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.
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.