

Incubation period 4 days

Illness period 10 days

Chances of infection 70%

Chances of recovery 80%

Chances of death 5%

5 RESULTS

We simulate agent-based models for trains, ﬂights,

buses, and a combination of these modes. We con-

duct each simulation with different seed values three

times and average the results.

5.1 Cumulative Number of Infections

with Varying Frequency for

Transportation

5.1.1 Trains

0 25 50 75 100

% of trains per day

100

Cumulative % of infected people

Cumulative % of infected people with varying % of trains

Unrestricted Movement

Restricted Movement

89%

Figure 3: The cumulative percentage of infected people

with an increasing number of trains running per day for un-

restricted and restricted movements. The cumulative num-

ber increases until we reach a saturation point of 89% with

more than 50% trains per day in the case of unrestricted

movement.

Figure 3 shows that unrestricted movement results in

45% to 89% of the population contracting the disease,

with 8% to 50% of trains running daily, respectively.

On average, the cumulative number increases at 17%

with an increase in the percentage of trains until 25%

of the trains. This rate decreases to 3.3% from 25% to

50% of trains. With more than 50% of trains per day,

it produces nearly the same cumulative percentage of

infections in the range of 87% to 89%.

When the local movement is restricted, we ob-

serve that the cumulative number of infections is 7%

with 8% of trains and 50% with 100% of trains run-

ning daily. Until 50% of trains per day, there is an

average increase of 6.6%. The increase is 1.8% with

a further rise in the number of trains. The restrictions

with more than 50% of trains result in nearly half of

the population getting the disease. However, we ob-

serve that the restriction on the local movement with

100% of trains lowers the cumulative number to 38%

as compared to the unrestricted movement. To reduce

the cumulative number, the trains need to be operated

with restricted local movement.

5.1.2 Flights

Figure 4 shows that an increase in the percentage of

ﬂights with unrestricted movement results in up to

63% of the population getting infected in twenty-eight

days. In contrast to the trains, the cumulative num-

ber of infections increases at 1% from 10% to 30%

of trains. The rate further increases to 8.2% for more

than 30% of ﬂights.

ICAART 2023 - 15th International Conference on Agents and Artiﬁcial Intelligence

Table 2: Parameters for each mode of transport in the simulation.

Mode of Transport Number of Vehicles Number of Passengers

Trains [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12] 50

Flights [10, 20, 30, 40, 50, 60, 70, 80, 90, 100] 9

Buses [30, 60, 90, 120, 150, 180, 210, 240, 270, 300] 5

10 20 30 40 50 60 70 80 90 100

% of flights per day

100

Cumulative % of infected people

Cumulative % of infected people with varying % of flights

Unrestricted Movement

Restricted Movement

63%

Figure 4: The cumulative percentage of infected people

with an increasing number of ﬂights ﬂying per day for un-

restricted and restricted movements. In the case of unre-

stricted movement, the number of infections keeps rising

up to 63%.

In the case of restricted local movement with 10%

to 30% of ﬂights, the cumulative percentage of in-

fected people remains less than 1%. We see that 1% of

the population gets the infection with 40% of ﬂights.

With a further increase in ﬂights to 100%, we observe

that 32% of the population contracts the infection.

On average, with more than 40% of ﬂights per day,

the cumulative number increases at 5.1%. The differ-

ence in cumulative numbers between the unrestricted

and restricted movements rises with an increase in the

percentage of ﬂights suggesting that, unlike trains, re-

strictions on local movement are beneﬁting even if the

percentage of ﬂights is more than 50%.

5.1.3 Buses

In Figure 5, we observe that buses, among other trans-

port modes, result in the least number of cumulative

infections even. The unrestricted movement shows

3% to 27% of the population getting infected with

10% to 100% of buses, respectively. We see an in-

crease of 1% from 10% to 50% of buses. It increases

at 4% from 60% to 100% of buses.

Similar to the case of ﬂights, the restricted local

movement with 10% to 30% of buses shows less than

1% of the infected population. The cumulative num-

ber for 40% to 90% of buses increases at 2.6%. After

90%, the rate is 9%. As the cumulative % with un-

restricted movement is 27%, it is unsafe to operate

buses with full frequency without local restrictions.

10 20 30 40 50 60 70 80 90 100

% of buses per day

100

Cumulative % of infected people

Cumulative % of infected people with varying % of buses

Unrestricted Movement

Restricted Movement

27%

Figure 5: The cumulative percentage of infected people

with an increasing number of buses running per day for un-

restricted and restricted movements. The number of infec-

tions keeps rising to 27% for the case of unrestricted move-

ment.

5.1.4 Combination

The combination of all transport modes shows the

worst scenario regarding the infected population. In

Figure 6, we observe that the unrestricted movement

results in 90% of the population getting infected with

10% of vehicles and the whole population with 40%

of vehicles.

With restrictions on local movement, we see that

68% of the population gets infected with 10% of ve-

hicles. The restriction in local movement lowers the

cumulative percentage but is not signiﬁcant enough to

contain the infection. The whole population contracts

the disease when more than 50% of vehicles operate.

This suggests that operating the transport network si-

multaneously transmits the infection the most.

10 20 30 40 50 60 70 80 90 100

% of vehicles per day

100

Cumulative % of infected people

Cumulative % of infected people with varying % of vehicles

Unrestricted Movement

Restricted Movement

100%

Figure 6: The cumulative percentage of infected people

with an increasing number of vehicles running per day for

unrestricted and restricted movements. In the case of unre-

stricted movement, The number of infections reaches 100%

with more than 40% of vehicles.

Studying the Impact of Transportation During Lockdown on the Spread of COVID-19 Using Agent-Based Modeling

5.2 Number of Recoveries with Varying

Frequency of Transportation

5.2.1 Trains

0 25 50 75 100

% of trains per day

% of recovered people

Maximum % of recovered people with varying % of trains

Unrestricted Movement

Restricted Movement

13%

18%

23%

Figure 7: The maximum number of recoveries with increas-

ing trains running per day for unrestricted and restricted

movements scenarios. The percentage of the population re-

covering each day increases until 50% trains per day, after

which it remains nearly constant.

The maximum number of recovered people observed

within twenty-eight days is 23% of the total popula-

tion, with 50% trains, i.e., six trains running per day

in case of unrestricted movement as shown in Figure

7. The percentage of the population recovering each

day increases from 9% to 23% for unrestricted move-

ment and from 1% to 8% for restricted movement

as the number of trains increases up to 50% trains,

which is six trains per day. After that, it remains al-

most constant at 13% and at 18% for further increase

in the number of trains for restricted and unrestricted

movements, respectively. The number of recoveries

increases faster initially with an increase in trains and

later ﬂattens. As the number of infected people in-

creases from 36% with one train to 71% with twelve

trains, as shown in Figure 11, we see an increase in

the number of recovered people from 9% to 18%, re-

spectively. It implies that the higher the frequency of

trains, the higher the number of infections in a short

period which, if people recover, leads to an increase

in the number of recoveries.

5.2.2 Flights

In Figure 8, we observe that the maximum number

of recovered people within twenty-eight days is 18%

of the total population, with 100% ﬂights, i.e., a hun-

dred ﬂights running per day, for unrestricted move-

ment. The number of recoveries increases steadily

with ﬂights for unrestricted and restricted movements.

In the case of unrestricted movement, from 10% to

30% of ﬂights, we see 1% of the population recover.

With 40% to 70% of ﬂights, the rate rises from 2% to

6% of the population to recover. With more than 70%

of ﬂights, the rate further rises from 11% to 18%. For

restricted movement, the number of recoveries ranges

from 1% to 12% for 10% to 100% of ﬂights. From the

number of infections, we know that restricted move-

ment minimizes the infection from spreading, allow-

ing people to recover. This, in turn, buys time for

healthcare facilities to be ready.

10 20 30 40 50 60 70 80 90 100

% of flights per day

% of recovered people

Maximum % of recovered people with varying % of flights

Unrestricted Movement

Restricted Movement

18%

Figure 8: The maximum number of recoveries with an in-

creasing number of ﬂights running per day for unrestricted

and restricted movements scenarios. The number of recov-

eries increases steadily with the number of ﬂights.

5.2.3 Buses

10 20 30 40 50 60 70 80 90 100

% of buses per day

% of recovered people

Maximum % of recovered people with varying % of buses

Unrestricted Movement

Restricted Movement

Figure 9: The maximum number of recoveries with an in-

creasing number of buses running per day for unrestricted

and restricted movements. The number of recoveries re-

mains constant up to two hundred and ten buses per day,

after which it increases in the case of unrestricted move-

ment. The case of restricted movement produces almost the

same number of recoveries as that of unrestricted.

We observe the maximum number of recovered peo-

ple within twenty-eight days, as shown in Figure 9,

to be 5% of the total population, with 100% buses,

i.e., three hundred buses per day in case of unre-

stricted movement. The number of recoveries re-

mains constant at 1-2% for up to two hundred and

ten buses per day, after which it increases to 4%

for the case of unrestricted movement. With an in-

crease in the frequency of buses, the restricted move-

ment produces a similar number of recovered people

as the unrestricted movement. Thus, buses are the

most favorable transport mode in minimizing infec-

ICAART 2023 - 15th International Conference on Agents and Artiﬁcial Intelligence

tions even with 100% of frequency without restricting

local movement.

5.2.4 Combination

10 20 30 40 50 60 70 80 90 100

% of vehicles per day

100

% of recovered people

Maximum % of recovered people with varying % of vehicles

Unrestricted Movement

Restricted Movement

53%

97%

Figure 10: The maximum number of recoveries is 97% of

the total population. It is observed with eleven trains, ninety

ﬂights, and two hundred and ten buses running per day for

unrestricted movement at 97% of the total population.

With 90% of total vehicles, i.e., eleven trains, ninety

ﬂights, and two hundred and ten buses running per

day, for unrestricted movement, the maximum num-

ber of recovered people observed within twenty-eight

days is 97% of the total population as shown in Fig-

ure 10. The number of recoveries keeps increasing

from 53% to 97% as the frequency of each transport

mode increases and reaches the maximum. We ob-

serve that with an increase in the frequency of vehi-

cles, the numbers of recovered people for restricted

and unrestricted are nearly equal. Allowing all trans-

port modes to operate together leads to a rapid in-

crease in the spread of infection. Thus, a combination

of transport networks must be avoided regardless of

restrictions on the local movement.

5.3 An Upper Bound On the Number of

Vehicles To Keep the Maximum

Number of Infections Less Than

20%

5.3.1 Trains

With 8% of trains, as shown in Figure 11, the maxi-

mum number of infections goes up to 36% in case of

unrestricted movement. Thus, we cannot allow trains

to run with unrestricted movement if we want to keep

the maximum number of cases less than 20%. In the

case of restricted movement, we can allow up to 25%

trains per day.

0 25 50 75 100

% of trains per day

100

% of infected people

Maximum % of infected people with varying % of trains

Unrestricted Movement

Restricted Movement

71%

36%

Figure 11: We show the maximum number of infected peo-

ple with an increasing number of trains running per day for

unrestricted and restricted movements. The number of in-

fections keeps rising until we reach a saturation point of

71% once we allow more than 75% trains per day.

5.3.2 Flights

In Figure 12, we see that the maximum number of in-

fections stays below 20% for up to 50% of ﬂights per

day in case of unrestricted movement. In restricted

movement, the maximum number of infections re-

mains below 20% even for the maximum frequency

of ﬂights.

10 20 30 40 50 60 70 80 90 100

% of flights per day

100

% of infected people

Maximum % of infected people with varying % of flights

Unrestricted Movement

Restricted Movement

42%

Figure 12: We show the maximum number of infected peo-

ple with an increasing number of ﬂights running per day for

the scenarios of unrestricted and restricted movements. The

number of infections keeps rising for twenty-eight days, and

we see a considerable increase of ∼ 5% for cases each time

as we increase the number of ﬂights per day by ten.

5.3.3 Buses

In the case of unrestricted movement, the number of

infections in Figure 13, stays below 20% till 90%

of buses per day. With the maximum frequency of

buses, the number of infections rises to 23%. In case

of restricted movement, the maximum number of in-

fections stays below 20% even for the maximum fre-

quency of buses.

5.3.4 Combination

With the combination of the least frequency for each

transportation mode, i.e., three trains, ten ﬂights, and

Studying the Impact of Transportation During Lockdown on the Spread of COVID-19 Using Agent-Based Modeling

10 20 30 40 50 60 70 80 90 100

% of buses per day

100

% of infected people

Maximum % of infected people with varying % of buses

Unrestricted Movement

Restricted Movement

23%

Figure 13: We show the maximum number of infected peo-

ple with an increasing number of buses per day for both un-

restricted and restricted movement scenarios. The number

of infections keeps rising for twenty-eight days. With fewer

buses, the number of infections increases steadily (∼1%)

each time we increase the frequency by ∼10%, but as the

number of buses crosses ∼50% (150), we see a sharp in-

crease.

thirty buses, the minimum number of infections, as

shown in Figure 14, is 57% in case of unrestricted

movement and 40% in case of restricted movement.

Thus, we cannot allow a combination of transports if

we do not want the maximum number of cases to go

above 20%.

10 20 30 40 50 60 70 80 90 100

% of vehicles per day

100

% of infected people

Maximum % of infected people with varying % of vehicles

Unrestricted Movement

Restricted Movement

91%

Figure 14: We show the maximum number of infected peo-

ple with an increasing number of buses per day for both

unrestricted and restricted movement scenarios. We see the

maximum number of infections for twelve trains, hundred

ﬂights, and three hundred buses running per day in unre-

stricted movement. It is 91% of the total population.

5.4 Rate of Change in Number of

Infection for the Initial Days

vis-

a-vis Final Days

5.4.1 Trains

In Figure 15, we observe that the number of infections

rose by 235% and 192% on the ﬁfth day with twelve

trains per day for unrestricted and restricted move-

ment, respectively. Since the incubation period is the

ﬁrst four days, we see a sharp increase in the number

of infections during that period. The growth decreases

rapidly the next day and eventually goes to zero, sug-

gesting that the number of infections decreases and

starts falling as more people recover.

0 7 14 21 28

Number of days

100

150

200

250

300

% of rate of change

Rate of change in no. of infected people with 100% of trains

Unrestricted Movement

Restricted Movement

235%

192%

Figure 15: We show the rate of change in the number of

infections over a period of twenty-eight days when twelve

trains operate per day.

5.4.2 Flights

0 7 14 21 28

Number of days

-100

-50

100

150

200

250

% of rate of change

Rate of change in no. of infected people with 100% of flights

Unrestricted Movement

Restricted Movement

186%

216%

Figure 16: We show the rate of change in the number of

infections over twenty-eight days when a hundred ﬂights

are operated per day.

In Figure 16, we see that the number of infections

rose by 216% and 186% on the ﬁfth day with hundred

ﬂights per day for unrestricted and restricted move-

ment, respectively. Similar to the case of trains, this

growth falls rapidly after the incubation period. Even-

tually, it goes to zero, suggesting that the number of

cases eventually saturates and starts falling as more

people recover.

5.4.3 Buses

In Figure 17, we do not see a sudden increase in the

rate of change for the number of infections even with

the maximum frequency of the buses. The rate of

change for the number of infections remains between

25-50% for most days. This suggests that the spread

of infection, in the case of buses, is much more con-

trolled than on trains and ﬂights.

ICAART 2023 - 15th International Conference on Agents and Artiﬁcial Intelligence

0 7 14 21 28

Number of days

-100

-75

-50

-25

100

% of rate of change

Rate of change in no. of infected people with 100% of buses

Unrestricted Movement

Restricted Movement

58%

61%

Figure 17: We show the rate of change in the number of

infections over twenty-eight days when three hundred buses

operate per day.

5.4.4 Combination

0 7 14 21 28

Number of days

500

1000

1500

2000

2500

3000

% of rate of change

Rate of change in no. of infected people with 100% of vehicles

Unrestricted Movement

Restricted Movement

2922%

2717%

Figure 18: We show the rate of change in the number of

infections over twenty-eight days when twelve trains, hun-

dred ﬂights, and three hundred buses operate per day.

In Figure 18, we see that the number of infections rose

by 2922% and 2717% on the ﬁfth day with twelve

trains, hundred ﬂights, and three hundred buses per

day for unrestricted and restricted movement, respec-

tively. This is more than twelve times the rise in in-

fections for any individual mode of transport with un-

restricted movement and more than fourteen times in

case of restricted movement. Thus, we see that a com-

bination of the three modes of transport grows faster

than linear.

5.5 Equivalence Point between

Unrestricted and Restricted

Movements

5.5.1 Trains

Figure 19 (a) and Figure 19 (b) show that the scenario

of unrestricted movement of people with one opera-

tional train per day produces an equivalent number of

infections as the scenario of eight operational trains

per day but with restricted movement.

0 7 14 21 28

Number of days

100

% of infected people

% of infected people with 8% of trains per day

Unrestricted Movement

Restricted Movement

36%

0 7 14 21 28

Number of days

100

% of infected people

% of infected people with 67% of trains per day

Unrestricted Movement

Restricted Movement

37%

70%

Figure 19: We show an equivalence point between unre-

stricted movement and restricted movement for trains. (a)

The scenario of 8% of trains with unrestricted movement

shows a peak of 36% of the population to be infected. (b)

The scenario of 67% of trains with restricted movement

shows a peak of 37% of the population to be infected.

5.5.2 Flights

Figure 20 (a) and Figure 20 (b) show the unrestricted

movement of people with ﬁfty operational ﬂights per

day produces an equivalent number of infections as

the scenario of hundred operational ﬂights per day but

with restricted movement.

5.5.3 Buses

The scenario of unrestricted movement of people with

two hundred and forty operational buses per day, as

shown in Figure 21 (a), produces an equivalent num-

ber of infections as the scenario of three hundred op-

erational buses per day but with restricted movement

a shown in Figure 21 (b).

5.5.4 Combination

The scenario of unrestricted movement of people with

30% of vehicles, as shown in Figure 22 (a), produces

an equivalent number of infections as the scenario of

60% of vehicles per day but with restricted move-

ment, as shown in Figure 22 (b).

Studying the Impact of Transportation During Lockdown on the Spread of COVID-19 Using Agent-Based Modeling

0 7 14 21 28

Number of days

100

% of infected people

% of infected people with 50% of flights per day

Unrestricted Movement

Restricted Movement

18%

0 7 14 21 28

Number of days

100

% of infected people

% of infected people with 100% of flights per day

Unrestricted Movement

Restricted Movement

42%

18%

Figure 20: We show an equivalence point between unre-

stricted movement and restricted movement for ﬂights. (a)

The scenario of 50% of ﬂights with unrestricted movement

show shows a peak of 18% of the population to be infected.

(b) The scenario of 100% of ﬂights show shows a peak of

18% of the population to be infected.

5.6 A Comparison of Simulation Data

with the Real-world Data

By plotting the results of the combined transporta-

tion, we ﬁnd that it produces a bell-shaped curve. In

Figure 23, we compare our simulation observations

for the combined transportation with the real-world

COVID-19 data for all three pandemic waves in Goa

for twenty-eight days. The ﬁrst wave begins in early

November 2020, the second wave begins in early May

2021, and the third wave begins in the second week

of January 2022. We plot the number of infections

normalized individually per the maximum number of

infections. The cases spread less rapidly as we go

from the ﬁrst wave to the third wave. We see that

the curve for the unrestricted scenario reaches a max-

imum on the same timeline as the second wave. The

curve for the restricted scenario reaches a maximum

on the same timeline as the ﬁrst wave and the third

wave. While there were strict restrictions at the time

of the ﬁrst and third waves, the restrictions were lesser

at the time of the second wave.

0 7 14 21 28

Number of days

% of infected people

% of infected people with 80% of buses per day

Unrestricted Movement

Restricted Movement

17%

0 7 14 21 28

Number of days

% of infected people

% of infected people with 100% of buses per day

Unrestricted Movement

Restricted Movement

23%

17%

Figure 21: We show an equivalence point between unre-

stricted movement and restricted movement for buses. (a)

The scenario of 80% of buses with unrestricted movement

show shows a peak of 17% of the infected population. (b)

The scenario of 100% of buses with restricted movement

show shows a peak of 17% of the infected population.

6 CONCLUSION AND FUTURE

WORK

The spread of COVID-19 infections depends on sev-

eral factors. This study presented how the infections

spread via transportation networks in two different

scenarios of movements allowed. This was done us-

ing the agent-based simulation modeling technique in

NetLogo, combined with the geospatial representa-

tion of Goa and its railway stations, airports, and bus

stop. Our source code and data ﬁles are publicly avail-

able at a GitHub

repository.

Previous closely related work looking at the im-

pact of transportation on the spread of the disease by

Talekar et al. (Talekar et al., 2020) focused on how

impactful the policy of creating cohorts of people is.

We, however, look at every person as an individual

and then measure how their movement via different

transportation modes will impact the spread.

In the future, this simulation can be extended to

multiple states and include international travel. More

precise road movements of people can be tracked and

https://github.com/Networked-Systems-Lab/

Simulating-COVID-19-Using-ABM

ICAART 2023 - 15th International Conference on Agents and Artiﬁcial Intelligence

0 7 14 21 28

Number of days

100

% of infected people

% of infected people with 30% of vehicles per day

Unrestricted Movement

Restricted Movement

77%

63%

0 7 14 21 28

Number of days

100

% of infected people

% of infected people with 60% of vehicles

Unrestricted Movement

Restricted Movement

77%

85%

Figure 22: We show an equivalence point between unre-

stricted movement and restricted movement for the combi-

nation of transport modes. (a) The scenario of 30% of ve-

hicles with unrestricted movement shows a peak of 77% in

the number of infections. (b) The scenario of 60% of vehi-

cles with restricted movement shows a peak of 77% in the

number of infections.

0 7 14 21 28

Numberofdays

0.25

0.5

0.75

Normalisedvalueofnumberofinfectedpeople

Comparingthenumberofinfectionsinthreewaves

ofthepandemicwiththesimulationstudy

Unrestricted

Restricted

FirstWave

SecondWave

ThirdWave

Figure 23: A comparison of our combined transportation

simulation results to the three waves of COVID-19 in Goa.

The peak of the second wave resembles the case of unre-

stricted and the peak of the third wave resembles that of

restricted movement. This is a positive indicator for the ac-

curacy of the simulation as cases rose less rapidly for the

third wave, similar to the case of restricted movement.

used to identify exposure to the virus. This will re-

quire much more computation power as we need to

track each person’s and vehicle’s movement. We may

add vaccinated people to the simulation, who will

have immunity or fewer chances of getting the dis-

ease.

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ICAART 2023 - 15th International Conference on Agents and Artiﬁcial Intelligence