RADIOELECTRONIC AND COMPUTER SYSTEMS

The COVID-19 pandemic has posed a challenge to public health systems worldwide. As of March 2022, almost 500 million cases have been reported worldwide. More than 6.2 million people died. The war that Russia launched for no reason on the territory of Ukraine is not only the cause of the death of thousands of people and the destruction of dozens of cities but also a large-scale humanitarian crisis. The military invasion also affected the public health sector. The impossibility of providing medical care, non-compliance with sanitary conditions in areas where active hostilities are occurring, high population density during the evacuation, and other factors contribute to a new stage in the spread of COVID-19 in Ukraine. Building an adequate model of the epidemic process will make it possible to assess the actual statistics of the incidence of COVID-19 and assess the risks and effectiveness of measures to curb the curse of the disease epidemic process. The article aims to develop a simulation model of the COVID-19 epidemic process in Ukraine and to study the results of an experimental study in war conditions. The research is targeted at the epidemic process of COVID-19 under military conditions. The subjects of the study are models and methods for modeling the epidemic process based on statistical machine learning methods. To achieve the study's aim, we used forecasting methods and built a model of the COVID-19 epidemic process based on the polynomial regression method. Because of the experiments, the accuracy of predicting new cases of COVID-19 in Ukraine for 30 days was 97,98%, and deaths of COVID-19 in Ukraine – was 99,87%. The model was applied to data on the incidence of COVID-19 in Ukraine for the first month of the war (02/24/22 - 03/25/22). The calculated predictive values showed a significant deviation from the registered statistics. Conclusions. This article describes experimental studies of implementing the COVID-19 epidemic process model in Ukraine based on the polynomial regression method. The constructed model was sufficiently accurate in deciding on anti-epidemic measures to combat the COVID-19 pandemic in the selected area. The study of the model in data on the incidence of COVID-19 in Ukraine during the war made it possible to assess the completeness of the recorded statistics, identify the risks of the spread of COVID-19 in wartime, and determine the necessary measures to curb the epidemic curse of the incidence of COVID-19 in Ukraine. The investigation of the experimental study results shows a significant decrease in the registration of the COVID-19 incidence in Ukraine. An analysis of the situation showed difficulty in accessing medical care, a reduction in diagnosis and registration of new cases, and the war led to the intensification of the COVID-19 epidemic process.

epidemic model; epidemic process; epidemic simulation; simulation; COVID-19; polynomial regression; war

Kumar, A., Singh, R., Kaur, J., Pandey, S., Sharma, V., Thakur, L., Sati, S., Mani, S., Asthana, S., Sharma, T.K., Chaudhuri, S., Bhattacharyya, S., Kumar, N. Wuhan to world: the COVID-19 pandemic. Frontiers in cellular and infection microbiology, 2021, vol. 11, article no. 596201. DOI: 10.3389/fcimb.2021.596201.

Cucinotta, D., Vanelli, V. WHO declares COVID-19 a pandemic. Acta biomedica: Atenei Parmensis, 2020, vol. 91, iss. 1, pp. 157-160. DOI: 10.23750/abm.v91i1.9397.

Parasher, A. COVID-19: current understanding of its Pathophysiology, Clinical presentation and Treatment. Postgraduate medical journal, 2021, vol. 97, iss. 1147, pp. 312-320. DOI: 10.1136/postgradmedj-2020-138577.

Yuce, M., Filiztekin, E., Ozkaya, K.Z. COVID-19 diagnosis – a review of current methods. Biosensors & Bioelectronics, 2021, vol. 172, article no. 112752. DOI: 10.1016/j.bios.2020.112752.

Erdinc, B., Sahni, S., Gotlieb, V. Hematological manifestations and complications of COVID-19. Advances in clinical and experimental medicine: official organ Wroclaw Medical University, 2021, vol. 30, iss. 1, pp. 101-107. DOI: 10.17219/acem/130604.

Fedushko, S., Ustyianovych, T. E-commerce customers behavior research using cohort analysis: a case study of COVID-19. Journal of Open Innovation: Technology, Market, and Complexity, vol. 8, iss. 1, article no. 12. DOI: 10.3390/joitmc8010012.

Misiuk, T., Kondratenko, Y., Sidenko, I., Kondratenko, G. Computer vision mobile system for education using augmented reality technology. Journal of Mobile Multimedia, 2021, vol. 17, iss. 4, pp. 555-576. DOI: 10.13052/jmm1550-4646.1744.

Davidich, N., Galkin, A., Iwan, S., Kijewska, K., Chumachenko, I., Davidich, Y. Monitoring of urban freight flows distribution considering the human factor. Sustainable Cities and Society, 2021, vol. 75, article no. 103168. DOI: 10.1016/j.scs.2021.103168.

Izonin, I., Tkachenko, R., Dronyuk, I., Tkachenko, P., Gregus, M., Rashkevych, M. Predictive modeling based on small data in clinical medicine: RBF-based additive input-doubling method. Mathematical Biosciences and Engineering, 2021, vol. 18, iss. 3, pp. 2599-2613. DOI: 10.3934/mbe.2021132.

Saadi, N., Chi, Y.L., Ghosh, S., Eggo, R.M., McCarthy, C.V., Quaife, M., Dawa, J., Jit, M., Vassall, A. Models of COVID-19 vaccine prioritization: a systematic literature search and narrative review. BMC Medicine, 2021, vol. 19, article no. 318. DOI: 10.1186/s12916-021-02190-3.

Strilets, V., Donets, V., Ugryumov, M., Artiuch, S., Zelenskyi, R., Goncharova, T. Agent-oriented data clustering for medical monitoring. Radioelectronic and Computer Systems, 2022, no. 1 (101), pp. 103-114. DOI: 10.32620/reks.2022.1.08.

Kern, C., Schoning, V., Chaccour, C., Hammann, F. Modeling of SARS-CoV-2 treatment effects for informed drug repurposing. Frontiers in Pharmacology, 2021, vol. 12, article no. 625678. DOI: 10.3389/fphar.2021.625678.

Shakeel, S. M., Kumar, N. S., Madalli, P. P., Srinivasaiah, R., Swamy, D. R. COVID-19 prediction models: a systematic literature review. Osong Public Health and Research Perspectives, 2021, vol. 12, iss. 4, pp. 215-229. DOI: 10.24171/j.phrp.2021.0100.

Cimini, C., Pezzotta, G., Lagorio, A., Pirola, F., Cavalieri, S. How can hybrid simulation support organizations in assessing COVID-19 containment measures. Healthcare, 2021, vol. 9, article no. 1412. DOI: 10.3390/healthcare9111412.

Yakovlev, S., Bazilevych, K., Chumachen¬ko, D., Chumachenko, T., Hulianytskyi, L., Meniailov, I., Tkachenko. A. The concept of developing a decision support system for the epidemic morbidity control. CEUR Workshop Proceedings, 2020, vol. 2753, pp. 265–274.

Kermack, W.O., McKendrick, A.G. A contribution to the mathematical theory of epidemics. Proceedings of the royal society A: Mathematical, physical and engineering sciences, 1927, vol. 115, iss. 772, pp. 700-721. DOI: 10.1098/rspa.1927.0118.

Liu, J., Xie, W., Wang, Y., Xiong, Y., Chen, S., Han, J., Wu, Q. A comparative overview of COVID-19, MERS and SARS: review article. International journal of surgery, 2020, vol. 81, pp. 1-8. DOI: 10.1016/j.ijsu.2020.07.032.

Zhou, Y., Ma, Z., Brauer, F. A discrete epidemic model for SARS transmission and control in China. Mathematical and Computer Modelling, 2004, vol. 40, iss. 13, pp. 1491-1506. DOI: 10.1016/j.mcm.2005.01.007.

Ng, T.W., Turinici, G., Danchin, A. A double epidemic model for the SARS propagation. BMC Infectious Diseases, 2003, vol. 3, article no. 19. DOI: 10.1186/1471-2334-3-19.

Bauch, C.T., Lloyd-Smith, J.O., Coffee, M.P., Galvani, A.P. Dynamically modeling SARS and other newly emerging respiratory illnesses: past, present and future. Epidemiology, 2005, vol. 16, iss. 6, pp. 791-801. DOI: 10.1097/01.ede.0000181633.80269.4c.

Yong, B., Owen, L. Dynamical transmission model of MERS-CoV in two areas. AIP Conference Proceedings, 2016, vol. 1716, iss. 1, article no. 020010. DOI: 10.1063/1.4942993.

Tang, S., Ma, W., Bai, P. A novel dynamic model describing the spread of the MERS-CoV and the expression of dipeptidyl peptidase 4. Computational and Mathematical Methods in Medicine, 2017, vol. 2017, article no. 5285810. DOI: 10.1155/2017/5285810.

Chang, H.J. Estimation of basic reproduction number of the Middle East respiratory syndrome coronavirus (MERS-CoV) during the outbreak in South Korea, 2015. BioMedical Engineering Online, 2017, vol. 16, article no. 79. DOI: 10.1186/s12938-017-0370-7.

Cooper, I., Mondal, A., Antonopoulos, C. G. A SIR model assumption for the spread of COVID-19 in different communities. Chaos, Solutions & Fractals, 2020, vol. 139, article no. 110057. DOI: 10.1016/j.chaos.2020.110057.

Ajbar, A., Abdelhamis, A., Alqahtani, R.T., Boumaza, M. Dynamics of an SIR-based COVID-19 model with linear incidence rate, nonlinear removal rate, and public awareness. Frontiers in Physics, 2021, vol. 9, article no. 634251. DOI: 10.3389/fphy.2021.634251.

Talukder, H., Debnath, K., Raquib, A., Uddin, M.M., Hussain, S. Estimation of basic reproduction number (R0) of novel coronavirus (COVID-19) from SEIR model in perspective of Bangladesh. Journal of Infectious Diseases and Epidemiology, 2020, vol. 6, article no. 144. DOI: 10.23937/2474-3658/1510144.

Ghostine, R., Gharamti, M., Hassrouny, S., Hoteit, I. An extended SEIR model with vaccination for forecasting the COVID-19 pandemic in Saudi Arabia using an ensemble Kalman filter. Mathematics, 2021, vol. 9, article no. 636. DOI: 10.3390/ math9060636.

Leontitsis, A., Senok, A., Alsheikh-Ali, A., Al Nasser, Y., Loney, T., Alshamsi, A. SEAHIR: a specialized compartmental model for COVID-19. International Journal of Environmental Research and Public Health, 2021, vol. 18, iss. 5, article no. 2667. DOI: 10.3390/ijerph18052667.

Moein, S., Nickaeen, N., Roointan, A., Borhani, N., Heidary, Z., Javanmard, S. H., Ghaisairi, J., Gheisari, Y. Inefficiency of SIR models in forecasting COVID-19 epidemic: a case study of Isfahan. Scientific reports, 2021, vol. 11, article no. 4725. DOI: 10.1038/s41598-021-84055-6.

Kerr, C. C., Stuart, R. M., Mistry, D., Abeysuriya, R. G., Rosenfeld, K., Hart, G. R., Nunez, R. C., Cohen, J. A., Selvaraj, P., Hagedorn, B., George, L., Jastrzebski, M., Izzo, A. S., Fowler, G., Palmer, A., Delport, D., Scott, N., Kelly, S.L., Bennette, C. S., Wagner, B. G., Chang, S. T., Oron, A. P., Wenger, E. A., Panovska-Griffiths, J., Famulare, M., Klein, D. J. Covasim: an agent-based model of COVID-19 dynamics and interventions. PLOS Computational Biology, 2021, vol. 17, iss. 7, article no. e1009149. DOI: 10.1371/journal.pcbi.1009149.

Latkowski, R., Dunin-Keplicz, B. An agent-based COVID-19 simulator: extending Covasim to the Polish context. Procedia Computer Science, 2021, vol. 192, pp. 3607-3616. DOI: 10.1016/j.procs.2021.09.134.

Pham, Q. D., Stuart, R. M., Nguyen, T. V., Luong, Q. C., Tran, Q. D., Pham, T. Q., Phan, L. T., Dang, T. Q., Tran, D. N., Do, H. T., Mistry, D., Klein, D. J., Abeysuriya, R. G., Oron, A. P., Kerr, C. C. Estimating and mitigating the risk of COVID-19 epidemic rebound associated with reopening of international borders in Vietnam: a modelling study. The Lancet Global Health, 2021, vol. 9, iss. 7, pp. e916-e924. DOI: 10.1016/S2214-109X(21)00103-0.

Cuevas, E. An agent-based model to evaluate the COVID-19 transmission risks in facilities. Computer in Biology and Medicine, 2020, vol. 121, article no. 103827. DOI: 10.1016/j.compbiomed.2020.103827.

Datta, A. Winkelstein, P., Sen, S. An agent-based model of spread of a pandemic with validation using COVID-19 data from New York State. Physica A, 2022, vol. 585, article no. 126401. DOI: 10.1016/j.physa.2021.126401.

Alali, Y., Harrou, F., Sun, Y. A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models. Scientific Reports, 2022, vol. 12, article no. 2467. DOI: 10.1038/s41598-022-06218-3.

Mojjada, R. K., Yadav, A., Prabhu, A. V., Natarajan, Y. Machine learning models for COVID-19 future forecasting. Materialstoday: proceedings, 2020. DOI: 10.1016/j.matpr.2020.10.962.

Moulaei, K., Shanbehzadeh, M., Mohammadi-Taghiabad, Z., Kazemi-Arpanahi, H. Comparing machine learning algorithms for predicting COVID-19 mortality. BMC Medical Informatics and Decision Making, 2022, vol. 22, article no. 2. DOI: 10.1186/s12911-021-01742-0.

Alballa, N., Al-Turaiki, I. Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: a review. Informatics in medicine unlocked, 2021, vol. 24, article no. 100564. DOI: 10.1016/j.imu.2021.100564.

Kushwaha, S., Bahl, S., Bagha, A. K., Parmar, K. S., Javaid, M., Haleem, A., Singh, R. P. Significant applications of machine learning for COVID-19 pandemic. Journal of Industrial Integration and Management, 2020, vol. 5, no. 4, pp. 453-479. DOI: 10.1142/S2424862220500268.

Chumachenko, D., Meniailov, I., Bazile¬vych, K., Chub, O. On COVID-19 epidemic process simulation: three regression approaches investigation. Radioelectronic and Computer Systems, 2022, no. 1 (101), pp. 6-22. DOI: 10.32620/reks.2022.1.01.

Gankin, Y., Nemira, A., Koniukhovskii, V., Chowell, G., Weppelmann, T. A., Skums, P., Kirpich, A. Investigating the first stage of the COVID-19 pandemic in Ukraine using epidemiological and genomic data. Infection, genetics and evolution: journal of molecular epidemiology and evolutionary genetics in infectious diseases, 2021, vol. 95, article no. 105087. DOI: 10.1016/j.meegid.2021.105087.

Ivats-Chabina, A. R., Korolchuk, O. L., Kachur, A. Yu., Smiianov, V. A. Healthcare in Ukraine during the epidemic: difficulties, challenges and solutions. Wiadomosci lekarskie, 2021, vol. 74, iss. 5, pp. 1256-1261. DOI: 10.36740/WLek202105139.

Matiashova, L., Isayeva, G., Shanker, A., Tsagkaris, C., Aborode, A. T., Essar, M. Y., Ahmad, S. COVID-19 vaccination in Ukraine: an update on the status of vaccination and the challenges at hand. Journal of medical virology, 2021, vol. 93, iss. 9, pp. 5252-5253. DOI: 10.1002/jmv.27091.

WHO COVID-19 Dashboard. Geneva: World Health Organization, 2020. Available at: https://covid19.who.int/ (accessed 25.03.2022).

Surveillance system for attacks on health care (SSA). World Health Organization, 2022. Available at: https://extranet.who.int/ssa/Index.aspx (accessed 25.03.2022).

Coronavirus Resource Center. Johns Hopkins University & Medicine, 2020. Available at: https://coronavirus.jhu.edu/ (accessed 25.03.2022).

Ostertagova, E. Modelling using polynomial regression. Procedia Engineering, 2012, vol. 48, pp. 500-506. DOI: 10.1016/j.proeng.2012.09.545.

Linka, K., Peirlinck, M., Costabal, F. S., Kuhl, E. Outbreak dynamics of COVID-19 in Europe and the effect of travel restrictions. Computer methods in biomechanics and biomedical engineering, 2020, vol. 23, iss. 11, pp. 710-717. DOI: 10.1080/10255842.2020.1759560.

Hamid, M. A., Aditama, D., Permata, E., Kholifah, N., Nurtanto, M., Majid, N. W. A. Simulating the COVID-19 epidemic event and its prevention measures using Python programming. Indonesian journal of electrical engineering and computer science, 2022, vol. 26, no. 1, pp. 278-288. DOI: 10.11591/ijeecs.v26.i1.pp278-288.

Yu, X. Modeling return of the epidemic: impact of population structure, asymptomatic infection, case importation and personal contacts. Travel medicine and infectious disease, 2020, vol. 37, 101858. DOI: 10.1016/j.tmaid.2020.101858.

Dhamodharavadhani, S., Rathipriya, R., Chatterjee, G. M. COVID-19 mortality rate prediction for India using statistical neural network models. Frontiers in Public Health, 2020, vol. 8, article no. 441. DOI: 10.3389/fpubh.2020.00441.

Hong, W. H., Yap, J. H., Selvachandran, G., Thong, P. H., Son, L. H. Forecasting mortality ratesusing hybrid Lee-Carter model, artificial neural network and random forest. Complex and intelligent systems, 2021, vol. 7, pp. 163-189. DOI: 10.1007/s40747-020-00185-w.

Ebubeogu, A. F., Ozigbu, C. E., Maswadi, K., Seixas, A., Ofem, P., Conserve, D. F. Predicting the number of COVID-19 infections and deaths in USA. Globalization and health, 2022, vol. 18, article no. 37. DOI: 10.1186/s12992-022-00827-3.

Rauf, A., Abu-Izneid, T., Olatunde, A., Khalil, A. A., Alhumaydhi, F. A., Tufail, T., Shariati, M. A., Rebezov, M., Almarhoon, Z. M., Mabkhot, Y. N., Alsayari, A., Rengasamy, K. R. R. COVID-19 pandemic: epidemiology, etiology, conventional and non-conventional therapies. International journal of environmental research and public health, 2020, vol. 17, iss. 21, 8155. DOI: 10.3390/ijerph17218155.

Ge, H., Wang, X., Yuan, X., Xiao, G., Wang, C., Deng, T., Yuan, Q., Xiao, X. The epidemiology and clinical information about COVID-19. European journal of clinical microbiology and infectious diseases, 2020, vol. 39, pp. 1011-1019. DOI: 10.1007/s10096-020-03874-z.

Nishiura, H., Kobayashi, T., Miyama, T., Suzuki, A., Jung, S. M., Hayashi, K., Kinoshita, R., Yang, Y., Yuan, B., Akhmetzhanov, A. R., Linton, N.M. Estimation of the asymptomatic ratio of novel coronavirus infections (COVID-19). International journal of infectious diseases, 2020, vol. 94, pp. 154-155. DOI: 10.1016/j.ijid.2020.03.020.

Gandhi, M., Yokoe, D. S., Havlir, D. V. Asymptomatic transmission, the Achilles’ Heel of current strategies to control COVID-19. The New England journal of medicine, 2020, vol. 382, pp. 2158-2160. DOI: 10.1056/NEJMe2009758.

Buitrago-Garcia, D., Egli-Gany, D., Counotte, M.J., Hossmann, S., Imeri, H., Ipekci, A. M., Salanti, G., Low, N. Occurrence and transmission potential of asymptomatic and presymptomatic SARS-CoV-2 infections: a living systematic review and meta-analysis. PLOS Medicine, 2020, vol. 17, iss. 9, article no. e1003346. DOI: 10.1371/journal.pmed.1003346.

Arora, S., Grover, V., Saluja, P., Algarni, Y. A., Saquib, S. A., Asif, S. M., Batra, K., Alshahrani, M. Y., Das, G., Jain, R., Ohri, A. Literature review of Omicron: a grim reality amidst COVID-19. Microorganisms, 2022, vol. 10, iss. 2, article no. 451. DOI: 10.3390/microorganisms10020451.

Kannan, S., Ali, P. S. S., Sheeza, A. Omicron (B.1.1.529) – variant of concern – molecular profile and epidemiology: a mini review. European review for medical and pharmacological sciences, 2021, vol. 25, iss. 24, pp. 8019-8022. DOI: 10.26355/eurrev_202112_27653.