Design of a Simulation Model for the Diagnosis of Classical Swine Fever
Virus in Ecuadorian Farms
CRISTIAN INCA1, CARLOS VELASCO2,a, ANGEL MENA1, FRANKLIN CORONEL1,
EVELYN INCA2,b, JOSÉ TINAJERO1
1Faculty Department of Computer Science and Electronics,
Polytechnic Higher School of Chimborazo (ESPOCH),
Km 1 1/2 Panamericana Sur, Riobamba, EC060155,
ECUADOR
2Independent Researcher,
ECUADOR
aORCiD: https://orcid.org/0009-0003-9012-9085
bORCiD: https://orcid.org/0000-0001-7055-9019
Abstract: - Classical swine fever (CSF) is a disease that slows down animal production and international trade;
therefore, its identification is key in pig farms to take the relevant health measures. Therefore, the objective of
this research was to design a Susceptible-Exposed-Infected-Recovered (SEIR) simulation model to carry out
epidemiological modeling for the identification of outbreaks of classical swine fever in the Sierra Region of
Ecuador, using Python software and historical data on incidences of this disease in the provinces of the
Ecuadorian highlands, considering the variables pig population, initial number of exposed pigs, initial number
of infected, number of pigs removed, contagion rate (α), transmission rate (β), and recovery rate (γ). The results
show that the SEIR model allowed us to determine that the population of susceptible (healthy) pigs decreases
over time until reaching zero. This decrease in susceptibility occurred during the first 15 days, which shows
that this is the time necessary to infect the entire population with an infected person. Therefore, the exposed
population increases during the 15 days that the total infection process lasts and then decreases. It is also
identified that throughout these five years of analysis of the past, it has been increasing from 2015 to 2019,
which hurt the yields and productivity of pig farms in the Ecuadorian mountains.
Key-Words: - Epidemiology, infestation, modeling, prevention, health, classical swine fever.
Received: April 11, 2024. Revised: August 16, 2024. Accepted: October 2, 2024. Published: November 5, 2024.
1 Introduction
Pig farming in Ecuador represents an important
economic sector, comprising a mix of small
traditional family units, industrial production, and a
culture of high pig consumption, [1]. However, the
presence of diseases such as classical swine fever
(CSF), which hampers animal production and
international trade [2], poses a significant challenge.
In essence, classical swine fever was first reported
in Ecuador in the 1940s and has since caused
significant losses to the national swine industry due
to its high morbidity and mortality rates, ranging
between 40% and 60%, particularly in the Sierra
and Coast regions, [3]. The causative agent is the
classical swine fever virus, a small enveloped RNA
virus of the genus Pestivirus.
The consequences of a CSF epidemic would be
serious, so it remains crucial to analyze the behavior
of the disease [4] as well as simulate different
scenarios for the control of classical swine fever
outbreaks. The control of classical swine fever
represents an essential context for guaranteeing the
sustainability of swine production systems. Thus,
the implementation of disease management and
control plans is an indispensable part of any
program aimed at its containment. In addition to
being able to understand the progression of the
disease in various conditions as crucial data to
manage effective management, in this context,
simulating the evolution of the disease is
advantageous when controlling swine fever, [5].
This, in turn, facilitates better planning and
contributes to the macroeconomic indicators of any
country, as well as health and disease management.
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Cristian Inca, Carlos Velasco, Angel Mena,
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A very effective technique to study the spread
of infectious diseases arises through the use of
mathematical modeling, which allows
epidemiological factors to be used at the sample
level to predict epidemic dynamics at the population
level. Currently, mathematical models serve as a
fundamental tool for the study of infectious
diseases, identifying how control strategies can
modify the dynamics and epidemiology of these
diseases, [6].
Using modeling as a tool can facilitate the
visualization of the different scenarios that may
arise and provide the most appropriate solutions for
each case and location. [7], have highlighted the
laudable work of simulating the spread of the CSF
virus on farms in the Ecuadorian Sierra, with the
contribution of relevant information to explore
specific surveillance and control strategies for this
disease.
Among the diversity of models, there are those
with a deterministic component, in which it is
explained that inputs will invariably produce the
same results without considering the existence of
chance or the uncertainty principle. In this scenario,
it is closely related to the creation of simulated
environments to study hypothetical situations. As a
result, these models do not take into account the
uncertainty of disease spread, [8]. On the other
hand, a model considered stochastic tends to assume
variable behavior resulting from a phenomenon that
guides random data and where the relationships
between variables are determined by probabilistic
functions. Therefore, these models incorporate the
stochastic component, which turns out to be
computationally expensive and has a greater degree
of difficulty when analyzing the structure and
corresponding findings.
Contributing to the implementation of an
approach to the situational phenomenon that occurs
in the Sierra region of Ecuador is following the
relevance of other similar studies. An example is the
one developed by [9], which reflected the success of
the use of stochastic SEIR models due to their
ability to simulate the evolution of infectious
diseases within a farm. This study was carried out in
2018 and focused on "Assessment of the risk of
livestock epidemics through a mathematical
simulation" coupled with the use of a Be-FAST
model, which represents a computer program built
on a mathematical model of spatiotemporal
stochastic propagation to investigate the
transmission of infectious livestock diseases within
and between farms. The study concludes by
quantitatively describing the possible spread of
diseases that can have serious consequences in the
respective countries.
The reality of the context implies
heteroskedasticity in the way the phenomenon
evolves; in this sense lies the importance of the
present study of expanding and evaluating the
conditions of outbreaks of classical swine flu in the
Sierra region of Ecuador and the usefulness of the
model SEIR to adjust scenario simulations that
allow a situational diagnosis on farms in Ecuador. In
essence, the objective was to design a Susceptible-
Exposed-Infectious-Recovered (SEIR) simulation
model for the spread of the CSF virus in farms in
the Sierra region during the period 2015-2020,
whose findings should contribute to farm owners
farms and improvements in the population that are
economically dependent on pig farming activities,
both directly and indirectly. In addition, it can
benefit consumers since it seeks to ensure the
quality of the meat ready for consumption.
The usefulness of this model lies in its ability to
quickly identify outbreaks of classical swine fever.
As well as being able to analyze farm data, such as
the presence of symptoms in pigs, mortality, and
laboratory test results, to detect possible CSF
outbreaks early. In addition, the designed SEIR
model can simulate different control scenarios for
classical swine fever, such as vaccination, sacrifice
of infected animals, and movement restrictions, to
evaluate effectiveness in reducing the spread of the
aforementioned disease.
2 Materials and Methods
2.1 Type and Design of the Research
The research has an exploratory, descriptive,
prospective, and non-experimental scope. It is
exploratory because it seeks to understand
everything related to CSF and explore historical
databases of disease incidence in the provinces of
the Sierra region, which will feed information to the
SEIR model. The descriptive scope comes into play
through the analytical method used to understand
and investigate the functioning of the SEIR model.
The study is prospective since it predicts the
behavior of the disease based on the
characterization, description, and operation of the
model. Finally, it is non-experimental since it
involves investigating and analyzing previously
obtained data to feed the mathematical model.
2.2 Research Methods
In this research, both deductive and analytical
methods were applied. In the deductive method, the
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research approach involves studying and analyzing
previous work related to the topic, reviewing
existing theories about the phenomenon under
study, and then testing the hypotheses that arise
from those theories. Start with a general statement
or hypothesis and examine the possibilities of
reaching a specific logical conclusion. This method
uses deduction to test hypotheses and theories,
which predict specific outcomes if they are correct.
2.3 Research Approach
This research applies both qualitative and
quantitative approaches, combining them to obtain
results on variables and their effects on a specific
population. In this way, the best methodology for
calculating an SEIR simulation model for the spread
of the CSF virus on farms in the Sierra region is
analyzed and determined.
2.4 Study Population and Sample Selection
For this study, the population of vaccinated pigs
from family farms in the Sierra region was used.
Non-probabilistic convenience sampling was used
because a specific affected population of pigs (from
the Sierra region of Ecuador) was selected for the
study.
2.5 Data Collection Techniques and
Instruments
During the data collection process, the data source
used for the epidemiological modeling of the SEIR
model for CSF outbreaks in the Sierra region of
Ecuador came from the database of the Agrocalidad
Ecuador system. The data includes records of
positive cases corresponding to vaccinated,
slaughtered, and quarantined pigs for CSF in the
Ecuadorian Sierra.
2.6 Data Simulation
The method is based on the construction of a model
consisting of the formulation of systems of
differential equations that show at a graphic level
the behavior and evolution of the epidemiological
phenomenon (the spread of the classical swine flu).
A SEIR model was used, which is an extension of
the SIR (Susceptible, Exposed, Infected, and
Recovered) model. It adds a compartment of the
population that has been in contact with an infected
animal, becomes infected, and enters a period of
latency (where it shows no symptoms) before
becoming infectious (Figure 1). The states
considered for this model are healthy and
susceptible to infection (S), exposed (E), infectious
(I), and recovered from the disease or eliminated
(R). The state transition sequence is as follows:
Fig. 1: Diagram of the SEIR model
The interpretation of the dynamics of
convergence between states or compartments in the
SEIR model is as follows: fraction of the host pig
population that is susceptible to infection (S),
fraction of pig species infected but not yet
transmitting the infection to other animals during
the latency period (E), fraction of infected porcine
species that can transmit the infection (I), and
fraction of recovered porcine species that acquire
temporary or permanent immunity (R).
In terms of this dynamic, the flow in the spread
of the CSF virus is defined using a system of
ordinary differential equations to adjust the
modeling of the phenomenon of interest for a fixed
population (10,000 swine species):
N=S+E+I+R (1)
ds / dt = μ *(NS)- β *((SI))/N- vS (2)
dE / dt = β *((SI))/N-( μ + σ )*E (3)
dI / dt = σ E-( γ + μ )YO (4)
dR / dt = γ I- μ R+vS (5)
Where µ is the mortality rate in the population
(not related to the disease), β is the effective contact
rate a parameter that controls the frequency with
which contact of the susceptible swine species
results in a new exposure, resulting in infected at a
ν, which represents the vaccination rate, if there is a
supply of a vaccine (in this case assumed to be
zero), σ is the rate at which an exposed individual
becomes infectious, and γ is the rate at which that an
infected individual recovers and becomes resistant.
This model considers a priori that it tends to handle
a constant value (10,000 porcine species) for the
population under study as an initial condition, based
on the information obtained from the Con case
records. With this information, parameters such as
the fatality rate are determined, which is low for this
phenomenon in the evaluation of the CSF virus for
the highland region of Ecuador. This indicator of the
fatality rate is considered low due to the control
strategies that have been implemented, compared to
the limitations in the control of other diseases that
occur in other affected animal species within the
Sierra region. In essence, it has been determined that
all infected pig hosts have a high probability of
acquiring immunity.
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2.7 Design of the SEIR Model in Python
To develop the SEIR model, the parameters to be
used and their respective formulas are first
determined. It is necessary to use the infection data
registered in the Ecuadorian Sierra region to
determine the required rates by using the following
formulas:
Tasadeinfecciónβ
ó
Tiempodeobservación 6
TasadeincidenciaσTotalpositivos
Poblacióntotal7
TasaderecuperaciónγTotalencuarentena Totalsacrificados
Poblacióntotal 8
After defining the system of differential
equations and the formulas that determine the
required rates, the Euler algorithm is formulated in
Python to solve the differential equations in the
model. To do this, initial population data, time, and
rates of infection, incidence, and recovery entered
by the user are used. The Python programming
language is used, using Visual Studio Code version
1.72, and the necessary libraries for the
development and compilation of the programming
were downloaded, including in this case the
“matplotlib” library.
Once the code to solve the differential equations
using the Euler method is completed, the graphical
user interface (GUI) of the program is designed for
user interaction to obtain a friendly environment for
handling the present study. The "tkinter" tool was
used to create spaces and buttons for the program
interface.
3 Results
3.1 Analysis of CSF Infection Data to
Determine Its Feasibility of Use in the
Susceptible-Exposed-Infectious-
Recovered (SEIR) Model
Based on data from the Animal Health Surveillance
Office (SIZSE) [10], Table 1 shows the number of
pigs in the Sierra region that tested positive for CSF
infection, the number of those slaughtered, and the
population registered for each anus.
In Table 1, it can be seen that positive cases of
CFP have been increasing from 2015 to 2018.
However, from that year onwards, there has been a
decrease in positive cases until 2020 (Figure 2).
Which coincides with a lower pork production rate
as a result of the COVID-19 pandemic.
Table 1. Historical Data on the Spread of Classical
Swine Fever in the Sierra Region, Ecuador. Period
2015-2020
Years Positives sacrificed Population
2015 79 18.54% 48 11.27% 426 100.00%
2016 76 15.87% 180 37.58% 479 100.00%
2017 30 25.64% 8 6.84% 117 100.00%
2018 54 21.43% 17 6.75% 252 100.00%
2019 28 13.33% 7 3.33% 210 100.00%
2020 21 23.86% 6 6.82% 88 100.00%
Fig. 2: Positive cases of classical swine fever in pig
farms in the Ecuadorian Sierra. Period 2015-2020
Classical swine fever cases more than doubled
between 2017 and 2019 compared to previous years
(Figure 1). Furthermore, the data show that the
percentage of pigs slaughtered in 2016 increased
significantly (Figure 3). This could be due to poor
disease control during that period. Starting in 2017,
the percentage decreased, and there was a slight
increase in cases in 2018. Finally, a decrease in
sacrifices is evident until 2020.
Fig. 3: Cases of pigs slaughtered due to classical
swine fever in pig farms in the Ecuadorian Sierra.
Period 2015-2020
The number of pigs in quarantine increased
from 2015 to 2019, indicating better control of the
disease (Figure 4). However, in 2020, the number of
pigs in quarantine decreased, in parallel with a
decrease in yields, due to the health restrictions
imposed by COVID-19, which affected pig
production.
1,59% 1,60%
4,35% 4,70% 4,20%
2,72%
0,00%
1,00%
2,00%
3,00%
4,00%
5,00%
2015 2016 2017 2018 2019 2020
0,97%
3,79%
1,16% 1,48% 1,05% 0,78%
0,00%
1,00%
2,00%
3,00%
4,00%
2015 2016 2017 2018 2019 2020
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Fig. 4: Cases of pigs in quarantine due to classical
swine fever in pig farms in the Ecuadorian Sierra.
Period 2015-2020
3.2 Estimation of Population Parameters of
the Susceptible-Exposed-Infectious-
Recovered Epidemiological Model
Table 2 shows the dynamics of growth and decline
in transmission rates throughout the study period. It
indicates that in 2019, the transmission rate was the
lowest compared to other years, while in 2018, the
highest transmission rate was observed.
Table 2. Annual transmission rate of classical swine
fever (CSF) in pig farms in the Ecuadorian Sierra.
Period 2015–2020
Year Transmission Speed Population
2015 0.569 426
2016 0.454 479
2017 0.572 117
2018 0.613 252
2019 0.383 210
2020 0.581 88
Table 3. Maximum peak for developing classical
swine fever (CSF) infection in pig farms in the
Ecuadorian Sierra. Period 2015-2023
Year Maximum Peak Day
2015 fifty
2016 59
2017 41
2018 44
2019 59
2020 40
2021 41
2022 48
2023 fifty
Maximum value 59
Minimum value 40
Average 48
During the period 2015–2023, it was shown that
in pig farms in the Ecuadorian highlands, the CSF
virus takes a minimum of 40 days to reach its peak
of contagion, an average of 48 days, and a
maximum of 59 days (Table 3). Therefore, when an
initial sign of CSF infection is determined in a pig,
there is a period of less than 40 days to implement
infection containment strategies in other pigs.
3.3 Estimation of the Areas Most Affected
by the Classical Swine Fever Virus in the
Sierra Region during the Period 2015-
2023
The analysis of data disaggregated by canton
determines that there is a peak in spikes in CFP
contagion in Quito (43%) and Zapatillo (21%) of the
Sierra in the Republic of Ecuador. In addition, it is
necessary to note that the trend in the prevalence of
positive cases of CPP decreased during the coverage
period of 2020-2022, a prominent situation resulting
from quarantine restrictions and a reduction in
mobilization dictated by public health policies to
mitigate the spread of SARS-CoV-2. The cantons of
Quito, Chillanes, Guaranda, and Loja, in the
Ecuadorian Sierra region, recorded the highest
numbers of CSF cases (Figure 5).
Fig. 5: Trend in annual prevalence of CSF in pig
farms in the Ecuadorian Sierra
In 2016, the cantons of Quito and Guaranda
reported the highest number of CSF cases,
comprising 32% and 17%, respectively. In 2017, the
cantons of Quito and Pelileo recorded the highest
number of CSF cases, representing 23% and 20%,
respectively. When describing the behavior in 2018,
the cantons of Cumandá and Guaranda registered
the highest number of CSF cases with percentages
of 30% and 20%, respectively. In 2019, the cantons
of Quito and Zapotillo had the highest number of
CSF cases, representing 43% and 21%, respectively.
Finally, in 2020, the cantons of Chillanes and
Sevilla de Oro registered higher cases of CSF, with
percentages of 33% and 19%, respectively. In 2021,
the behavior will be reduced to 13 only in the Loja
canton. However, as shown in Table 4, for the year
2022, there is only a 9% prevalence in the
Echeandia canton and other new records of CFP
cases for the Sierra region of Ecuador in the year
2023 within the Cañar cantons (20%), Troncal
(23%), and Caluma (7%).
8,57% 10,08%
16,98% 21,95%
31,48%
11,38%
0,00%
10,00%
20,00%
30,00%
40,00%
2015 2016 2017 2018 2019 2020
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These findings express that the phenomenon
that governs the presence of PCC cases represents a
fortuitous behavior in the distribution in each of the
cantons located in the Sierra region of the Republic
of Ecuador. This demonstrates significant variability
in the cantons with the highest number of positive
CSF cases, ranging between a minimum of 11% and
a maximum of 43%. The canton of Quito appeared
in most years, leading the statistics. This fact could
be related to greater economic activity with
geographic interaction that defines the location of a
greater number of pig farms in that region, which
increases the risk of transmission. Greater economic
activity and the greater number of pig farms may
contribute to more interactions between pigs in a
susceptible state and those moving to an infected
state, leading to a higher rate of virus transmission
in that area.
Table 4. Percentage prevalence of classical swine
fever (CSF) by cantons
Canton Annual prevalence percentage
2015 2016 2017 2018 2019 2020 2021 2022 2023
Quito 15% 32% 23% 43%
Loja 13%
13%
Lacatunga 11%
Guaranda 17% 20%
Pelillo
20%
Cumanda
30%
Slipper
21%
Chillanes
33%
Golden
Seville
19%
Echeandia
9%
Cañar 20%
The
Trunk
23%
Caluma 7%
4 Discussion
The results found in this study agree with the data
reported in previous studies that show a prevalence
of the CSF virus in Ecuador in different
demographic regions. For example, in 2010, the
prevalence was 0.169%; in 2012, it was 0.14%; in
2013, it was 0.18%; in 2014, it was 0.078%; and
finally, in 2015, it was 0.89%. These findings align
with what was reported in 2020 by the World
Organization for Animal Health (WOAH),
establishing that this disease is present in four
countries: Colombia, Brazil, Ecuador, and Peru. The
presence of classical swine fever in several
countries makes it difficult to achieve its
eradication, [11].
Following this context, the study by [12] is
cited, which, under a descriptive research
methodology based on the application of structured
surveys, showed, as a result of a prevalence of 7%
of the CSF virus during the years 2014 to 2021, a
significant reduction in pork production in the
Carlos Julio Arosemena Tola canton, Napo
province, Ecuador. In contrast, the reality situation
in the Ecuadorian Sierra region, which constitutes
the approach to the problem in its 12 provinces, is
compared with the statistics of recent years, which
indicate a moderate prevalence of the CSF virus in
its different farms within the region. Sierra region.
In general terms, significant findings from the
study are presented that allow us to assume an
increase in positive cases of CSF until 2018,
followed by a decrease until 2020 due to the
increase in the number of pigs in quarantine
expected until 2019, coinciding with a decrease in
positive cases in 2020. Therefore, the lowest rate of
spread of the CSF virus in swine animals occurs due
to the conditions of restrictions on movement and
quarantine resulting from the COVID-19 pandemic.
This refers to the effectiveness in the use of control
and quarantine measures. In comparison with the
presentation of variable behavior in the annual PPC
transmission rates, presenting the lowest rate in
2019 and the highest in 2018, this reality allows us
to raise questions about the factors that influence the
fluctuation of the spread of the disease in swine
animals. The parameters estimated in the SEIR
model indicate that the estimated time of at least 40
days for the CSF virus to reach its peak of contagion
is crucial for the timely implementation of
containment strategies. Furthermore, the results lead
to establishing an average of 48 days and a
maximum of 59 days for the active evolution of the
disease in swine animals.
Between 2017 and 2019, there was a doubling
of cases of classical swine fever due to the measures
implemented in 2016 related to a high percentage of
pigs slaughtered. Cantons most affected by the
Classical Swine Fever virus are Quito and Zapatillo,
with spikes in contagion in other cantons over the
years. This phenomenon is attributed to the greater
economic activity and number of pig farms found in
Quito; the above is a factor related to the prevalence
of CSF cases in that region. It would be appropriate
to explain in future research the economic and
geographic impact of the disease and the possible
reasons behind these disparities. These findings
show the importance of control and surveillance of
classical swine fever in the Sierra region of Ecuador
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to mitigate the spread of the virus and protect swine
production.
The contributions of this study on classical
swine fever in the Sierra region of Ecuador during
the period 2015-2023 compared to previous works
present in the literature are mainly related to the
management of updated and specific data on the
incidence of classical swine fever PPC in the Sierra
region of Ecuador for an extended period. Which
tends to enrich the existing literature with relevant
and recent information. In addition, obtaining
detailed analysis of trends in positive cases,
transmission rates, and impacts on control measures
implemented in a specific context represented by the
Sierra region of Ecuador. The construction of the
SEIR model, it was focused on the estimation of
population parameters in a specific territorial
context to enrich the understanding of the dynamics
of the disease and cognitive competencies at the
level of epidemiological and veterinary literature.
The generalizability of the findings of this study
on classical swine fever in the Sierra region of
Ecuador to other contexts may vary depending on
several factors. In this sense, each territorial context
is conditioned at an epidemiological, geographical,
and production management level. Therefore, this
implies controlling local variables and variables on
the virulence of the virus strain, the immune
response of the animals, and the effectiveness of the
control measures implemented. Ultimately, each
model must be built under the criteria inherent to the
phenomenon under study in the territorial space that
defines it. However, the findings preserve the
principle of comparability granted by the
methodology in the construction of SEIR models.
The increasing prevalence of classical swine
fever in the Sierra region of Ecuador can be
attributed to several factors, including the
movement of infected pigs, the implementation of
inadequate biosecurity measures on pig farms, and a
lack of effective control and surveillance measures.
This high prevalence translates into the transfer of
pig animals that are in a state of infection with the
disease within the territory of the Sierra region,
added to the uncertainty caused by the limitations in
the application of detection tests, which causes a
greater level of risk in the event of possible
contagion. In addition, it is annexed that it is
unlikely to implement strategies for management
and control in biosafety and quarantine measures to
correct and contain the spread of the virus in the pig
population that lives on the different farms in the
Sierra region of Ecuador.
To mitigate the implications inherent in the
spread of classical swine flu, it is essential to
implement comprehensive control and prevention
strategies, including strict biosecurity and
quarantine measures at the territorial level within
pig farms. Another key factor is detecting the
evolution of the virus within the litter of animals in
advance and achieving notification of positive cases
to official entities. At the same time, achieving the
promotion of vaccination programs with
collaboration between government authorities,
veterinarians, and pig breeders will effectively
reduce the impact of this disease on the pork
industry in the Ecuadorian Sierra.
The management and control of classical swine
flu as an endemic disease represents a fundamental
approach due to the considerable impact it causes on
the economy of Ecuador, and even more so when
the number of positive cases increases within pig
farms in the Sierra region. The consequences and
impacts of this disease are focused on the increase
in the rate of morbidity and deaths in pigs. In this
order, the occurrence of strong economic losses is
emphasized both for large pig producers and for
those where breeding is family or micro-enterprise,
with absolute predominance in backyard and family
pig breeding, [4]. Consequently, this impacts the
consumption and export of pork resources, [13].
In response to this challenge, to control and
eradicate this highly contagious viral disease that
affects both domestic and wild pigs, Ecuador has
implemented a classical swine flu control and
eradication project since 2012. However, the current
situation describes the non-existence of any
experimental design that is decisive in controlling or
reducing the spread of the disease. It has been a
specific strategy to develop vaccination processes as
a preventive mechanism since 2016 for pigs over 45
days old, [14].
Despite these efforts, there is a considerable
prevalence and transmission rate of classical swine
fever in the territory of the Sierra region of Ecuador.
This scenario assumes it is vital to generate constant
surveillance mechanisms, implement detection days,
and evaluate measures to generate immediate
responses to situations of exponential spread and
contain the different infection states of classic swine
flu. The formulation stage of projects involved in
the control and mitigation of classical swine fever in
the Sierra region must require a comprehensive and
multidisciplinary approach to include the best
practices of biosecurity, quarantine, and research on
the evolution of the phenomenon. Indeed, a link
between government authorities, veterinarians, and
pig farmers must be assumed to propose public
policies conducive to protecting the pork industry,
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DOI: 10.37394/23208.2024.21.35
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and public health, and consolidating the capacity to
market pork products in international markets.
It has been evident that within the vaccination
process and implementation of sanitary measures as
a control mechanism for classical swine flu in the
Sierra region of Ecuador, the results indicated that
for the year 2019, the notification of 523 cases of
classical swine fever was achieved in 13 provinces
of Ecuador. In this context, the empirical evidence
provided by laboratory tests validated the presence
of 100 positive cases of classic swine flu, and the
remaining 423 cases showed negative results. At the
territorial level, it was the provinces of Morona
Santiago and Los Ríos that had the highest number
of confirmed cases, followed by the provinces of
Pichincha and Zamora Chinchipe. Regarding
vaccination, 1,649 establishments (95%) applied
some type of vaccine, while the remaining 5% did
not apply any. These vaccination data related to
classical swine fever are important for consideration
in the control and eradication programs of this
disease. Countries that have successfully eradicated
classical swine fever have often relied on mass
vaccination programs, [15].
Despite the positive cases observed in the
Ecuadorian Sierra, the results showed that 85% of
the 5,000 simulations resulted in the non-spreading
of classical swine fever (i.e., 85% of intentionally
infected farms did not further spread the disease).
Specifically, only 73 of the backyard farm
simulations affected other farms. The spread of the
disease was mainly local, with an average number
of infected districts equal to 4 and an average
distance from the source of infection to the infected
farm of 4.5 km. In such scenarios, vaccination could
be considered a strategy. Regions where the disease
remains endemic have used live attenuated vaccines
to limit the effects of the disease or as a first step in
a comprehensive program for the control and
eradication of the virus, [16]. The implementation of
targeted vaccination programs in areas with a higher
prevalence of classical swine fever could effectively
reduce the transmission and impact of the disease.
As mentioned above, the most critical factor for
the spread of the epidemic between farms is local
infection that occurs in nearby farms. The study also
describes control measures that, as demonstrated in
this work, effectively stop the spread of the disease.
The study concludes that carrying out simulations is
of great value for making decisions that improve
prevention and control programs since they help
determine the most appropriate course of action
according to the specific scenario.
The choice of models depends on the
transmission characteristics of the disease. In this
case, the SEIR model aligns well with the
epidemiology of the disease in the Ecuadorian
Sierra. Stochastic models, such as the Be-Fast
model, are used to simulate the spread of diseases
between farms, [9]. However, these models may not
be suitable for evaluating the behavior of large
populations in response to suggested state changes,
where deterministic models based on dynamical
systems are more appropriate. These deterministic
models evaluate changes in the states of a
population and are suitable for evaluating viral
diseases, such as those used during the COVID-19
pandemic, [17], [18]. The use of adequate modeling
is essential to explain, understand, and manage the
processes that establish the dynamics for the spread
of diseases in swine animals, as well as to apply the
appropriate control strategies in the specific
territorial context in which they occur.
5 Conclusions
The findings in the present study have been decisive
in affirming that the Sierra region of the Republic of
Ecuador has shown significant variability in cases of
classic swine flu in the last eight years. Quito is the
canton that leads the statistics due to the increase in
economic activity and the proximity of pig farms,
which increase the risk of transmission of the virus.
However, during the year 2020, a fortuitous event
occurred that reduced the amount of contagion,
which translates into travel limitations due to the
danger of contagion of COVID-19 in the
Ecuadorian population.
By considering the data collection process and
constant monitoring of the phenomenon associated
with classic swine flu to be relevant to form reliable
data and information necessary to define a decision-
making process in line with the existing reality, This
line of recommendation has been supported by the
events that have arisen in the health emergencies of
the human race worldwide, where the process of
control and management of timely and reliable
information on the COVID-19 pandemic managed
to successfully contain the spread of the disease.
In addition, it is necessary to consolidate a
control process with the establishment of specific
measures to guarantee accurate monitoring and
records of the cases raised for this type of disease in
pigs to formulate appropriate control strategies for
the protection of the health of both human beings
and animals. In this context, the findings are
relevant to channel decision-making processes
within disease prevention and management
programs in the Republic of Ecuador, consolidating
adjusting strategies at the official level to the
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Cristian Inca, Carlos Velasco, Angel Mena,
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circumstances and challenges of the phenomenon of
interest. This will translate into greater capacity
within the Ecuadorian swine industry to resist and
mitigate the impacts of classical swine fever based
on continuous monitoring and adaptation of public
policies.
In conclusion, the compilation of data recorded
on cases of classical swine flu must allow the
parameters of the SEIR epidemiological model to be
adequately estimated and defined in terms of the
rate of infection, transmission, and recovery of pigs.
This estimation of parameters has considered the
animals in a state of quarantine, those that have been
sacrificed, and includes the interrelation between the
specific population of pigs and the level of infection
within the territory that makes up the mountains of
Ecuador. For this model, the values of α = 0.01, β =
0.003, and γ = 0.01 were used.
The SEIR model shows how diseases spread
over time among the pig population. It is
demonstrated that the number of susceptible
(healthy) pigs will eventually drop to zero. This
decline in susceptibility happens within the first
fifteen days, which is the amount of time needed for
the illness to propagate and infect every susceptible
individual, beginning with one diseased pig. The
number of exposed pigs grows throughout this time
as more of them contract the virus, and it
progressively declines as the infection worsens or
the animals recover.
The number of infected pigs grows somewhat
over time, suggesting that the population is still
being spread. As some affected people finally
recover from the illness and develop immunity, the
number of recovered pigs is also rising. The swine
population's infection and recovery patterns, as well
as the dynamics of illness, are all well-represented
by this SEIR model.
To monitor the process with a minimum level of
risk that leads to effectively restricting the spread of
the disease, it is vital to implement control
techniques and methods to contain the infection
dynamics that occur in the swine population of the
Sierra region in Ecuador. To do this, it is necessary
to manage the knowledge of the inherent
characteristics of the aforementioned population
through behavior analysis, leading to the prediction
of future events assuming the implementation of
focused preventive strategies and adequate control
aimed at reducing or mitigating the impact of the
disease in the industry swine in the Sierra region.
Being vaccination program-specific, one of the
improvement strategies consists of the use of
biosafety procedures with the early identification
and isolation of sick animals to stop the spread of
the implications for classical swine virus infection.
In definitive terms, SEIR modeling is the basic tool
for decision-making and improving disease
prevention methods in the context of classical swine
flu on farms in the Sierra region of Ecuador.
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final findings and solution.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of interests
The authors have no conflicts of interest to declare.
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