Speeding Up the Simulation Animals Diseases Spread: A Study Case

on R and Python Performance in PDSA-RS Platform

Rodrigo Schneider

, Felipe Machado

, Celio Trois

, Glênio Descovi

, Vinícius Maran

and Alencar Machado

Laboratory of Ubiquitous, Mobile and Applied Computing, Polytechnic School,

Federal University of Santa Maria (UFSM), Roraima Av. 1000, Santa Maria, Brazil

{vinicius.maran, alencar.machado}@ufsm.br

Keywords: Digital System, Disease Spreading Simulation, Intelligent System.

Abstract: The control and prevention of livestock diseases play a crucial role in safeguarding business continuity,

simulating disease prevention and control measures are vital to mitigate future epidemics. In this sense,

modelling systems can be an effective tool that allows the simulation of different ways of spreading diseases

by configuring parameters allowing testing of different prevention measures. This work investigates

enhancing a system that simulates disease spread processes in animals. The stochastic model system was

developed in R; however, given a large amount of data and intense processing of stochastic functions that

simulate spreading and control actions, it required optimization. We focused on translating and modifying it

to Python using packages focused on data analysis, aiming to speed up the system execution time. We

conducted experiments comparing high computational cost functions executed in the actual model R with the

new proposal implemented in Python. The results showed that rewriting the code in Python has advantages

such as performance in time execution, which in Python is more than four times faster than R, memory usage

consumption in R uses 460 MB and 315 MB in Python.

1 INTRODUCTION

With the advancement of technology in the current

times, the use of data for decision making has become

increasingly important. This data is generated

through various digital channels, such as mobile

devices, the Internet, social media, e-commerce sites,

among others. Using the data has proven to be of great

use since its inception, as companies began to realise

its importance for various business purposes. With

this amount of data it is possible now, with the

increase of processing power to develop intelligent

systems that can help to make decisions, create

simulation about critical situations. In this sense,

several applications are using real data to create

intelligent systems in different areas with different

https://orcid.org/0009-0007-9095-9475

https://orcid.org/0009-0005-8179-1987

https://orcid.org/0000-0002-7386-9749

https://orcid.org/0000-0002-0940-9641

https://orcid.org/0000-0003-1916-8893

https://orcid.org/0000-0002-6334-0120

applications, such as healthcare, transportation, and

sustainable ecosystems that can help to avoid

economical loss in any kind of business (Omolbanin

et al.,2017

An example of intelligent systems is the control

and prevention of animal diseases. It is important for

public health, and may include measures such as

vaccination of animals, vector control, good hygiene

and food handling practices, as well as monitoring of

diseases in animals. In addition, globalization and

international trade in animals and animals’ products

have increased the spread of zoonotic disease

worldwide. It is essential that effective prevention

and control measures are implemented to minimize

the risk of epidemics. The dynamics of farms pose

some risks for the spread of diseases, such as isolation

Schneider, R., Machado, F., Trois, C., Descovi, G., Maran, V. and Machado, A.

Speeding Up the Simulation Animals Diseases Spread: A Study Case on R and Python Performance in PDSA-RS Platform.

DOI: 10.5220/0012556200003690

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024) - Volume 2, pages 651-658

ISBN: 978-989-758-692-7; ISSN: 2184-4992

651

of animal lots, human traffic, truck traffic, the useful

area of the farm or farm can influence possible

transmission of diseases (Jason et al.,2022). In this

area, one intelligent system using real data was

proposed by Descovi et al,.2022, which uses data

from farms to simulate the spread of diseases in

animals (e.g. cattle, swine, and small ruminants). The

system also allows researchers to test different actions

to control these diseases. As for livestock, it is

possible to control and isolate animals with disease,

so all animal transport is controlled. With this, it is

possible to develop a system that can simulate the

impact of a disease that spreads when an infected

animal is transported and create scenarios about the

life cycle of the disease and the impact on the farm or

production of the region (Jason et al,.2022).

This system was developed in R Language, using real

data collected from farms in Rio Grande do Sul state

(Brazil) and used in PDSA-RS platform (Descovi et

al, 2021, Perlin et al., 2023). It is noteworthy that the

R language was developed specifically for statistical

analysis and data visualization, but, when it comes to

data handling performance, it is important to consider

factors including: dataset size, data format, hardware

processing power, and the efficiency of the libraries

used to read and manipulate data (Ioannis,2020

). As

the volume of input data increases, the existing

system developed in R faces performance challenges.

To address this, we decided to reimplement some

computationally intensive functions into Python. This

shift enables us to conduct a comparative analysis of

performance and processing speed between the two

implementations (R and Python). By migrating to

Python, we aim to enhance the system’s efficiency

and efficacy, ensuring it can handle the ever-

expanding data landscape more effectively and

deliver optimal results. This transition allows us to

explore the potential benefits of Python performance

capabilities and adapt our system accordingly.

The methodology for developing the performance

improvement initiated by testing Python and R

functions used in the implementation of the system.

Firstly, we investigated different libraries to speed up

reading the dataset. After this, we analyzed the

different ways to update the dataset cells values, as

many functions include situations that are necessary

to change these values based on specific conditions.

In the second phase of the study, we will improve the

performance rewriting the code in Python

programming language, all the functions of the

model, all the functions work with update in values in

the data frame, change the information when the day

moves across the disease spread process, this needs

for update in values in the data frame has better

performance in Python.

The present paper is structured as follows. The

next section presents the related work on intelligent

systems applied to disease spread problems, showing

R and Python performance comparison studies.

Section 3 describes the disease spread and control

system, highlighting the functions targeted in this

work. Section 4 describes our evaluation and results

comparing the execution time of the original

functions implemented in R and their version in

Python. Section 5 outlines the conclusions and

research opportunities that emerged from this work.

2 MOTIVATION AND RELATED

WORK

This section presents about the digital system, that we

are working to improve performance and time execu-

tion and some related papers about the programming

language R and Python, and its primary characteristics.

2.1 Compartmental Stochastic Model

Stochastic modelling is a mathematical technique used

to model systems or processes that involve randomness

or uncertainty. The term "stochastic" refers to

randomness, and stochastic modeling involves

describing the behavior of systems or phenomena in

probabilistic terms. Common techniques in stochastic

modeling include stochastic differential equations,

Markov chains, queuing theory and Monte Carlo.

These methods provide ways to simulate, analyze, and

make predictions about complex systems affected by

randomness (Shah, 2022).

Infectious diseases are known as one of the most

critical threats to global health today. Climate change

and the accelerated growth of population are some

causes of the disease spread among humans. This

leads to increased infection at the global level; some

systems use water waste to control the level of

infection in some areas, which can be monitored in

real-time at the community level can help to avoid or

control some diseases and infections and act to

prevent the spread of the disease (Nathalie & Barbara,

2021). Several disease transmission systems were

developed and used to examine control strategies

(Rohit et al., 2020). Some systems to control the

spread were developed based on cell phone location,

using data from the geolocation of the cell phone to

understand the movement of the population and study

the spread of the disease process (Sachi et al., 2021).

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The system in the scope of this work is a model that

simulates the spread of disease in animals cultivated

for human consumption, so controlling the disease

and the spreading process is important because it can

have an economic impact in the region. Even if we

can isolate the animals that are infected, the animal

transport, circulation of employees in the farm, the

logistics to deliver product and material can impact

the spread process. The system created to prevent and

simulate the disease spread uses a stochastic model

developed in R, this digital system was created

considering real data collected for the transporting

process of the animals between farms located in Rio

Grande do Sul, Brazil (Manuel et al., 2021)..

2.2 R and Python Comparison

As stated before, in this work we implemented in

Python an intelligent disease spread and control

system for the spread, originally implemented in R.

Python and R are programming languages used in

data analysis, and efforts have been made to support

our research practices using these two computational

programming languages. However, an important

focus has been given to the visualization of

researchoriented studies and their comparable

efficiencies in analyzing large fragmented datasets.

While Python is a general-purpose language with an

easy-to-understand syntax, R’s functionality was

designed with statisticians in mind, providing field-

specific advantages such as great data visualization

capabilities, but R lacks performance and speed

process (Anupam et al., 2021).

The R programming language is a free, powerful,

open-source software package with extensive

statistical computing and graphics capabilities due to

its high-level expressiveness and multitude of domain

specific packages. R prioritizes ease of use and data

exploration, which can lead to less efficient memory

management compared to languages like C or Java,

which prioritize performance and memory efficiency

(Weijia, et al., 2016

The Python language is a general-purpose, open-

source tool for web, internet, and software

development applications; education and academia;

and numerical and scientific tasks, among others. In the

field of data analysis, some of the common packages

are: Pandas and Polars, ideal for data manipulation;

Statsmodels, for modelling and testing; scikit-learn, for

classification and machine learning tasks; NumPy

(Numerical Python), for numerical operations and

vectors; and SciPy (Scientific Python), for scientific

tasks, other libraries like Dask to huge amount of data.

One big advantage of Python language is that for data

extraction and data analysis is the possibility to use the

API Apache Spark and use Pyspark to work with big

data and with Spark Streaming that is data near real

time (S Saabith et al., 2021).

Python is favoured for data science, AI and

machine learning due to its concise code, enabling

easy testing and focusing on actual programming. It

uses significantly less code compared to other

languages, Python ranks second after Ruby in lines of

code required for projects, making it a top choice for

ML and AI. It boasts simplicity, fewer keywords, and

a clear syntax, making it accessible for students and

newcomers. Python prioritizes readability, fostering

collaboration and rapid open-source project

development (Abhinav et al., 2019).

One library used in this proposal of a new model

in this digital system is Pandas, that is used for data

extraction and analysis, which in the current version

2.0, has implementations such as the use of PyArrow,

which in turn is possible to accelerate the process and

make operations more efficient in terms of memory,

using the C++ implementation of Arrow. Arrow

allows sharing data between processes without the

need to copy them, which improves performance and

reduces memory consumption. In this sense,

PyArrow is a library that provides a bridge between

Python and Apache Arrow, offering resources to

work with data in a columnar format in an efficient

way and interoperable with other programming

languages (Pantelis et al., 2019

). For the R language,

the two libraries we use in the tests are data.table and

tidyverse, the tidyverse package is a collection of R

packages that were developed to facilitate the

manipulation, analysis and visualization of data.

Within it we will use dplyr which is one of the

packages used in tidyverse, offering a consistent and

intuitive syntax to perform data manipulation

operations. The dply package also has the advantage of

having a more readable and intuitive syntax, and its

philosophy is based on “data manipulation grammar”,

this package is used a lot in data analysis and

exploration in conjunction with ggplot. In the model

the data manipulation and data visualization process

are created using the tidyverse package. The data.table,

that is used for data reading in the digital system, is

known for its efficiency and speed in processing large

data sets, it is fast in process of filtering, grouping,

modifying data, there are several reasons why

data.table is fast, but one of the main ones is that, unlike

many other tools, it allows you to modify the

information in your table by reference, i.e. it changes

in place rather than requiring the object to be recreated

with the modifications. This means that when using

data.table you need to use the <- operator less often.

Speeding Up the Simulation Animals Diseases Spread: A Study Case on R and Python Performance in PDSA-RS Platform

653

Python at the moment is the first choice of even

the topmost companies in the world such as Amazon,

Facebook, Spotify and Instagram, that have the

challenge to deal with enormous amounts of data, for

their needs, and for their clients’ needs, for data

processing and data analysis (Sebastiaan et al., 2021).

3 DISEASES SPREAD AND

CONTROL SYSTEM

The digital system proposed by the author

(

CARDENAS

et al., 2022). It was designed to

demonstrate the potential for the spread of infectious

animals, considering transmission through animal

movements. The system also implements the

simulation of control actions. These control actions

can, for example, prevent farms from receiving or

shipping infected animals such as culling, isolation of

animals, increased hygiene measures, or vaccination.

As the present work aims at improving the most

computational intensive functions, they will be

detailed in the next subsections. For a comprehensive

understanding of the system, please refer to the

original paper (

CARDENAS

et al., 2022).

3.1 Animal Movement Representation

The system uses Social Network Analysis (SNA)

methods to characterize animal trade patterns, and the

between-farm total of animals moved is represented

in the system as a directed graph, where each farm is

represented as a “node” and the movements among

farms are represented as “edges”. Each edge connects

a specific node origin to a specific node destination,

maintaining also the type and number of animals that

are being moved. The system maintains an event

dataset with data on origin, destination, type, and

number of animals, used to construct the movement

graph. The between-farm movements among farms of

different species, therefore considering a real multi-

host contact network of movement data collected

from (

CARDENAS

et al., 2022).

3.2 Disease Spread Dynamics

The system applies a stochastic simulation algorithm

(SSA) to simulate the disease spreading, as well as,

vital dynamics (birth and deaths) of animals inside

each farm. The system incorporates within farm and

between farm dynamics through a susceptible

infectious model using the temporal animal

movement data explicitly with a higher effective

contact rate to ensure an efficient disease

transmission over the simulations.

The within-farm dynamics is represented by a

state machine composed of four states. Transition

from susceptible (S), to exposed (E), to infected (I),

to recovered (R). Every state is represented as

compartments for each farm, at a rate proportional to

a frequency dependent transmission parameter (𝛽).

3.3 Control Action Zones

After an initial silent spread where animals on some

farms became infected, control and containment of

the disease agent is essential for eradication and

recovery. Quarantine and movement control are

examples of actions to protect animal health, helping

to prevent the disease from being transmitted to non-

infected populations (James, 2007). These actions are

applied to specified control zones, which are defined

through specific user parameters.

This function defines control action zones in

geographic locations, classified or designated

according to specific disease or disease-free status

criteria. These designations help to associate specific

response activities with specific locations.

Figure 1: Control zone representation.

Figure 1 shows the control action zones defined as

outbreaks for farms containing infected animals, the

infected zone containing farms within a 3 km area

around the outbreak, the buffer comprising farms in a

region considering 7 km, and the surveillance zone

was defined as 15 km from the outbreak.

3.4 Vaccination

This function simulates the animal vaccination

process. The process occurs during the disease spread

where vaccinated animals are moved from SEIR

compartments to a V (vaccinated) compartment,

according to the vaccination efficacy and specific rate

conversion day by day, specified by the user when

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defining the control actions. This implies changing

values in the dataset row by row, which can be a

problem in R when the model grows in size.

4 EVALUATION AND

DISCUSSION

This section reports the experiments carried out,

showing the comparison time between the different

tested approaches and discussing the results obtained.

It is divided in two subsections, where the first shows

functions implemented in the languages libraries and

the function call itself while the second presents the

execution time of complex functions developed to

simulate different aspects of the disease spreading.

All results presented through this section were

measured in seconds.

The tests were performed on a computer with the

following specifications. CPU Intel(R) Core(TM) i7-

8650U, 8 cores, 1.90GHz, 16Gb RAM, Linux Debian

5.10.162-1 (2023-01-21) x86_64 operating system.

There are some functions that were developed in the

digital system. One is to create a simulation about

movement of animals from one farm to another farm,

that is a root of some disease spread process, this

process occurs during 15 days. The data reading of

events is using data.table, we will compare with

Pandas and Polars in Python. Using two csv files, one

with 65Mb and other with 225MB, the reading

process will test 100 times and we will use the mean.

4.1 Reading Datasets and Function

Calling

The first test was performed for reading two datasets

with sizes 65MB and 225MB. For Python, we tested

two different libraries (Pandas and Polars), while

Tidyverse and data.table were used to read the same

datasets in R. The test was repeated 100 times and the

average execution times are shown in Table 1.

Figure 2 and Figure 3 show the distribution,

through boxplots, of the time for reading the datasets.

We can see some outliers in the boxplot distribution,

but in this case, we will consider them as special

cases, and they do not change our test results.

Table 1: Mean time for reading process.

Dataset Pandas Polars Tidyverse Data.table

65MB 0.526396 0.257375 1.093194 0.20933

225MB 4.377422 0.480025 15.98519 2.59795

Figure 2: Boxplot graph distribution times for reading for

65 MB dataset.

Figure 3: Box plot graph distribution times for reading the

225 MB dataset.

It is possible to observe that in the process of reading

the small dataset, the Polars library in Python and the

Data.table in R have equivalent reading speeds, with

a slight advantage for Data.table, plus increasing the

size of the dataset to Polars stands out and becomes

the fastest in the reading process, in our case is good,

because the system can grow with more data and

bigger datasets, Polars is around 5 times faster than

Data.table, mainly due to its construction in Rust,

which is a low-level language that in turn is more

efficient, and in Polars it is possible to work in

parallel and scale the system and maintain the

performance, if the digital system grow (Ruizhu et

al.,2018).

For the R language, data.table proves to be very

efficient due primarily to being built in C, the

data.table package is more performant than the

Tidyverse package, which includes the Dplyr

package, the data.table in several operations does not

copy it, filters and selects in the object itself, which

saves memory and time in the process and can

improve the performance of the model, but in some

situations it is necessary and practical using the

tidyverse package (Matt & Joshua, 2016). As the

datasets are passed as arguments to the functions, we

compare the time just for calling the previous

function in both languages. On average, the time in

Python was 0.000001533 seconds, while calling a

function in R took 0.2811756256 seconds. In terms of

Speeding Up the Simulation Animals Diseases Spread: A Study Case on R and Python Performance in PDSA-RS Platform

655

function calls, Python code is converted to the

machine code before being executed, while R code is

interpreted line by line, thus making it more

performative in this process. When the function

grows with more parameters, the difference in

performance in both languages also grows (Yi Lin et

al., 2016).

4.2 Spreading and Control Functions

for the Digital System

This section compares the digital system's most

computationally intensive functions model

implemented in R and Python. This study uses the

three most complex computational performance

functions to improve the performance.

4.2.1 Animal Movements

The first function simulates the animal movement

across the farms. It uses an event dataset containing

farm-to-farm animal movements and number of

animals being moved. We defined 15 days for moving

animals from the dataset of 65Mb, which contains

337.600 farms. This simulation creates the disease

spread situation in the moving process.

Figure 4: Distribution for function simulating animal's

movements for 15 days.

As we can see in Figure 4, Python is around eight

times faster than R for this function. This can be

explained as the Python version using an index on the

farm’s IDs, speeding up the search procedure for

modifying the dataset's number of animals.

4.2.2 Disease Spread Dynamics

The function simulates the spatial disease spread

driven by the geographic distance between farms. It

uses statistical methods based on the initial

parameters to update the number of infected animals

near infected farms. In this simulation, there were 52

infected farms and during the execution, 48.530 farms

had their status modified from this function. The

execution times for this update process in both

languages are presented in Figure 5.

Figure 5: Graph distribution for the local transmission

dynamics.

4.3 Control Zones

In this section we will study and improve the

performance and time execution from the functions

that simulate the vaccination process and control zone

simulation, that is control and isolate farms that

the disease is already vaccinated or isolated in the

process to control the infection, this function receives

data from the vaccination function and inserts and

updates the data in the data frame. The results are

presented in Figure 6.

Figure 6: Graph distribution for function simulating the

update and data in the function that controls the infected

zone.

4.3.1 Vaccination

The next function simulates the animal vaccination

process. The results shown in Figure 7 represent a

vaccination occurring between 6 and 14 days after the

disease spreads.

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Figure 7: Graph distribution for function simulating the

vaccination process from day six until day 14.

In the system, when the animal is vaccinated, the

dataset is updated row by row, updating the

vaccination status of each animal. This explains why

using Python language is four times faster than R in

this application. Finally, Table 2 summarizes the

function's average results previously seen in this

section.

Table 2: Average execution time of computationally

intensive functions.

Move

Function

Animal

Vaccination

Control

Infected

Farms

Spread

disease

Python 10.12597 14.34065

.3209121

3.046755

R 83.05695 60.686126

.4253064

4.128237

In terms of memory usage, the process of the

digital system using the R language is 460 MB, and

the Python language with the same application has

315 MB of memory usage. Python has a feature called

function memorization, where you can cache the

results of a function for specific inputs, improving

performance for expensive computations, which

helps to control memory usage (Xing Cai et al.,

2005). As an overview, the Python implementation

exhibited remarkable performance and significantly

faster processing speeds, presenting a promising

avenue for enhancing the overall system, aiming to

achieve responsiveness and reducing the execution

time.

5 CONCLUSIONS

Based on the tests carried out, it is possible to verify

that in a model with many functions and a lot of data

processing, data reading, and update in dataset, the

optimized solution would be Python, because of the

advantage in functions (time execution and speed

process) and the use of Polars for data analysis. The

implementation in Python proved to be more efficient

than in R, mainly due to its constructive

characteristics and objectives, with some great

advantages in performance, speed process and

memory usage. In terms of reading speed of large

datasets, Polars is more efficient in reading than

Pandas using the Python language. Polars is built in

Rust which guarantees more speed than Pandas, the

great advantage of Polars is that it works in parallel,

so even with an increase in data volume it maintains

its performance, in addition Polars is considered a

library that has Lazy operation, that is, only perform

the function, when necessary, that is, it saves

memory, Polars can work in parallel, which can

improve speed process. Even though Python is slower

in runtime and has some design restrictions as

compared to compiled languages like C or C++.

Python is preferred by scientists and developers in the

field of data analytics, numerical computations and

almost all technical domains, like AI and Deep

Learning. The best approach for work in the model,

according with the test, is to rewrite in Python, using

Polars in data reading (files .csv) and data

manipulation files, mainly because some digital

system grow and the question about memory usage in

Python, in R all objects are stored in memory and

when the system grows, we can have a problem.

Polars, have the lay attribute to save memory and if

necessary, in long term work in parallel, which can be

an advantage in this model, because it’s possible to to

speed up the simulation work with more simulation in

parallel.

For future work it could be important to try to

implement Rust, as Rust is compiled directly to the

machine code, has high performance, works in

parallel and has better memory control management,

which is for some authors the next step in data

analysis (Bugden & Alahmar, 2022).

ACKNOWLEDGEMENTS

This research is supported by FUNDESA, project

“Application of Machine Learning Techniques to

Predict the Prevalence of Diseases in the Processes

of Certified Swine Breeding Fars and Monthly Pige

Epidemiological Sheet” (UFSM/057438). The

research by Vinícius Maran is partially supported by

CNPq grant 306356/2020-1 (DT-2).

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