=Paper=
{{Paper
|id=Vol-3777/short7
|storemode=property
|title=Simulation of Influenza Dynamics in Kharkiv Oblast (Ukraine) using ARIMA Model
|pdfUrl=https://ceur-ws.org/Vol-3777/short7.pdf
|volume=Vol-3777
|authors=Mykola Butkevych,Dmytro Chumachenko
|dblpUrl=https://dblp.org/rec/conf/profitai/ButkevychC24
}}
==Simulation of Influenza Dynamics in Kharkiv Oblast (Ukraine) using ARIMA Model==
https://ceur-ws.org/Vol-3777/short7.pdf
Simulation of Influenza Dynamics in Kharkiv Oblast
(Ukraine) using ARIMA Model
Mykola Butkevych and Dmytro Chumachenko
National Aerospace University “Kharkiv Aviation Institute”, Vadym Manko str., 17, Kharkiv, 61070, Ukraine
Abstract
Influenza remains a significant public health concern globally, with its seasonal outbreaks leading to high
morbidity and mortality. This is especially relevant in conflict-affected regions like Kharkiv Oblast, Ukraine,
where the ongoing Russian war has severely disrupted the healthcare system. This study aimed to develop
an ARIMA-based model to simulate and forecast influenza incidence in Kharkiv Oblast, aiding public health
decision-making in a challenging environment. The study utilized monthly influenza incidence data from
January 2013 to April 2024, sourced from the Kharkiv Oblast Centre for Disease Control and Prevention.
The data were normalized using Min-Max scaling, and 90% of the dataset was used for training, with the
remaining 10% reserved for testing. The ARIMA model was selected for its ability to handle non-stationary
data and suitability for time-series forecasting. Model performance was evaluated using Mean Absolute
Percentage Error (MAPE) and Mean Absolute Error (MAE). The results showed that the ARIMA (10,1,11)
model achieved a MAPE of 9.5879% and an MAE of 37.5099, indicating strong predictive accuracy. This
suggests that ARIMA modelling can effectively forecast influenza trends, even in regions with strained
healthcare systems and unreliable real-time data collection. This study demonstrates ARIMA models'
practical and scientific potential for disease forecasting in conflict-affected regions. These findings
contribute to using data-driven models to enhance public health interventions in unstable environments.
Keywords
Epidemic model, machine learning, influenza, Ukraine, infectious disease simulation 1
1. Introduction
Influenza, commonly known as the flu, is a highly contagious respiratory illness. These viruses
primarily infect the upper and lower respiratory tracts, leading to symptoms such as sudden onset
of high fever, cough, sore throat, muscle and joint pain, headache, and severe fatigue [1]. Influenza
viruses are categorized into three main types affecting humans: A, B, and C. Types A and B are
responsible for the seasonal epidemics observed annually [2]. In contrast, type C infections cause
mild respiratory illnesses and are not associated with epidemics. Influenza viruses' rapid mutation
rate is a defining characteristic, particularly through mechanisms like antigenic drift and antigenic
shift [3]. Antigenic drift involves small genetic changes over time, leading to new virus strains that
the immune system may not recognize. Antigenic shift, which occurs only in type A viruses, involves
abrupt, major changes resulting in new hemagglutinin or neuraminidase proteins, potentially
leading to pandemics due to a lack of population immunity.
Globally, influenza represents a significant public health concern, contributing to yearly
morbidity and mortality [4]. The World Health Organization estimates that annual influenza
epidemics result in about 1 billion infections worldwide, including 3 to 5 million cases of severe
illness and 290,000 to 650,000 respiratory deaths [5]. The impact of influenza extends beyond health,
imposing considerable economic burdens due to increased healthcare costs, hospitalizations, and loss
of productivity from work and school absenteeism [6]. Influenza pandemics, such as the 2009 H1N1
pandemic, have demonstrated the virus's potential for widespread devastation, emphasizing vigilant
surveillance and prompt response strategies. Vaccination remains the most effective preventive
measure against influenza [7]. Yet, the virus's antigenic variability can compromise vaccine efficacy,
ProfIT AI 2024: 4th International Workshop of IT-professionals on Artificial Intelligence (ProfIT AI 2024), September 25–27,
2024, Cambridge, MA, USA
m.v.butkevych@khai.edu (M. Butkevych); dichumachenko@gmail.com (D. Chumachenko)
0000-0001-8189-631X (M. Butkevych); 0000-0003-2623-3294 (D. Chumachenko)
© 2024 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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and global vaccination coverage often falls short of targets due to factors like vaccine hesitancy and
accessibility issues.
Influenza continues to pose a critical health challenge in Ukraine, particularly in regions such as
Kharkiv Oblast [8]. The full-scale Russian invasion that began in 2022 has exacerbated existing public
health issues by severely disrupting healthcare infrastructure and disease surveillance systems [9].
The war has led to significant population displacement, with many individuals residing in
overcrowded shelters or temporary accommodations lacking adequate sanitation and healthcare
services. These conditions facilitate the transmission of infectious diseases, including influenza, by
increasing close contact among individuals and reducing access to preventive measures like
vaccination and antiviral medications [10]. Additionally, the strain on healthcare resources hinders
effective diagnosis, treatment, and reporting of influenza cases, complicating efforts to monitor and
control outbreaks. Understanding influenza dynamics in this context is crucial for developing
targeted interventions to protect vulnerable populations and prevent further strain on the already
challenged healthcare system.
Data-driven healthcare approaches have become increasingly vital in controlling and managing
epidemic processes, especially when traditional surveillance methods are compromised [11].
Statistical models, such as the Autoregressive Integrated Moving Average (ARIMA) model, offer
powerful tools for analyzing time-series data to understand past trends and forecast future disease
incidence [12]. By leveraging historical influenza data, ARIMA models can provide accurate short-
term predictions of influenza activity, enabling health authorities to allocate resources efficiently,
plan vaccination campaigns, and implement timely public health interventions [13]. In conflict-
affected regions like Kharkiv Oblast, where data collection may be fragmented, predictive modelling
can compensate for gaps in surveillance by identifying patterns and potential outbreaks based on
available information. Incorporating data-driven methodologies into public health strategies
enhances the capacity to respond proactively to influenza epidemics, ultimately reducing the disease
burden and improving health outcomes for the affected populations.
The research aims to develop an ARIMA-based forecasting model of influenza in Kharkiv oblast
of Ukraine.
Current research is a part of the comprehensive information system for assessing the impact of
emergencies on the spread of infectious diseases described in [14].
2. Current research analysis
The application of the ARIMA model in simulating and forecasting influenza dynamics has gained
significant attention in epidemiological research. ARIMA models have been utilized to analyze time-
series data of influenza cases, enabling researchers to predict future trends and understand the
temporal patterns of outbreaks. These models effectively capture the seasonality and fluctuations in
influenza incidence, providing valuable insights for public health planning and intervention
strategies. The flexibility of ARIMA in handling non-stationary data and its capacity to incorporate
both autoregressive and moving average components make it a suitable tool for modelling the
complex behaviour of influenza transmission. Researchers have applied ARIMA models across
various geographical regions and contexts, demonstrating their adaptability and effectiveness in
different epidemiological settings.
The study [16] focuses on the epidemiology of influenza viruses among children in Wuhan, China,
using data from a nine-year surveillance period and forecasting future trends with ARIMA models.
The primary objective was to analyze the positive rates of influenza virus types and apply time series
models for short-term predictions. One strength of this research is its comprehensive data collection
over a substantial period, which enables more reliable statistical modelling. However, the study has
limitations. First, the consistency of throat swab quality across different hospitals could not be
ensured, potentially affecting the accuracy of influenza detection. Additionally, the limited number
of swabs taken per week from a high-volume hospital might not fully represent the broader
population, limiting the generalizability of the findings. Improving the homogeneity of sample
collection and expanding surveillance coverage could enhance future studies.
Paper [16] compares the effectiveness of ARIMA and LSTM models in predicting influenza trends
using air quality data from central Taiwan between 2014 and 2018. Both models were applied to
datasets from three regions: Changhua County, Taichung City, and Nantou County. LSTM models,
particularly the variant incorporating the Extra Trees Classifier feature selection, demonstrated
superior accuracy over ARIMA. This was especially notable in Taichung City, where LSTM ETC
improved prediction accuracy by 73%. However, the study has limitations. The scope of the
prediction relies heavily on air quality and meteorological data, which may not account for other
factors influencing influenza trends, such as population behaviour or healthcare interventions.
Additionally, using a limited geographical area and timeframe may restrict the generalizability of
the findings to other regions or longer periods.
The study [17] investigates using ARIMA and LSTM models to predict influenza-like illness and
respiratory diseases based on air quality data from Taiwan. By comparing both models across five-
and ten-year datasets, the results demonstrate that LSTM models outperform ARIMA regarding
prediction accuracy, particularly when extended to longer periods. LSTM models also benefit from
feature selection techniques, such as matrix correlation and extra trees classifier, which enhance the
prediction results. However, a key limitation of the study is its reliance on data from a single
geographical region, which may limit the generalizability of the results to other locations.
Additionally, while LSTM models show higher accuracy, they require significantly more
computational resources than ARIMA, which could be challenging in large-scale applications.
Moreover, future work may explore real-time data integration and further feature selection
improvements to enhance prediction reliability.
The paper [18] compares the predictive capabilities of ARIMA, GLARMA, and Random Forest
(RF) models in forecasting the frequency of influenza A virus in Ontario swine populations. The RF
model consistently outperformed the ARIMA and GLARMA models in predicting increases in
diagnostic and positive virological submissions, particularly at weekly and monthly intervals. The
RF model demonstrated greater sensitivity and lower error rates, making it a more reliable tool for
surveillance purposes. However, the study has limitations. The data used were based on voluntary
diagnostic submissions, which may not fully represent the swine population, leading to potential
biases in the results. Furthermore, the analysis did not include environmental factors that could
influence IAV trends, limiting the comprehensiveness of the predictive models.
The study [19] examines the relationship between influenza incidence and various climate
indicators in Guangxi, China, using ARIMA and ARIMAX models for predictive analysis. The
findings indicate that air pollution variables, such as NO2 and SO2, and meteorological factors, like
temperature and humidity, significantly influence influenza incidence, with NO2 showing the
strongest positive correlation. The ARIMAX model, which incorporates these exogenous variables,
performed better than the standard ARIMA model, offering improved prediction accuracy. However,
the study is limited by its reliance on data from a single region, which may limit the generalizability
of the results to other geographic areas or climates. Additionally, the influence of other potential
factors, such as healthcare interventions or population movement, was not considered, which could
affect the accuracy of the predictions.
The paper [20] compares the ARIMA and Holt-Winters Exponential Smoothing (HWES) models
for predicting influenza outbreaks based on Twitter data from Australia collected between 2015 and
2017. Both models were tested for their ability to predict influenza cases by analyzing tweets related
to flu symptoms and comparing them to confirmed cases from the Centers for Disease Control. The
ARIMA model showed superior predictive accuracy with a mean relative error of 7.25%, compared
to 11.29% for the HWES model. A major strength of this study lies in utilizing social media data,
which allows for earlier detection of influenza trends compared to traditional CDC reports. However,
the study has limitations, including the inherent noise in social media data, as flu-related tweets do
not always correspond to confirmed cases.
The paper [21] evaluates the effectiveness of different time series models, ARIMA, SARIMA, and
XGBoost, in predicting monthly seasonal influenza cases in Saudi Arabia. The analysis demonstrates
that the XGBoost model significantly outperforms both ARIMA and SARIMA models in accuracy, as
indicated by lower mean absolute error, mean squared error, and root mean squared error. The
XGBoost model's ability to capture complex, nonlinear relationships is well-suited for influenza
prediction. However, one limitation of the study is its reliance on a relatively short dataset covering
just five years, which may not fully capture long-term trends and seasonal variations. Additionally,
the study does not account for other potential influencing factors, such as public health interventions
or demographic variables, which could further improve the predictive power of the models.
Despite the extensive application of ARIMA models in forecasting influenza dynamics across
various regions and contexts, there remains a gap in research focusing on emergent settings like
Kharkiv Oblast. The unique challenges posed by the full-scale Russian invasion necessitate a tailored
approach to influenza modelling. Our study aims to address this gap by applying the ARIMA model
to simulate and predict influenza trends in Kharkiv oblast under these atypical conditions. By
focusing on this emergent setting, we contribute to the existing body of knowledge by demonstrating
the adaptability and effectiveness of ARIMA modelling in environments with limited data and
heightened public health challenges. This research enhances the understanding of influenza
dynamics in conflict-affected regions and underscores the critical role of data-driven methodologies
in informing public health interventions amidst crises.
3. Materials and methods
For this study, we obtained monthly influenza incidence data from the Kharkiv Oblast Centre for
Disease Control and Prevention. The dataset spans from January 2013 to April 2024 and consists of
136 observations representing the number of reported influenza cases in Kharkiv Oblast during this
period. Figure 1 presents the distribution of influenza cases in Kharkiv oblast of Ukraine.
Figure 1: The distribution of influenza morbidity in Kharkiv oblast of Ukraine
We applied Min-Max normalization to the influenza incidence values to prepare the data for
analysis. This scaling technique transforms the data to a fixed range, specifically from 1 to 2.
Normalization is essential for standardizing the data, reducing the impact of varying scales, and
enhancing the performance of the ARIMA model.
The Min-Max normalization is defined as:
$𝑋 − 𝑋*+(, '() &(𝑋'#. − 𝑋'() ) (1)
𝑋!"#$%& = 𝑋'() + ,
𝑋*+(, '#. − 𝑋*+(, '()
where X is the original data point, Xscaled is the normalized data point, Xmin and Xmax are the
desired scaling range, Xorig min and Xorig max are the minimum and maximum values of the original
dataset.
The normalized dataset was then divided into training and testing sets to evaluate the forecasting
capability of the model. The training set included 90% of the data (122 observations from January
2013 to October 2023), while the testing set comprised the remaining 10% (14 observations from
November 2023 to April 2024). This split allows the model to be trained on historical data and tested
on future, unseen data, providing a robust assessment of its predictive accuracy.
The ARIMA model is a widely used statistical approach for time series forecasting [22]. It
combines three components:
1. Autoregressive (AR) Component models the relationship between an observation and a
number of lagged observations.
2. Integrated (I) Component involves differencing the time series to achieve stationarity.
3. Moving Average (MA) Component models the relationship between an observation and a
residual error from a moving average model applied to lagged observations.
An ARIMA model is denoted as ARIMA (p, d, q), where p is the order of the autoregressive part,
d is the degree of differencing, q is the order of the moving average part.
The general form of the ARIMA (p, d, q) model is:
𝛷(𝐵)(1 − 𝐵)& 𝑦/ = 𝛩(𝐵)𝜖/ , (2)
where yt is the time series at time t, B is the backshift operator (Byt = yt-1), F(B) = 1 – f1B – f2B2
– … – fpBp is the autoregressive operator, Q(B) = 1 – q1B – q2B2 – … – qqBq is the moving average
operator, Ît is the error term at time t.
4. Results
To identify the appropriate ARIMA model for our data, we followed these steps:
1. Stationarity Testing: We conducted the Augmented Dickey-Fuller test to assess whether the
time series is stationary. If the series was non-stationary, we applied differencing until
stationarity was achieved, determining the value of d.
2. Autocorrelation Analysis: We analyzed the Autocorrelation Function (ACF) and Partial
Autocorrelation Function (PACF) plots of the stationary series to identify potential values for
p and q.
a. ACF measures the correlation between the time series and its lagged values.
b. PACF measures the correlation between the time series and its lagged values after
removing the effects of shorter lags.
3. Model Selection: Based on the ACF and PACF analyses, we tested multiple ARIMA models
with different combinations of p, d, and q. We evaluated these models using the Akaike
Information Criterion and the Bayesian Information Criterion, selecting the model with the
lowest AIC and BIC values as the optimal one.
4. Parameter Estimation: The parameters of the selected ARIMA model were estimated using
the Maximum Likelihood Estimation (MLE) method. MLE finds the parameter values that
maximize the likelihood function, which measures how well the model explains the observed
data.
With a validated ARIMA model, we proceeded to forecast influenza incidence for the testing
period (November 2023 to April 2024). To evaluate the forecasting performance, we calculated the
following error metrics.
Mean Absolute Percentage Error (MAPE):
)
100% 𝐴/ − 𝐹/ (3)
MAPE = 78 8,
𝑛 𝐴/
/01
where At is the actual value at time t, Ft is the forecasted value at time t, n is the number of
forecasts.
Mean Absolute Error (MAE):
)
1 (4)
MAE = 7|𝐴/ − 𝐹/ |.
𝑛
/01
The best ARIMA order is (10, 1, 11).
Figure 2 presents the forecasted influenza morbidity in Kharkiv oblast of Ukraine.
Figure 2: The forecasted influenza morbidity in Kharkiv oblast of Ukraine
MAPE of the model is 9.5879.
MAE of the model is 37.5099.
5. Discussion
The paper applied the ARIMA model to simulate and predict influenza trends in the Kharkiv Oblast
of Ukraine, demonstrating the model’s adaptability to conflict-affected regions where healthcare and
surveillance systems are under stress. Applying time-series models like ARIMA provides valuable
insights for public health planning, particularly in areas where real-time data collection may be
compromised. The results of this study underscore the potential of data-driven models to aid
decision-making processes in regions facing complex socio-political challenges.
The ARIMA (10,1,11) model was selected as the optimal model, ensuring minimal forecast error.
This choice reflects ARIMA’s robustness in capturing the seasonality and fluctuations in influenza
dynamics, which are influenced by environmental and behavioural factors.
The model’s performance was evaluated using standard metrics like MAPE and MAE. The model
demonstrated strong predictive accuracy with a MAPE of 9.5879% and an MAE of 37.5099. These
results suggest the model is well-suited for forecasting influenza trends in regions where data quality
and availability may vary. However, while the error margins are within an acceptable range, there
is room for improvement. The performance might be further optimized by integrating additional
variables such as climate data, healthcare access, or vaccination rates, which have been shown in
other studies to influence influenza transmission.
This study’s findings highlight the ARIMA models’ significant role in informing public health
interventions, especially in conflict-affected regions like Kharkiv oblast. The ability to forecast
influenza incidence with reasonable accuracy enables health authorities to allocate resources
effectively, plan vaccination campaigns, and implement early interventions to curb outbreaks.
The situation in Kharkiv Oblast, exacerbated by the Russian invasion, presents a unique case
where healthcare infrastructure is strained, and access to basic services, including vaccinations and
antiviral medications, is limited. In this context, predictive models like ARIMA become essential tools
for disease surveillance, compensating for gaps in real-time data collection. By identifying trends in
influenza transmission, authorities can take preemptive measures to protect vulnerable populations,
particularly those displaced by the conflict and living in overcrowded conditions.
While the study demonstrates the effectiveness of the ARIMA model, it is important to
acknowledge its limitations. While capable of capturing time-series patterns, the ARIMA model is
inherently limited by its linear nature. Influenza dynamics, influenced by non-linear factors such as
sudden changes in weather, migration patterns, or population immunity, may not be fully accounted
for by the ARIMA model alone. To address these limitations, future studies could explore the
integration of machine learning approaches, such as Long Short-Term Memory (LSTM) models,
which have been shown to outperform ARIMA in certain contexts due to their ability to capture non-
linear relationships in the data.
In conflict-affected regions, interruptions in surveillance systems can result in incomplete or
fragmented data, potentially affecting the accuracy of the forecasts. Future studies could improve
model robustness by incorporating real-time data sources, such as social media signals or
environmental data, to enhance the model’s predictive capabilities.
6. Conclusions
This study developed and applied an ARIMA (10,1,11) model to forecast influenza dynamics in
Kharkiv Oblast, Ukraine, a region facing significant public health challenges due to the ongoing full-
scale Russian invasion. The model demonstrated strong predictive accuracy, as indicated by the low
error margins, providing essential insights for influenza forecasting in regions with disrupted
healthcare systems.
The primary contribution of this study lies in its application of ARIMA modelling to a conflict-
affected region, emphasizing the adaptability of time-series models in non-ideal conditions. Focusing
on Kharkiv oblast, the study highlights the capacity of data-driven models to fill gaps left by
compromised traditional surveillance systems. This research offers a data-centric approach to
support public health efforts where direct interventions and accurate data collection may be difficult.
This scientific novelty stems from applying ARIMA in a complex, conflict-affected setting. While
ARIMA has been widely used for influenza forecasting in stable environments, this study shows that
the model can be adapted for effective forecasting even in regions with unreliable or incomplete data.
This research also lays the foundation for further exploration into how predictive models can be
tailored to dynamic, high-risk environments with limited surveillance infrastructure.
The study’s practical novelty is demonstrated through its immediate applicability in real-world
scenarios where public health authorities face resource constraints. The ARIMA model provides
health agencies in Kharkiv and similar regions with a practical tool to effectively forecast influenza
outbreaks and allocate resources. As the region’s healthcare infrastructure continues to be under
strain due to conflict, such models could help prioritize vaccination campaigns and emergency
responses to mitigate the spread of influenza.
While this study focused on the ARIMA model, future research could expand the scope by
incorporating other predictive models, such as LSTM or hybrid approaches, to improve forecasting
accuracy in highly dynamic environments. Additionally, integrating real-time data sources,
including climate, migration, and social media indicators, would enhance the model’s robust model’s
predictive power. Expanding this research to other conflict-affected regions or including other
infectious diseases would also provide a broader understanding of how predictive models can aid
public health decision-making in volatile situations.
In conclusion, this research underscores the importance of predictive modelling in public health,
particularly in conflict-affected regions. It offers a roadmap for future studies to build upon its
findings by incorporating more advanced methodologies and diverse data sources.
Acknowledgements
The study was funded by the National Research Foundation of Ukraine in the framework of the
research project 2023.03/0197 on the topic “Multidisciplinary study of the impact of emergency
situations on the infectious diseases spreading to support management decision-making in the field
of population biosafety”.
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