Кыргызстандын Саламаттык Сактоо
Zdravoohraneniye Kyrgyzstana

ISSN 1694-8068 (Print)

ISSN 1694-805X (Online)

Кирүү

COVID-19 эпидемиясынын математикалык моделдөө ыкмалары: методдор, сценарийлер жана практикалык колдонулушу

https://dx.doi.org/10.51350/zdravkg2026.1.3.18.148.153
COVID-19 эпидемиясынын математикалык моделдөө ыкмалары: методдор, сценарийлер жана практикалык колдонулушу
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Корутунду Авторлор жөнүндө Шилтемелер

Корутунду

Киришүү. COVID-19 пандемиясы жогорку деңгээлдеги белгисиздик шартында башкаруучулук чечимдерди ыкчам кабыл алуу зарылдыгын актуалдаштырды. Бул контекстте эпидемиологиялык кырдаалды талдоого жана анын өнүгүү сценарийлерин алдын ала айтууга мүмкүндүк берген негизги инструменттердин бири катары математикалык модел дөө кеңири колдонулду. Максаты. COVID-19 илдетине байланыштуу математикалык моделдө- өнүн учурдагы багыттарын системалаштыруу жана аларды ресурстары чектелген аймактар үчүн колдонуу мүмкүндүгүн баалоо. Материалдар жана ыкмалар. Макалада COVID-19 эпидемиясын математикалык моделдөөнүн заманбап ыкмалары боюнча системалаштырылган адабияттарга сереп берилет. Классикалык компартменттик моделдер (SIR, SEIR жана алардын модификациялары), убакыт катарларына негизделген моделдер (ARIMA, SSA), жасалма интеллект элементтери менен гибриддик моделдер, ошондой эле агентке негизделген жана стохастикалык моделдер каралат. Жыйынтыктар. Ар кандай эпидемиологиялык шарттарда, анын ичинде Кыргыз Республикасы сыяктуу ресурстары чектелген өлкө- лөрдө бул моделдердин колдонууга ылайыктуулугу талданды. CoMo Consortium платформасынын алкагында SEIR моделин колдонууга өзгөчө көңүл бурулду. Ар кандай моделдердин негизги артыкчылыктары жана чектөөлөрү сүрөттөлүп, жергиликтүү шарттарга ылайыкташтыруу боюнча сунуштар берилди. Корутунду. COVID-19 пандемиясынын шартында моделдөөнү эпидемиологиялык көзөмөл, ыкчам статистика жана тобокелдиктерди баалоо менен айкалыштыруу ыкчам чараларды тез өзгөрүп жаткан кырдаалга ылайык адаптациялоого мүмкүндүк берди.

Авторлор жөнүндө

Дооронбекова Aйжан Жакыпбековна, соискатель, научный сотрудник, Центра анализа, управления рисками общественному здравоохранению и профилактики заболеваний Национального института общественного здоровья МЗ, Бишкек, Кыргызская Республика

Dooronbekova Aizhan Zhakypbekovna, applicant, research fellow of the Center for Analysis and Management of Public Health Risks National Institute of Public Health of the Ministry of Health, Bishkek, Kyrgyz Republic

Дооронбекова Айжан Жакыпбековна, талапкер, Саламаттыкты сактоо министрлигинин Коомдук саламаттыкты сактоо боюнча улуттук институтунун Коомдук саламаттыкты сактоо тобокелдиктерин талдоо жана башкаруу борборунун илимий кызматкери, Бишкек, Кыргыз Республикасы

Шилтемелер

1. Kermack WO, McKendrick AG. A contribution to the mathematical theory of epidemics. Proc R Soc A. 1927;115(772):700-721. https://doi.org/10.1098/rspa.1927.0118

2. Anderson RM, May RM. Infectious Diseases of Humans: Dynamics and Control. Oxford: Oxford University Press; 1991.

3. Hethcote HW. The mathematics of infectious diseases. SIAM Rev. 2000;42(4):599-653. https://doi.org/10.1137/S0036144500371907

4. Diekmann O, Heesterbeek JAP, Roberts MG. The construction of next-generation matrices for compartmental epidemic models. J R Soc Interface. 2010;7(47):873-885. https://doi.org/10.1098/rsif.2009.0386

5. Moore S, Hill EM, Tildesley MJ, Dyson L, Keeling MJ. Vaccination and non-pharmaceutical interventions for COVID-19: a mathematical modelling study. Lancet Infect Dis. 2021;21(6):793-802. https://doi.org/10.1016/S1473-3099(21)00143-2

6. Prem K, Liu Y, Russell TW, et al. The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China:a modelling study. Lancet Public Health. 2020;5(5):e261-e270.https://doi.org/10.1016/S2468-2667(20)30073-6

7. Aguas R, Hupert N, Shretta R, et al. COVID-19 pandemic modelling in context: uniting people and technology across nations. BMJ Preprint. 2020. https://www.researchgate.net/publication/342747645

8. CoMo Consortium. COVID-19 Modelling for Resource-Constrained Settings. Oxford: University of Oxford; 2021. https://www.co momodel.net/

9. World Health Organization. Considerations for implementing and adjusting public health and social measures. Geneva: WHO; 2021. https://apps.who.int/iris/handle/10665/337575

10. World Health Organization. COVID-19 Strategic Preparedness and Response Plan. Geneva: WHO; 2021. https://www.who.int/pub lications/i/item/WHO-WHE-2021.02 11. UNICEF. COVID-19 Vaccine Delivery Partnership: Central Asia Case Studies. Final Report. New York: UNICEF; May 2022. https://www.unicef.org/kyrgyzstan/media/8711/file

12. Hunter E, Mac Namee B, Kelleher JD. A comparison of agent-based models and equation based models for infectious disease epi demiology. Complex Syst. 2018;27(6):519-559.

13. Wangping J, Ke H, Yang S, et al. Extended SIR prediction of the epidemics trend of COVID-19 in Italy and compared with Hunan, China. Front Med. 2020;7:169. https://doi.org/10.3389/fmed.2020.00169

14. Cooper I, Mondal A, Antonopoulos CG. A SIR model assumption for the spread of COVID-19 in different communities. Chaos Solitons Fractals. 2020;139:110057. https://doi.org/10.1016/j.chaos.2020.110057

15. Ndaïrou F, Area I, Nieto JJ, Torres DFM. Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan. Chaos Solitons Fractals. 2020;135:109846. https://doi.org/10.1016/j.chaos.2020.109846

16. Kissler SM, Tedijanto C, Goldstein E, Grad YH, Lipsitch M. Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science. 2020;368(6493):860-868. https://doi.org/10.1126/science.abb5793

17. Giordano G, Blanchini F, Bruno R, et al. Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy. Nat Med. 2020;26(6):855-860. https://doi.org/10.1038/s41591-020-0883-7

18. Radev ST, Graw F, West R, Rieger MO, Lasser J. OutbreakFlow: model-based Bayesian inference of disease outbreaks. PLoS Comput Biol. 2021;17(5):e1008854. https://doi.org/10.1371/journal.pcbi.1008854

19. Ceylan Z. Estimation of COVID-19 prevalence in Italy, Spain, and France. Sci Total Environ. 2020;729:138817. https://doi.org/10.1016/j.scitotenv.2020.138817

20. Ajelli M, Gonçalves B, Balcan D, et al. Comparing large-scale computational approaches to epidemic modeling: agent-based versus structured metapopulation models. BMC Infect Dis. 2010;10:190. https://doi.org/10.1186/1471-2334-10-190

21. Zhang X, Ma R, Wang L. Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries. Chaos Solitons Fractals. 2020;135:109829. https://doi.org/10.1016/j.chaos.2020.109829

22. Alzahrani SI, Aljamaan IA, Al-Fakih EA. Forecasting COVID-19 pandemic in Saudi Arabia using ARIMA model. J Infect Public Health. 2020;13(7):920-925. https://doi.org/10.1016/j.jiph.2020.06.001

23. Arifi F, Moldokmatova A, Aitmatov K, et al. Application of the CoMo model in the Kyrgyz Republic: scenario-based planning for COVID-19 response. CoMo Consortium Report. Oxford: University of Oxford; 2021.

24. Elsawah S, Spencer T, Ryan MJ, et al. A review of computational model robustness evaluation strategies. Int J Environ Res Public Health. 2020;17(18):6280. https://doi.org/10.3390/ijerph17186280

25. Cramer EY, Huang Y, Wang Y, et al. The United States COVID-19 Forecast Hub dataset. J Am Med Inform Assoc. 2022;29(8): 1490-1499. https://doi.org/10.1093/jamia/ocac066

26. Taylor SJ, Letham B. Forecasting at scale. Am Stat. 2018;72(1):37-45. https://doi.org/10.1080/00031305.2017.1380080

27. Petropoulos F, Makridakis S. Forecasting the novel coronavirus COVID-19. PLoS One. 2020;15(3):e0231236. https://doi.org/10.1371/journal.pone.0231236

28. Hassani H, Silva ES, Gupta R. Forecasting the COVID-19 pandemic with singular spectrum analysis. J Data Sci. 2020;18(3):531- 544. https://doi.org/10.6339/JDS.202007_18(3).00003

29. Rustam F, Reshi AA, Mehmood A, et al. COVID-19 future forecasting using supervised machine learning models. IEEE Access. 2020;8:101489-101499. https://doi.org/10.1109/ACCESS.2020.2997311

30. Aboluwarin OA, Falade OO. COVID-19 forecasting using deep learning models. Sci Afr. 2022;16:e01163. https://doi.org/10.101 6/j.sciaf.2022.e01163

31. Singh RK, Rani M, Bhagavathula AS, et al. Prediction of the COVID-19 pandemic for the top 15 affected countries: advanced autoregressive integrated moving average (ARIMA) model. JMIR Public Health Surveill. 2020;6(2):e19115. https://doi.org/ 10.2196/19115

32. Hellewell J, Abbott S, Gimma A, et al. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. Lancet Glob Health. 2020;8(4):e488-e496. https://doi.org/10.1016/S2214-109X(20)30074-7

33. Ferguson NM, Laydon D, Nedjati-Gilani G, et al. Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mor tality and healthcare demand. Imperial College COVID-19 Response Team. 2020. https://doi.org/10.25561/77482

34. Dooronbekova AZh. Epidemiological analysis of COVID-19 spread in the Kyrgyz Republic using mathematical modeling and forecasting methods [dissertation]. Bishkek; 2025. 120 p.

1. Kermack WO, McKendrick AG. A contribution to the mathematical theory of epidemics. Proc R Soc A. 1927;115(772):700-721. https://doi.org/10.1098/rspa.1927.0118

2. Anderson RM, May RM. Infectious Diseases of Humans: Dynamics and Control. Oxford: Oxford University Press; 1991.

3. Hethcote HW. The mathematics of infectious diseases. SIAM Rev. 2000;42(4):599-653. https://doi.org/10.1137/S0036144500371907

4. Diekmann O, Heesterbeek JAP, Roberts MG. The construction of next-generation matrices for compartmental epidemic models. J R Soc Interface. 2010;7(47):873-885. https://doi.org/10.1098/rsif.2009.0386

5. Moore S, Hill EM, Tildesley MJ, Dyson L, Keeling MJ. Vaccination and non-pharmaceutical interventions for COVID-19: a mathematical modelling study. Lancet Infect Dis. 2021;21(6):793-802. https://doi.org/10.1016/S1473-3099(21)00143-2

6. Prem K, Liu Y, Russell TW, et al. The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China:a modelling study. Lancet Public Health. 2020;5(5):e261-e270.https://doi.org/10.1016/S2468-2667(20)30073-6

7. Aguas R, Hupert N, Shretta R, et al. COVID-19 pandemic modelling in context: uniting people and technology across nations. BMJ Preprint. 2020. https://www.researchgate.net/publication/342747645

8. CoMo Consortium. COVID-19 Modelling for Resource-Constrained Settings. Oxford: University of Oxford; 2021. https://www.co momodel.net/

9. World Health Organization. Considerations for implementing and adjusting public health and social measures. Geneva: WHO; 2021. https://apps.who.int/iris/handle/10665/337575

10. World Health Organization. COVID-19 Strategic Preparedness and Response Plan. Geneva: WHO; 2021. https://www.who.int/pub lications/i/item/WHO-WHE-2021.02 11. UNICEF. COVID-19 Vaccine Delivery Partnership: Central Asia Case Studies. Final Report. New York: UNICEF; May 2022. https://www.unicef.org/kyrgyzstan/media/8711/file

12. Hunter E, Mac Namee B, Kelleher JD. A comparison of agent-based models and equation based models for infectious disease epi demiology. Complex Syst. 2018;27(6):519-559.

13. Wangping J, Ke H, Yang S, et al. Extended SIR prediction of the epidemics trend of COVID-19 in Italy and compared with Hunan, China. Front Med. 2020;7:169. https://doi.org/10.3389/fmed.2020.00169

14. Cooper I, Mondal A, Antonopoulos CG. A SIR model assumption for the spread of COVID-19 in different communities. Chaos Solitons Fractals. 2020;139:110057. https://doi.org/10.1016/j.chaos.2020.110057

15. Ndaïrou F, Area I, Nieto JJ, Torres DFM. Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan. Chaos Solitons Fractals. 2020;135:109846. https://doi.org/10.1016/j.chaos.2020.109846

16. Kissler SM, Tedijanto C, Goldstein E, Grad YH, Lipsitch M. Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science. 2020;368(6493):860-868. https://doi.org/10.1126/science.abb5793

17. Giordano G, Blanchini F, Bruno R, et al. Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy. Nat Med. 2020;26(6):855-860. https://doi.org/10.1038/s41591-020-0883-7

18. Radev ST, Graw F, West R, Rieger MO, Lasser J. OutbreakFlow: model-based Bayesian inference of disease outbreaks. PLoS Comput Biol. 2021;17(5):e1008854. https://doi.org/10.1371/journal.pcbi.1008854

19. Ceylan Z. Estimation of COVID-19 prevalence in Italy, Spain, and France. Sci Total Environ. 2020;729:138817. https://doi.org/10.1016/j.scitotenv.2020.138817

20. Ajelli M, Gonçalves B, Balcan D, et al. Comparing large-scale computational approaches to epidemic modeling: agent-based versus structured metapopulation models. BMC Infect Dis. 2010;10:190. https://doi.org/10.1186/1471-2334-10-190

21. Zhang X, Ma R, Wang L. Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries. Chaos Solitons Fractals. 2020;135:109829. https://doi.org/10.1016/j.chaos.2020.109829

22. Alzahrani SI, Aljamaan IA, Al-Fakih EA. Forecasting COVID-19 pandemic in Saudi Arabia using ARIMA model. J Infect Public Health. 2020;13(7):920-925. https://doi.org/10.1016/j.jiph.2020.06.001

23. Arifi F, Moldokmatova A, Aitmatov K, et al. Application of the CoMo model in the Kyrgyz Republic: scenario-based planning for COVID-19 response. CoMo Consortium Report. Oxford: University of Oxford; 2021.

24. Elsawah S, Spencer T, Ryan MJ, et al. A review of computational model robustness evaluation strategies. Int J Environ Res Public Health. 2020;17(18):6280. https://doi.org/10.3390/ijerph17186280

25. Cramer EY, Huang Y, Wang Y, et al. The United States COVID-19 Forecast Hub dataset. J Am Med Inform Assoc. 2022;29(8): 1490-1499. https://doi.org/10.1093/jamia/ocac066

26. Taylor SJ, Letham B. Forecasting at scale. Am Stat. 2018;72(1):37-45. https://doi.org/10.1080/00031305.2017.1380080

27. Petropoulos F, Makridakis S. Forecasting the novel coronavirus COVID-19. PLoS One. 2020;15(3):e0231236. https://doi.org/10.1371/journal.pone.0231236

28. Hassani H, Silva ES, Gupta R. Forecasting the COVID-19 pandemic with singular spectrum analysis. J Data Sci. 2020;18(3):531- 544. https://doi.org/10.6339/JDS.202007_18(3).00003

29. Rustam F, Reshi AA, Mehmood A, et al. COVID-19 future forecasting using supervised machine learning models. IEEE Access. 2020;8:101489-101499. https://doi.org/10.1109/ACCESS.2020.2997311

30. Aboluwarin OA, Falade OO. COVID-19 forecasting using deep learning models. Sci Afr. 2022;16:e01163. https://doi.org/10.101 6/j.sciaf.2022.e01163

31. Singh RK, Rani M, Bhagavathula AS, et al. Prediction of the COVID-19 pandemic for the top 15 affected countries: advanced autoregressive integrated moving average (ARIMA) model. JMIR Public Health Surveill. 2020;6(2):e19115. https://doi.org/ 10.2196/19115

32. Hellewell J, Abbott S, Gimma A, et al. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. Lancet Glob Health. 2020;8(4):e488-e496. https://doi.org/10.1016/S2214-109X(20)30074-7

33. Ferguson NM, Laydon D, Nedjati-Gilani G, et al. Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mor tality and healthcare demand. Imperial College COVID-19 Response Team. 2020. https://doi.org/10.25561/77482

34. Dooronbekova AZh. Epidemiological analysis of COVID-19 spread in the Kyrgyz Republic using mathematical modeling and forecasting methods [dissertation]. Bishkek; 2025. 120 p.

1. Kermack WO, McKendrick AG. A contribution to the mathematical theory of epidemics. Proc R Soc A. 1927;115(772):700-721. https://doi.org/10.1098/rspa.1927.0118

2. Anderson RM, May RM. Infectious Diseases of Humans: Dynamics and Control. Oxford: Oxford University Press; 1991.

3. Hethcote HW. The mathematics of infectious diseases. SIAM Rev. 2000;42(4):599-653. https://doi.org/10.1137/S0036144500371907

4. Diekmann O, Heesterbeek JAP, Roberts MG. The construction of next-generation matrices for compartmental epidemic models. J R Soc Interface. 2010;7(47):873-885. https://doi.org/10.1098/rsif.2009.0386

5. Moore S, Hill EM, Tildesley MJ, Dyson L, Keeling MJ. Vaccination and non-pharmaceutical interventions for COVID-19: a mathematical modelling study. Lancet Infect Dis. 2021;21(6):793-802. https://doi.org/10.1016/S1473-3099(21)00143-2

6. Prem K, Liu Y, Russell TW, et al. The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China:a modelling study. Lancet Public Health. 2020;5(5):e261-e270.https://doi.org/10.1016/S2468-2667(20)30073-6

7. Aguas R, Hupert N, Shretta R, et al. COVID-19 pandemic modelling in context: uniting people and technology across nations. BMJ Preprint. 2020. https://www.researchgate.net/publication/342747645

8. CoMo Consortium. COVID-19 Modelling for Resource-Constrained Settings. Oxford: University of Oxford; 2021. https://www.co momodel.net/

9. World Health Organization. Considerations for implementing and adjusting public health and social measures. Geneva: WHO; 2021. https://apps.who.int/iris/handle/10665/337575

10. World Health Organization. COVID-19 Strategic Preparedness and Response Plan. Geneva: WHO; 2021. https://www.who.int/pub lications/i/item/WHO-WHE-2021.02 11. UNICEF. COVID-19 Vaccine Delivery Partnership: Central Asia Case Studies. Final Report. New York: UNICEF; May 2022. https://www.unicef.org/kyrgyzstan/media/8711/file

12. Hunter E, Mac Namee B, Kelleher JD. A comparison of agent-based models and equation based models for infectious disease epi demiology. Complex Syst. 2018;27(6):519-559.

13. Wangping J, Ke H, Yang S, et al. Extended SIR prediction of the epidemics trend of COVID-19 in Italy and compared with Hunan, China. Front Med. 2020;7:169. https://doi.org/10.3389/fmed.2020.00169

14. Cooper I, Mondal A, Antonopoulos CG. A SIR model assumption for the spread of COVID-19 in different communities. Chaos Solitons Fractals. 2020;139:110057. https://doi.org/10.1016/j.chaos.2020.110057

15. Ndaïrou F, Area I, Nieto JJ, Torres DFM. Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan. Chaos Solitons Fractals. 2020;135:109846. https://doi.org/10.1016/j.chaos.2020.109846

16. Kissler SM, Tedijanto C, Goldstein E, Grad YH, Lipsitch M. Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science. 2020;368(6493):860-868. https://doi.org/10.1126/science.abb5793

17. Giordano G, Blanchini F, Bruno R, et al. Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy. Nat Med. 2020;26(6):855-860. https://doi.org/10.1038/s41591-020-0883-7

18. Radev ST, Graw F, West R, Rieger MO, Lasser J. OutbreakFlow: model-based Bayesian inference of disease outbreaks. PLoS Comput Biol. 2021;17(5):e1008854. https://doi.org/10.1371/journal.pcbi.1008854

19. Ceylan Z. Estimation of COVID-19 prevalence in Italy, Spain, and France. Sci Total Environ. 2020;729:138817. https://doi.org/10.1016/j.scitotenv.2020.138817

20. Ajelli M, Gonçalves B, Balcan D, et al. Comparing large-scale computational approaches to epidemic modeling: agent-based versus structured metapopulation models. BMC Infect Dis. 2010;10:190. https://doi.org/10.1186/1471-2334-10-190

21. Zhang X, Ma R, Wang L. Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries. Chaos Solitons Fractals. 2020;135:109829. https://doi.org/10.1016/j.chaos.2020.109829

22. Alzahrani SI, Aljamaan IA, Al-Fakih EA. Forecasting COVID-19 pandemic in Saudi Arabia using ARIMA model. J Infect Public Health. 2020;13(7):920-925. https://doi.org/10.1016/j.jiph.2020.06.001

23. Arifi F, Moldokmatova A, Aitmatov K, et al. Application of the CoMo model in the Kyrgyz Republic: scenario-based planning for COVID-19 response. CoMo Consortium Report. Oxford: University of Oxford; 2021.

24. Elsawah S, Spencer T, Ryan MJ, et al. A review of computational model robustness evaluation strategies. Int J Environ Res Public Health. 2020;17(18):6280. https://doi.org/10.3390/ijerph17186280

25. Cramer EY, Huang Y, Wang Y, et al. The United States COVID-19 Forecast Hub dataset. J Am Med Inform Assoc. 2022;29(8): 1490-1499. https://doi.org/10.1093/jamia/ocac066

26. Taylor SJ, Letham B. Forecasting at scale. Am Stat. 2018;72(1):37-45. https://doi.org/10.1080/00031305.2017.1380080

27. Petropoulos F, Makridakis S. Forecasting the novel coronavirus COVID-19. PLoS One. 2020;15(3):e0231236. https://doi.org/10.1371/journal.pone.0231236

28. Hassani H, Silva ES, Gupta R. Forecasting the COVID-19 pandemic with singular spectrum analysis. J Data Sci. 2020;18(3):531- 544. https://doi.org/10.6339/JDS.202007_18(3).00003

29. Rustam F, Reshi AA, Mehmood A, et al. COVID-19 future forecasting using supervised machine learning models. IEEE Access. 2020;8:101489-101499. https://doi.org/10.1109/ACCESS.2020.2997311

30. Aboluwarin OA, Falade OO. COVID-19 forecasting using deep learning models. Sci Afr. 2022;16:e01163. https://doi.org/10.101 6/j.sciaf.2022.e01163

31. Singh RK, Rani M, Bhagavathula AS, et al. Prediction of the COVID-19 pandemic for the top 15 affected countries: advanced autoregressive integrated moving average (ARIMA) model. JMIR Public Health Surveill. 2020;6(2):e19115. https://doi.org/ 10.2196/19115

32. Hellewell J, Abbott S, Gimma A, et al. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. Lancet Glob Health. 2020;8(4):e488-e496. https://doi.org/10.1016/S2214-109X(20)30074-7

33. Ferguson NM, Laydon D, Nedjati-Gilani G, et al. Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mor tality and healthcare demand. Imperial College COVID-19 Response Team. 2020. https://doi.org/10.25561/77482

34. Dooronbekova AZh. Epidemiological analysis of COVID-19 spread in the Kyrgyz Republic using mathematical modeling and forecasting methods [dissertation]. Bishkek; 2025. 120 p.

Негизги сөздөр
Сценарий , COVID-19 , Кыргызстан , ARIMA , Математикалык моделдөө , SEIR , Божомол
Для цитирования

Дооронбекова А.Ж. Математическое моделирование эпидемии COVID-19: методы, сценарии и практическая применимость. Научнопрактический журнал «Здравоохранение Кыргызстана» 2026, № 1, с. 148-153.  https://dx.doi.org/10.51350/zdravkg2026.1.3.18.148.153
For citation

Dooronbekova A. Zh. Mathematical Modeling of the COVID -19 Epidemic: Methods, Scenarios, and Practical Applica bility. Scientific practical journal “Health care of Kyrgyzstan” 2026, No.1, p. 148-153.  https://dx.doi.org/10.51350/zdravkg2026.1.3.18.148.153
Цитата үчүн

Дооронбекова А.Ж. COVID-19 эпидемиясынын математикалык моделдөө ыкмалары: методдор, сценарийлер жана практикалык колдонулушу. Кыргызстандын саламаттык сактоо илимий-практикалык журналы 2026, № 1, б. 148-153. https://dx.doi.org/10.51350/zdravkg2026.1.3.18.148.153
Авторлор Дооронбекова А.Ж.
Ссылка doi.org https://dx.doi.org/10.51350/zdravkg2026.1.3.18.148.153
Беттер 148-153
Негизги сөздөр Сценарий, COVID-19, Кыргызстан, ARIMA, Математикалык моделдөө, SEIR, Божомол
Орусча
Об авторах

Дооронбекова Aйжан Жакыпбековна, соискатель, научный сотрудник, Центра анализа, управления рисками общественному здравоохранению и профилактики заболеваний Национального института общественного здоровья МЗ, Бишкек, Кыргызская Республика

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Список литературы

1. Kermack WO, McKendrick AG. A contribution to the mathematical theory of epidemics. Proc R Soc A. 1927;115(772):700-721. https://doi.org/10.1098/rspa.1927.0118

2. Anderson RM, May RM. Infectious Diseases of Humans: Dynamics and Control. Oxford: Oxford University Press; 1991.

3. Hethcote HW. The mathematics of infectious diseases. SIAM Rev. 2000;42(4):599-653. https://doi.org/10.1137/S0036144500371907

4. Diekmann O, Heesterbeek JAP, Roberts MG. The construction of next-generation matrices for compartmental epidemic models. J R Soc Interface. 2010;7(47):873-885. https://doi.org/10.1098/rsif.2009.0386

5. Moore S, Hill EM, Tildesley MJ, Dyson L, Keeling MJ. Vaccination and non-pharmaceutical interventions for COVID-19: a mathematical modelling study. Lancet Infect Dis. 2021;21(6):793-802. https://doi.org/10.1016/S1473-3099(21)00143-2

6. Prem K, Liu Y, Russell TW, et al. The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China:a modelling study. Lancet Public Health. 2020;5(5):e261-e270.https://doi.org/10.1016/S2468-2667(20)30073-6

7. Aguas R, Hupert N, Shretta R, et al. COVID-19 pandemic modelling in context: uniting people and technology across nations. BMJ Preprint. 2020. https://www.researchgate.net/publication/342747645

8. CoMo Consortium. COVID-19 Modelling for Resource-Constrained Settings. Oxford: University of Oxford; 2021. https://www.co momodel.net/

9. World Health Organization. Considerations for implementing and adjusting public health and social measures. Geneva: WHO; 2021. https://apps.who.int/iris/handle/10665/337575

10. World Health Organization. COVID-19 Strategic Preparedness and Response Plan. Geneva: WHO; 2021. https://www.who.int/pub lications/i/item/WHO-WHE-2021.02 11. UNICEF. COVID-19 Vaccine Delivery Partnership: Central Asia Case Studies. Final Report. New York: UNICEF; May 2022. https://www.unicef.org/kyrgyzstan/media/8711/file

12. Hunter E, Mac Namee B, Kelleher JD. A comparison of agent-based models and equation based models for infectious disease epi demiology. Complex Syst. 2018;27(6):519-559.

13. Wangping J, Ke H, Yang S, et al. Extended SIR prediction of the epidemics trend of COVID-19 in Italy and compared with Hunan, China. Front Med. 2020;7:169. https://doi.org/10.3389/fmed.2020.00169

14. Cooper I, Mondal A, Antonopoulos CG. A SIR model assumption for the spread of COVID-19 in different communities. Chaos Solitons Fractals. 2020;139:110057. https://doi.org/10.1016/j.chaos.2020.110057

15. Ndaïrou F, Area I, Nieto JJ, Torres DFM. Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan. Chaos Solitons Fractals. 2020;135:109846. https://doi.org/10.1016/j.chaos.2020.109846

16. Kissler SM, Tedijanto C, Goldstein E, Grad YH, Lipsitch M. Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science. 2020;368(6493):860-868. https://doi.org/10.1126/science.abb5793

17. Giordano G, Blanchini F, Bruno R, et al. Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy. Nat Med. 2020;26(6):855-860. https://doi.org/10.1038/s41591-020-0883-7

18. Radev ST, Graw F, West R, Rieger MO, Lasser J. OutbreakFlow: model-based Bayesian inference of disease outbreaks. PLoS Comput Biol. 2021;17(5):e1008854. https://doi.org/10.1371/journal.pcbi.1008854

19. Ceylan Z. Estimation of COVID-19 prevalence in Italy, Spain, and France. Sci Total Environ. 2020;729:138817. https://doi.org/10.1016/j.scitotenv.2020.138817

20. Ajelli M, Gonçalves B, Balcan D, et al. Comparing large-scale computational approaches to epidemic modeling: agent-based versus structured metapopulation models. BMC Infect Dis. 2010;10:190. https://doi.org/10.1186/1471-2334-10-190

21. Zhang X, Ma R, Wang L. Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries. Chaos Solitons Fractals. 2020;135:109829. https://doi.org/10.1016/j.chaos.2020.109829

22. Alzahrani SI, Aljamaan IA, Al-Fakih EA. Forecasting COVID-19 pandemic in Saudi Arabia using ARIMA model. J Infect Public Health. 2020;13(7):920-925. https://doi.org/10.1016/j.jiph.2020.06.001

23. Arifi F, Moldokmatova A, Aitmatov K, et al. Application of the CoMo model in the Kyrgyz Republic: scenario-based planning for COVID-19 response. CoMo Consortium Report. Oxford: University of Oxford; 2021.

24. Elsawah S, Spencer T, Ryan MJ, et al. A review of computational model robustness evaluation strategies. Int J Environ Res Public Health. 2020;17(18):6280. https://doi.org/10.3390/ijerph17186280

25. Cramer EY, Huang Y, Wang Y, et al. The United States COVID-19 Forecast Hub dataset. J Am Med Inform Assoc. 2022;29(8): 1490-1499. https://doi.org/10.1093/jamia/ocac066

26. Taylor SJ, Letham B. Forecasting at scale. Am Stat. 2018;72(1):37-45. https://doi.org/10.1080/00031305.2017.1380080

27. Petropoulos F, Makridakis S. Forecasting the novel coronavirus COVID-19. PLoS One. 2020;15(3):e0231236. https://doi.org/10.1371/journal.pone.0231236

28. Hassani H, Silva ES, Gupta R. Forecasting the COVID-19 pandemic with singular spectrum analysis. J Data Sci. 2020;18(3):531- 544. https://doi.org/10.6339/JDS.202007_18(3).00003

29. Rustam F, Reshi AA, Mehmood A, et al. COVID-19 future forecasting using supervised machine learning models. IEEE Access. 2020;8:101489-101499. https://doi.org/10.1109/ACCESS.2020.2997311

30. Aboluwarin OA, Falade OO. COVID-19 forecasting using deep learning models. Sci Afr. 2022;16:e01163. https://doi.org/10.101 6/j.sciaf.2022.e01163

31. Singh RK, Rani M, Bhagavathula AS, et al. Prediction of the COVID-19 pandemic for the top 15 affected countries: advanced autoregressive integrated moving average (ARIMA) model. JMIR Public Health Surveill. 2020;6(2):e19115. https://doi.org/ 10.2196/19115

32. Hellewell J, Abbott S, Gimma A, et al. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. Lancet Glob Health. 2020;8(4):e488-e496. https://doi.org/10.1016/S2214-109X(20)30074-7

33. Ferguson NM, Laydon D, Nedjati-Gilani G, et al. Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mor tality and healthcare demand. Imperial College COVID-19 Response Team. 2020. https://doi.org/10.25561/77482

34. Dooronbekova AZh. Epidemiological analysis of COVID-19 spread in the Kyrgyz Republic using mathematical modeling and forecasting methods [dissertation]. Bishkek; 2025. 120 p.
Для цитирования

Дооронбекова А.Ж. Математическое моделирование эпидемии COVID-19: методы, сценарии и практическая применимость. Научнопрактический журнал «Здравоохранение Кыргызстана» 2026, № 1, с. 148-153.  https://dx.doi.org/10.51350/zdravkg2026.1.3.18.148.153
Англисче
About authors

Dooronbekova Aizhan Zhakypbekovna, applicant, research fellow of the Center for Analysis and Management of Public Health Risks National Institute of Public Health of the Ministry of Health, Bishkek, Kyrgyz Republic

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References

1. Kermack WO, McKendrick AG. A contribution to the mathematical theory of epidemics. Proc R Soc A. 1927;115(772):700-721. https://doi.org/10.1098/rspa.1927.0118

2. Anderson RM, May RM. Infectious Diseases of Humans: Dynamics and Control. Oxford: Oxford University Press; 1991.

3. Hethcote HW. The mathematics of infectious diseases. SIAM Rev. 2000;42(4):599-653. https://doi.org/10.1137/S0036144500371907

4. Diekmann O, Heesterbeek JAP, Roberts MG. The construction of next-generation matrices for compartmental epidemic models. J R Soc Interface. 2010;7(47):873-885. https://doi.org/10.1098/rsif.2009.0386

5. Moore S, Hill EM, Tildesley MJ, Dyson L, Keeling MJ. Vaccination and non-pharmaceutical interventions for COVID-19: a mathematical modelling study. Lancet Infect Dis. 2021;21(6):793-802. https://doi.org/10.1016/S1473-3099(21)00143-2

6. Prem K, Liu Y, Russell TW, et al. The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China:a modelling study. Lancet Public Health. 2020;5(5):e261-e270.https://doi.org/10.1016/S2468-2667(20)30073-6

7. Aguas R, Hupert N, Shretta R, et al. COVID-19 pandemic modelling in context: uniting people and technology across nations. BMJ Preprint. 2020. https://www.researchgate.net/publication/342747645

8. CoMo Consortium. COVID-19 Modelling for Resource-Constrained Settings. Oxford: University of Oxford; 2021. https://www.co momodel.net/

9. World Health Organization. Considerations for implementing and adjusting public health and social measures. Geneva: WHO; 2021. https://apps.who.int/iris/handle/10665/337575

10. World Health Organization. COVID-19 Strategic Preparedness and Response Plan. Geneva: WHO; 2021. https://www.who.int/pub lications/i/item/WHO-WHE-2021.02 11. UNICEF. COVID-19 Vaccine Delivery Partnership: Central Asia Case Studies. Final Report. New York: UNICEF; May 2022. https://www.unicef.org/kyrgyzstan/media/8711/file

12. Hunter E, Mac Namee B, Kelleher JD. A comparison of agent-based models and equation based models for infectious disease epi demiology. Complex Syst. 2018;27(6):519-559.

13. Wangping J, Ke H, Yang S, et al. Extended SIR prediction of the epidemics trend of COVID-19 in Italy and compared with Hunan, China. Front Med. 2020;7:169. https://doi.org/10.3389/fmed.2020.00169

14. Cooper I, Mondal A, Antonopoulos CG. A SIR model assumption for the spread of COVID-19 in different communities. Chaos Solitons Fractals. 2020;139:110057. https://doi.org/10.1016/j.chaos.2020.110057

15. Ndaïrou F, Area I, Nieto JJ, Torres DFM. Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan. Chaos Solitons Fractals. 2020;135:109846. https://doi.org/10.1016/j.chaos.2020.109846

16. Kissler SM, Tedijanto C, Goldstein E, Grad YH, Lipsitch M. Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science. 2020;368(6493):860-868. https://doi.org/10.1126/science.abb5793

17. Giordano G, Blanchini F, Bruno R, et al. Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy. Nat Med. 2020;26(6):855-860. https://doi.org/10.1038/s41591-020-0883-7

18. Radev ST, Graw F, West R, Rieger MO, Lasser J. OutbreakFlow: model-based Bayesian inference of disease outbreaks. PLoS Comput Biol. 2021;17(5):e1008854. https://doi.org/10.1371/journal.pcbi.1008854

19. Ceylan Z. Estimation of COVID-19 prevalence in Italy, Spain, and France. Sci Total Environ. 2020;729:138817. https://doi.org/10.1016/j.scitotenv.2020.138817

20. Ajelli M, Gonçalves B, Balcan D, et al. Comparing large-scale computational approaches to epidemic modeling: agent-based versus structured metapopulation models. BMC Infect Dis. 2010;10:190. https://doi.org/10.1186/1471-2334-10-190

21. Zhang X, Ma R, Wang L. Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries. Chaos Solitons Fractals. 2020;135:109829. https://doi.org/10.1016/j.chaos.2020.109829

22. Alzahrani SI, Aljamaan IA, Al-Fakih EA. Forecasting COVID-19 pandemic in Saudi Arabia using ARIMA model. J Infect Public Health. 2020;13(7):920-925. https://doi.org/10.1016/j.jiph.2020.06.001

23. Arifi F, Moldokmatova A, Aitmatov K, et al. Application of the CoMo model in the Kyrgyz Republic: scenario-based planning for COVID-19 response. CoMo Consortium Report. Oxford: University of Oxford; 2021.

24. Elsawah S, Spencer T, Ryan MJ, et al. A review of computational model robustness evaluation strategies. Int J Environ Res Public Health. 2020;17(18):6280. https://doi.org/10.3390/ijerph17186280

25. Cramer EY, Huang Y, Wang Y, et al. The United States COVID-19 Forecast Hub dataset. J Am Med Inform Assoc. 2022;29(8): 1490-1499. https://doi.org/10.1093/jamia/ocac066

26. Taylor SJ, Letham B. Forecasting at scale. Am Stat. 2018;72(1):37-45. https://doi.org/10.1080/00031305.2017.1380080

27. Petropoulos F, Makridakis S. Forecasting the novel coronavirus COVID-19. PLoS One. 2020;15(3):e0231236. https://doi.org/10.1371/journal.pone.0231236

28. Hassani H, Silva ES, Gupta R. Forecasting the COVID-19 pandemic with singular spectrum analysis. J Data Sci. 2020;18(3):531- 544. https://doi.org/10.6339/JDS.202007_18(3).00003

29. Rustam F, Reshi AA, Mehmood A, et al. COVID-19 future forecasting using supervised machine learning models. IEEE Access. 2020;8:101489-101499. https://doi.org/10.1109/ACCESS.2020.2997311

30. Aboluwarin OA, Falade OO. COVID-19 forecasting using deep learning models. Sci Afr. 2022;16:e01163. https://doi.org/10.101 6/j.sciaf.2022.e01163

31. Singh RK, Rani M, Bhagavathula AS, et al. Prediction of the COVID-19 pandemic for the top 15 affected countries: advanced autoregressive integrated moving average (ARIMA) model. JMIR Public Health Surveill. 2020;6(2):e19115. https://doi.org/ 10.2196/19115

32. Hellewell J, Abbott S, Gimma A, et al. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. Lancet Glob Health. 2020;8(4):e488-e496. https://doi.org/10.1016/S2214-109X(20)30074-7

33. Ferguson NM, Laydon D, Nedjati-Gilani G, et al. Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mor tality and healthcare demand. Imperial College COVID-19 Response Team. 2020. https://doi.org/10.25561/77482

34. Dooronbekova AZh. Epidemiological analysis of COVID-19 spread in the Kyrgyz Republic using mathematical modeling and forecasting methods [dissertation]. Bishkek; 2025. 120 p.
For citation

Dooronbekova A. Zh. Mathematical Modeling of the COVID -19 Epidemic: Methods, Scenarios, and Practical Applica bility. Scientific practical journal “Health care of Kyrgyzstan” 2026, No.1, p. 148-153.  https://dx.doi.org/10.51350/zdravkg2026.1.3.18.148.153
Кыргызча
Авторлор жөнүндө

Дооронбекова Айжан Жакыпбековна, талапкер, Саламаттыкты сактоо министрлигинин Коомдук саламаттыкты сактоо боюнча улуттук институтунун Коомдук саламаттыкты сактоо тобокелдиктерин талдоо жана башкаруу борборунун илимий кызматкери, Бишкек, Кыргыз Республикасы
Шилтемелер

1. Kermack WO, McKendrick AG. A contribution to the mathematical theory of epidemics. Proc R Soc A. 1927;115(772):700-721. https://doi.org/10.1098/rspa.1927.0118

2. Anderson RM, May RM. Infectious Diseases of Humans: Dynamics and Control. Oxford: Oxford University Press; 1991.

3. Hethcote HW. The mathematics of infectious diseases. SIAM Rev. 2000;42(4):599-653. https://doi.org/10.1137/S0036144500371907

4. Diekmann O, Heesterbeek JAP, Roberts MG. The construction of next-generation matrices for compartmental epidemic models. J R Soc Interface. 2010;7(47):873-885. https://doi.org/10.1098/rsif.2009.0386

5. Moore S, Hill EM, Tildesley MJ, Dyson L, Keeling MJ. Vaccination and non-pharmaceutical interventions for COVID-19: a mathematical modelling study. Lancet Infect Dis. 2021;21(6):793-802. https://doi.org/10.1016/S1473-3099(21)00143-2

6. Prem K, Liu Y, Russell TW, et al. The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China:a modelling study. Lancet Public Health. 2020;5(5):e261-e270.https://doi.org/10.1016/S2468-2667(20)30073-6

7. Aguas R, Hupert N, Shretta R, et al. COVID-19 pandemic modelling in context: uniting people and technology across nations. BMJ Preprint. 2020. https://www.researchgate.net/publication/342747645

8. CoMo Consortium. COVID-19 Modelling for Resource-Constrained Settings. Oxford: University of Oxford; 2021. https://www.co momodel.net/

9. World Health Organization. Considerations for implementing and adjusting public health and social measures. Geneva: WHO; 2021. https://apps.who.int/iris/handle/10665/337575

10. World Health Organization. COVID-19 Strategic Preparedness and Response Plan. Geneva: WHO; 2021. https://www.who.int/pub lications/i/item/WHO-WHE-2021.02 11. UNICEF. COVID-19 Vaccine Delivery Partnership: Central Asia Case Studies. Final Report. New York: UNICEF; May 2022. https://www.unicef.org/kyrgyzstan/media/8711/file

12. Hunter E, Mac Namee B, Kelleher JD. A comparison of agent-based models and equation based models for infectious disease epi demiology. Complex Syst. 2018;27(6):519-559.

13. Wangping J, Ke H, Yang S, et al. Extended SIR prediction of the epidemics trend of COVID-19 in Italy and compared with Hunan, China. Front Med. 2020;7:169. https://doi.org/10.3389/fmed.2020.00169

14. Cooper I, Mondal A, Antonopoulos CG. A SIR model assumption for the spread of COVID-19 in different communities. Chaos Solitons Fractals. 2020;139:110057. https://doi.org/10.1016/j.chaos.2020.110057

15. Ndaïrou F, Area I, Nieto JJ, Torres DFM. Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan. Chaos Solitons Fractals. 2020;135:109846. https://doi.org/10.1016/j.chaos.2020.109846

16. Kissler SM, Tedijanto C, Goldstein E, Grad YH, Lipsitch M. Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science. 2020;368(6493):860-868. https://doi.org/10.1126/science.abb5793

17. Giordano G, Blanchini F, Bruno R, et al. Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy. Nat Med. 2020;26(6):855-860. https://doi.org/10.1038/s41591-020-0883-7

18. Radev ST, Graw F, West R, Rieger MO, Lasser J. OutbreakFlow: model-based Bayesian inference of disease outbreaks. PLoS Comput Biol. 2021;17(5):e1008854. https://doi.org/10.1371/journal.pcbi.1008854

19. Ceylan Z. Estimation of COVID-19 prevalence in Italy, Spain, and France. Sci Total Environ. 2020;729:138817. https://doi.org/10.1016/j.scitotenv.2020.138817

20. Ajelli M, Gonçalves B, Balcan D, et al. Comparing large-scale computational approaches to epidemic modeling: agent-based versus structured metapopulation models. BMC Infect Dis. 2010;10:190. https://doi.org/10.1186/1471-2334-10-190

21. Zhang X, Ma R, Wang L. Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries. Chaos Solitons Fractals. 2020;135:109829. https://doi.org/10.1016/j.chaos.2020.109829

22. Alzahrani SI, Aljamaan IA, Al-Fakih EA. Forecasting COVID-19 pandemic in Saudi Arabia using ARIMA model. J Infect Public Health. 2020;13(7):920-925. https://doi.org/10.1016/j.jiph.2020.06.001

23. Arifi F, Moldokmatova A, Aitmatov K, et al. Application of the CoMo model in the Kyrgyz Republic: scenario-based planning for COVID-19 response. CoMo Consortium Report. Oxford: University of Oxford; 2021.

24. Elsawah S, Spencer T, Ryan MJ, et al. A review of computational model robustness evaluation strategies. Int J Environ Res Public Health. 2020;17(18):6280. https://doi.org/10.3390/ijerph17186280

25. Cramer EY, Huang Y, Wang Y, et al. The United States COVID-19 Forecast Hub dataset. J Am Med Inform Assoc. 2022;29(8): 1490-1499. https://doi.org/10.1093/jamia/ocac066

26. Taylor SJ, Letham B. Forecasting at scale. Am Stat. 2018;72(1):37-45. https://doi.org/10.1080/00031305.2017.1380080

27. Petropoulos F, Makridakis S. Forecasting the novel coronavirus COVID-19. PLoS One. 2020;15(3):e0231236. https://doi.org/10.1371/journal.pone.0231236

28. Hassani H, Silva ES, Gupta R. Forecasting the COVID-19 pandemic with singular spectrum analysis. J Data Sci. 2020;18(3):531- 544. https://doi.org/10.6339/JDS.202007_18(3).00003

29. Rustam F, Reshi AA, Mehmood A, et al. COVID-19 future forecasting using supervised machine learning models. IEEE Access. 2020;8:101489-101499. https://doi.org/10.1109/ACCESS.2020.2997311

30. Aboluwarin OA, Falade OO. COVID-19 forecasting using deep learning models. Sci Afr. 2022;16:e01163. https://doi.org/10.101 6/j.sciaf.2022.e01163

31. Singh RK, Rani M, Bhagavathula AS, et al. Prediction of the COVID-19 pandemic for the top 15 affected countries: advanced autoregressive integrated moving average (ARIMA) model. JMIR Public Health Surveill. 2020;6(2):e19115. https://doi.org/ 10.2196/19115

32. Hellewell J, Abbott S, Gimma A, et al. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. Lancet Glob Health. 2020;8(4):e488-e496. https://doi.org/10.1016/S2214-109X(20)30074-7

33. Ferguson NM, Laydon D, Nedjati-Gilani G, et al. Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mor tality and healthcare demand. Imperial College COVID-19 Response Team. 2020. https://doi.org/10.25561/77482

34. Dooronbekova AZh. Epidemiological analysis of COVID-19 spread in the Kyrgyz Republic using mathematical modeling and forecasting methods [dissertation]. Bishkek; 2025. 120 p.
Цитата үчүн

Дооронбекова А.Ж. COVID-19 эпидемиясынын математикалык моделдөө ыкмалары: методдор, сценарийлер жана практикалык колдонулушу. Кыргызстандын саламаттык сактоо илимий-практикалык журналы 2026, № 1, б. 148-153. https://dx.doi.org/10.51350/zdravkg2026.1.3.18.148.153
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