Ukraine on COVID-19 Epidemic Process in Moldova

Abstract

The novel coronavirus pandemic has become a global challenge and has shown that health systems worldwide are unprepared for pandemics of this magnitude. The war in Ukraine, escalated by Russia on February 24, 2022, brought deaths and a humanitarian catastrophe and stimulated the spread of COVID-19. Most refugees who evacuated from the war crossed the border with other countries. At the end of July, almost 550 thousand people crossed the border with Moldova. This study is devoted to modeling the impact of migration processes on the dynamics of COVID-19 in Moldova. For this, a machine learning model was built based on the polynomial regression method. The forecast accuracy a month before the escalation of the war was from 98.77% to 96.37% for new cases and from 99.8% to 99.75% for fatal cases. The forecast accuracy for the first month after the escalation of the war was from 99.96% to 99.34% for new cases and from 99.91% to 99.88% for fatal cases. The high accuracy of the model, both before the war and with the start of its escalation, suggests that the migration flows of refugees from Ukraine to Moldova did not affect the dynamics of The COVID-19 pandemic began in December 2019 in Wuhan, China. In the first few months, the virus spread throughout the planet. The rapid spread of the new coronavirus has led the World Health Organization to declare a global pandemic of COVID-19. At the end of July 2022, almost 580 million cases were registered worldwide, almost 6.5 million of which died [1].

Keywords

• 1
• COVID-19
• machine learning
• epidemic model
• polynomial regression
• war

COVID-19.

1. Introduction

2022 Copyright for this paper by its authors. thousand people crossed the border with Moldova, and more than 85 thousand people received refugee status in Moldova.

In the first weeks of the escalation of the war, there were large concentrations of people at border

crossings and in evacuation transport. At the same time, refugees' documents were checked according to a simplified procedure, and medical documents were not controlled. Mass gatherings, a low level of vaccination against COVID-19 in Ukraine, and the lack of control of medical records can lead to outbreaks of infectious diseases in countries hosting refugees.

The COVID-19 pandemic has given impetus to the development of health information tools.

Groups of scientists around the world have developed tools for modeling epidemic processes [5], analyzing medical data [6], medical diagnostics [7], assessing social factors [8], decision-making for healthcare organizations [9], and processing medical data [10], etc.

The study aims to build a machine learning model based on the polynomial regression method for

assessing the incidence of COVID-19 in Moldova after the escalation of the Russian war in Ukraine.

The relevance of the study is justified by the fact that the escalation of the Russian war in Ukraine

affects the dynamics of infectious diseases not only in Ukraine, but also in countries that have received a large number of refugees. Therefore, this study is the first step - testing the hypothesis of the influence of migration flows on the dynamics of the incidence of COVID-19 in Moldova.

Research is part of a complex, intelligent information system for epidemiological diagnostics, the concept of which is discussed in [11].

2. Materials and Methods

Regression analysis is a set of methods for studying the influence of one or more independent

variables on the dependent one [12]. Regression analysis reflects only the mathematical dependence of variables and not a causal relationship. The main tasks for which regression analysis is used are predicting the value of a dependent variable using independent variables, determining the contribution of individual independent variables to the variation of the dependent variable, and determining the degree of determination of the variation of the dependent variable by independent variables.

Polynomial regression is a machine learning algorithm that trains a linear model on non-linear data [13]. Polynomial regression allows you to use a linear model even if the data is non -linear by adding additional features to the data.

The polynomial regression model has the following form:

A polynomial regression model can be expressed in matrix form: = 0 + 1 + 2 2 + ⋯ + + .

1 2 ⋮ [ ] 3 = 1 3 32 1 1 12 1 2 22 ⋮ [1 ⋮ ⋮ 2 … 1 … 2 … 3 ⋱

⋮ … ] [ ] 0 1 ⋮ 2 + 3 .

1 2 ⋮ [ ] ( 1 ) ( 2 )

Although polynomial regression is a particular case of multiple linear regression, interpreting a fitted polynomial regression model requires a slightly different perspective. When fitting polynomial regression, it is often difficult to interpret individual coefficients because the underlying monomials can be highly correlated. Although orthogonal polynomials can reduce the correlation, it is usually more informative to consider the fitted regression function as a whole. The point of simultaneous confidence intervals can then be used to determine the uncertainty of the estimate of the regression function.

Polynomial regression makes it possible to model non-linear shared data. A polynomial regression model is more flexible and can model complex relationships. However, models require careful design, and prior knowledge of the data is required. To assess the accuracy of the forecast, the mean absolute error was used:

= , ( 3 ) analyzed period, n is the number of periods. where Yi is the registered incidence for the analyzed period, ̂ is the predicted incidence for the

3. Results

The Python programming language was used to implement the study. Data used for modeling

included new cases and deaths provided by the World Health Organization Coronavirus (COVID-19)

Dashboard [1]. To verify the model, a forecast of new cases and deaths from COVID-19 in Moldova

was built for January 25, 2022 - February 23, 2022. Figure 1 shows the forecast for new cases of

COVID-19.

Duration of forecast

7 days 10 days 20 days 30 days

New cases

Figure 2 shows the forecast of fatal cases of COVID-19 in Moldova from January 25, 2022, to February 23, 2022.

The results of COVID-19 simulations in Moldova a month before the Russian war in Ukraine

showed high accuracy for both short-term and long-term forecasts for 30 days.

To assess the impact of migration processes, a forecast was made of the dynamics of the COVID19 epidemic process in Moldova for the first month after the escalation of the Russian war in Ukraine. Figure 3 shows the forecast of cumulative new cases of COVID-19 in Moldova from February 24, 2022, to March 25, 2022.

Figure 4 shows the forecast of cumulative fatal cases of COVID-19 in Moldova from February 24, 2022, to March 25, 2022.

Table 2 shows the forecast accuracy of the constructed model in Moldova for the period from February 24, 2022, to March 25, 2022.

4. Conclusions

The escalation of the Russian war in Ukraine has stimulated the spread of COVID-19. Identifying

new cases in areas with a destroyed public health infrastructure is challenging. Diagnostics in the temporarily occupied territories are complex due to the impossibility of conducting laboratory tests.

Treatment is complicated due to the redistribution of medical capacities to help the armed forces and the affected civilian population. The accumulation of people in bomb shelters and during evacuations stimulates the spread of the virus. Mentally, people feel the danger of war, not infection with COVID19, so anti-epidemic measures are not observed.

Almost 550,000 refugees from Ukraine crossed the border with Moldova. Social distance, mask regime, and other control measures were not observed during the evacuation. Refugees have not been tested for COVID-19 vaccination status.

However, the high accuracy of the model built as part of this study shows that the migration flows

of refugees from Ukraine have not been the critical factor of changing in the dynamics of the spread of COVID-19 in Moldova. It can be concluded that the increase in the incidence associated with the migration of the population was offset by the natural decrease in the incidence of COVID-19 in

Moldova, which was observed from late February 2022 – early March 2022.

However, to avoid new outbreaks, it is necessary to analyze the medical data of refugees arriving in third countries. Also, a necessary tool is the prioritization of vaccination against COVID-19 for refugees who have not completed the entire course of vaccination.

This study is only the first step in the study of the impact of the escalation of the Russian war in

Ukraine on the public health system and, in particular, on changes in the patterns of behavior of the epidemic process of infectious disease. Therefore, to test the hypothesis about the impact of migration flows on the dynamics of the COVID-19 epidemic process in Moldova, the simplest statistical machine learning method, polynomial regression, was chosen.

Future research directions are developing more complex models and methods that will account for the stochastic nature of the spread of infectious diseases and the heterogeneity of the population. Such models will make it possible to identify specific factors influencing the epidemic process and evaluate their information content.

5. Acknowledgements

The study was funded by the National Research Foundation of Ukraine in the frame-work of the research project 2020.02/0404 on the topic “Development of intelligent technologies for assessing the epidemic situation to support decision-making within the population biosafety management” 6. References [6] I. Izonin, et. al., Predictive modeling based on small data in clinical medicine: RBF-based additive input-doubling method, Mathematical Biosciences and Engineering 18 ( 3 ) (2021): 25992613. doi: 10.3934/mbe.2021132 [7] A. Nechyporenko, et. al., Assessment of measurement uncertainty of the uncinated process and middle nasal concha in spiral computed tomography data, 2019 IEEE International Scientific

Practical Conference: Problems of Infocommunications Science and Technology, PIC S and T

2019 – Proceedings (2019): 585-588. doi: 10.1109/PICST47496.2019.9061557. [8] O. Zakharchenko, et. al. Multifaced nature of social media content propagating COVID-19 vaccine hesitancy: Ukrainian case, Procedia Computer Science 198 (2022): 682-687. doi: 10.1016/j.procs.2021.12.306. [9] N. Davidich, et. al. Monitoring of urban freight flows distribution considering the human factor,

Sustainable Cities and Society 75 (2021): 103168. doi: 10.1016/j.scs.2021.103168. [10] O. Skitsan, I. Meniailov, K. Bazilevych, H. Padalko, Evaluation of the informative features of cardiac studies diagnostic data using the Kullback method, CEUR Workshop Proceedings 2917 (2021): 186-195. [11] S. Yakovlev, et. al., The concept of developing a decision support system for the epidemic morbidity control, CEUR Workshop Proceedings 2753 (2020): 265-274. [12] M. Dou, C. Qin, G. Li, C. Wang, Research on calculation method of free flow discharge based on artificial neural network and regression analysis, Flow Measurement and Instrumentation 72 (2020) 101707. doi: 10.1016/j.flowmeasinst.2020.101707. [13] E. Ostertagova, Modelling using Polynomial Regression, Procedia Engineering 48 (2012): 500506. doi: 10.1016/j.proeng.2012.09.545

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