In the realm of COVID-19 transmission data, scientists are scrutinizing policies to identify the ideal vaccination rate for halting the virus. This study aimed to pinpoint the minimal vaccinated percentage needed to break the virus cycle within communities. The underlying motivation stems from the urgent need to contain COVID-19’s spread and reduce the strain on healthcare systems worldwide. With fluctuating infection rates and the emergence of new variants, understanding the optimal vaccination rate has become a cornerstone in public health planning and pandemic response. Using diverse machine learning methods, this study analyzed infection peaks and hospitalization rates during vaccination campaigns across countries. The goal was to find the vaccination threshold necessary to prevent virus resurgence, even with new variants. This critical milestone is crucial for health systems to combat the pandemic effectively. The study’s analysis revealed the correlation between vaccination rates and hospitalizations, highlighting immunization’s pivotal role. Employing Random Forest regression, the study successfully predicted new cases and hospitalization rates, offering valuable insights into pandemic management strategies. For future research, we recommend exploring the impact of vaccination on the evolution of virus variants and the potential influence of socio-economic factors on vaccination uptake. Moreover, a broader analysis across different geographical regions can further validate the study’s findings and enhance global pandemic preparedness.

COVID-19; vaccine; random forest regression; chain of infection; public health policies

1. Adetunji, C. O., Olaniyan, O. P., Adeyomoye, O., et al. (2022). Machine learning approaches for COVID-19 pandemic. In: Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis. Springer.
2. Ahamad, Md. M., Aktar, S., Rashed-Al-Mahfuz, Md., et al. (2020). A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients. Expert Systems with Applications, 160, 113661. https://doi.org/10.1016/j.eswa.2020.113661
3. Araújo, J. L. de, Oliveira, K. K. D. de, & Freitas, R. J. M. de. (2020). In defense of the Unified Health System in the context of SARS-CoV-2 pandemic. Revista Brasileira de Enfermagem, 73(suppl 2). https://doi.org/10.1590/0034-7167-2020-0247
4. Ardabili, S., Mosavi, A., Ghamisi, P., et al. (2020). COVID-19 Outbreak Prediction with Machine Learning. Algorithms, 13(10), 249. https://doi.org/10.3390/a13100249
5. Argirova, R., & Zlatareva, A. (2023). Lifelong vaccination model: for a better quality of life. Biotechnology & Biotechnological Equipment, 37(1), 24–33. https://doi.org/10.1080/13102818.2022.2151379
6. Arvind, K. S., Vanitha, S., & Suganya, K. S. (2023). Pandemic Management Using Internet of Things and Big Data – A Security and Privacy Perspective. IoT and Big Data Analytics for Smart Cities, 159–173. https://doi.org/10.1201/9781003217404-8
7. Bai, X., Fang, C., Zhou, Y., et al. (2020). Predicting COVID-19 Malignant Progression with AI Techniques. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3557984
8. Ballı, S. (2021). Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods. Chaos, Solitons & Fractals, 142, 110512. https://doi.org/10.1016/j.chaos.2020.110512
9. Bian, L., Gao, F., Zhang, J., et al. (2021). Effects of SARS-CoV-2 variants on vaccine efficacy and response strategies. Expert Review of Vaccines, 20(4), 365–373. https://doi.org/10.1080/14760584.2021.1903879
10. Buitrago-Garcia, D., Egli-Gany, D., Counotte, M. J., et al. (2020). Occurrence and transmission potential of asymptomatic and presymptomatic SARS-CoV-2 infections: A living systematic review and meta-analysis. PLOS Medicine, 17(9), e1003346. https://doi.org/10.1371/journal.pmed.1003346
11. Campos, D. M. de O., Fulco, U. L., de Oliveira, C. B. S., et al. (2020). SARS‐CoV‐2 virus infection: Targets and antiviral pharmacological strategies. Journal of Evidence-Based Medicine, 13(4), 255–260. Portico. https://doi.org/10.1111/jebm.12414
12. Cevik, M., Kuppalli, K., Kindrachuk, J., et al. (2020). Virology, transmission, and pathogenesis of SARS-CoV-2. BMJ, m3862. https://doi.org/10.1136/bmj.m3862
13. Cevik, M., Marcus, J. L., Buckee, C., et al. (2021). Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Transmission Dynamics Should Inform Policy. Clinical Infectious Diseases, 73(Supplement_2), S170–S176. https://doi.org/10.1093/cid/ciaa1442
14. Chasapis, C. T., Perlepes, S. P., Bjørklund, G., et al. (2023). Structural modeling of protein ensembles between E3 RING ligases and SARS-CoV-2: The role of zinc binding domains. Journal of Trace Elements in Medicine and Biology, 75, 127089. https://doi.org/10.1016/j.jtemb.2022.127089
15. Choi, Y., Tuel, A., & Eltahir, E. A. B. (2021). On the Environmental Determinants of COVID‐19 Seasonality. GeoHealth, 5(6). Portico. https://doi.org/10.1029/2021gh000413
16. Coro, G. (2020). A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate. Ecological Modelling, 431, 109187. https://doi.org/10.1016/j.ecolmodel.2020.109187
17. Evensen, G., Amezcua, J., Bocquet, M., et al. (2021). An international initiative of predicting the SARS-CoV-2 pandemic using ensemble data assimilation. Foundations of Data Science, 3(3), 413. https://doi.org/10.3934/fods.2021001
18. Fawagreh, K., Gaber, M. M., & Elyan, E. (2014). Random forests: from early developments to recent advancements. Systems Science & Control Engineering, 2(1), 602–609. https://doi.org/10.1080/21642583.2014.956265
19. Fong, S. J., Li, G., Dey, N., et al. (2020). Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction. Applied Soft Computing, 93, 106282. https://doi.org/10.1016/j.asoc.2020.106282
20. Haque, S. M., Ashwaq, O., Sarief, A., et al. (2020). A comprehensive review about SARS-CoV-2. Future Virology, 15(9), 625–648. https://doi.org/10.2217/fvl-2020-0124
21. Hu, B., Guo, H., Zhou, P., et al. (2021). Characteristics of SARS-CoV-2 and COVID-19. Nature Reviews Microbiology, 19(3), 141–154. https://doi.org/10.1038/s41579-020-00459-7
22. Hussein, H. A., Abdulazeez, A. M. (2021). COVID-19 pandemic datasets based on machine learning clustering algorithms: a review. PalArch’s Journal of Archaeology of Egypt/Egyptology, 18, 2672–700.
23. Jones, T. C., Biele, G., Mühlemann, B., et al. (2021). Estimating infectiousness throughout SARS-CoV-2 infection course. Science, 373(6551). https://doi.org/10.1126/science.abi5273
24. Kandikattu, H. K., Manohar, M., Verma, A. K., et al. (2021). Macrophages-induced IL-18–mediated eosinophilia promotes characteristics of pancreatic malignancy. Life Science Alliance, 4(8), e202000979. https://doi.org/10.26508/lsa.202000979
25. Karadimas, P. (2023). Public Choice Theory: An Explanation of the Pandemic Policy Responses. In: The Covid-19 Pandemic: A Public Choice View. Springer.
26. Kasting, M. L., Head, K. J., Hartsock, J. A., et al. (2020). Public perceptions of the effectiveness of recommended non-pharmaceutical intervention behaviors to mitigate the spread of SARS-CoV-2. PLOS ONE, 15(11), e0241662. https://doi.org/10.1371/journal.pone.0241662
27. Krammer, F. (2020). SARS-CoV-2 vaccines in development. Nature, 586(7830), 516–527. https://doi.org/10.1038/s41586-020-2798-3
28. Kwekha-Rashid, A. S., Abduljabbar, H. N., & Alhayani, B. (2021). Coronavirus disease (COVID-19) cases analysis using machine-learning applications. Applied Nanoscience, 13(3), 2013–2025. https://doi.org/10.1007/s13204-021-01868-7
29. Lalmuanawma, S., Hussain, J., & Chhakchhuak, L. (2020). Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons & Fractals, 139, 110059. https://doi.org/10.1016/j.chaos.2020.110059
30. Liu, Y., Morgenstern, C., Kelly, J., et al. (2021). The impact of non-pharmaceutical interventions on SARS-CoV-2 transmission across 130 countries and territories. BMC Medicine, 19(1). https://doi.org/10.1186/s12916-020-01872-8
31. Loey, M., Manogaran, G., Taha, M. H. N., et al. (2021). A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic. Measurement, 167, 108288. https://doi.org/10.1016/j.measurement.2020.108288
32. Ludwig, S., & Zarbock, A. (2020). Coronaviruses and SARS-CoV-2: A Brief Overview. Anesthesia & Analgesia, 131(1), 93–96. https://doi.org/10.1213/ane.0000000000004845
33. Luong, N.-D. M., Guillier, L., Federighi, M., et al. (2023). An agent-based model to simulate SARS-CoV-2 contamination of surfaces and meat cuts in processing plants. International Journal of Food Microbiology, 404, 110321. https://doi.org/10.1016/j.ijfoodmicro.2023.110321
34. Malik, J. A., Ahmed, S., Mir, A., et al. (2022). The SARS-CoV-2 mutations versus vaccine effectiveness: New opportunities to new challenges. Journal of Infection and Public Health, 15(2), 228–240. https://doi.org/10.1016/j.jiph.2021.12.014
35. Malki, Z., Atlam, E.-S., Hassanien, A. E., et al. (2020). Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches. Chaos, Solitons & Fractals, 138, 110137. https://doi.org/10.1016/j.chaos.2020.110137
36. Merow, C., & Urban, M. C. (2020). Seasonality and uncertainty in global COVID-19 growth rates. Proceedings of the National Academy of Sciences, 117(44), 27456–27464. https://doi.org/10.1073/pnas.2008590117
37. Perelson, A. S., & Ke, R. (2021). Mechanistic Modeling of SARS‐CoV‐2 and Other Infectious Diseases and the Effects of Therapeutics. Clinical Pharmacology & Therapeutics, 109(4), 829–840. Portico. https://doi.org/10.1002/cpt.2160
38. Poole, L. (2020). Seasonal Influences On The Spread Of SARS-CoV-2 (COVID19), Causality, and Forecastabililty (3-15-2020). SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3554746
39. Purssell, E., and Gould, D. (2023). Infection Prevention and Control in Healthcare Settings. John Wiley & Sons.
40. Putra, K. A., & Drajati, N. A. (2023). Moving ahead: Challenges and opportunities for teachers in post-pandemic pedagogy. Teacher Education and Teacher Professional Development in the COVID-19 Turn, 3–8. https://doi.org/10.1201/9781003347798-1
41. Qiang, X.-L., Xu, P., Fang, G., et al. (2020). Using the spike protein feature to predict infection risk and monitor the evolutionary dynamic of coronavirus. Infectious Diseases of Poverty, 9(1). https://doi.org/10.1186/s40249-020-00649-8
42. Rahman, M. M., Islam, Md. M., Manik, Md. M. H., et al. (2021). Machine Learning Approaches for Tackling Novel Coronavirus (COVID-19) Pandemic. SN Computer Science, 2(5). https://doi.org/10.1007/s42979-021-00774-7
43. Reme, B.-A., Gjesvik, J., & Magnusson, K. (2023). Predictors of the post-COVID condition following mild SARS-CoV-2 infection. Nature Communications, 14(1). https://doi.org/10.1038/s41467-023-41541-x
44. Rian, K., Esteban-Medina, M., Hidalgo, M. R., et al. (2021). Mechanistic modeling of the SARS-CoV-2 disease map. BioData Mining, 14(1). https://doi.org/10.1186/s13040-021-00234-1
45. Ruffieux, H., Hanson, A. L., Lodge, S., et al. (2023). A patient-centric modeling framework captures recovery from SARS-CoV-2 infection. Nature Immunology, 24(2), 349–358. https://doi.org/10.1038/s41590-022-01380-2
46. Ryan, J. M. (2023). COVID-19: Surviving a Pandemic. Routledge. https://doi.org/10.4324/9781003302698
47. Ryan, J. M., & Nanda, S. (2023). Pandemic Politics and the Politics of the Pandemic1. COVID-19: Individual Rights and Community Responsibilities, 105–123. https://doi.org/10.4324/9781003302643-7
48. Saha, I., Ghosh, N., Maity, D., et al. (2021). COVID-DeepPredictor: Recurrent Neural Network to Predict SARS-CoV-2 and Other Pathogenic Viruses. Frontiers in Genetics, 12. https://doi.org/10.3389/fgene.2021.569120
49. Shinde, G. R., Kalamkar, A. B., Mahalle, P. N., et al. (2020). Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art. SN Computer Science, 1(4). https://doi.org/10.1007/s42979-020-00209-9
50. Shulman, L. M. (2023). Infectious Diseases. In: Encyclopedia of Sustainability Science and Technology Series. Springer. https://doi.org/10.1007/978-1-0716-2463-0
51. Sultana, J., Kumar Singha, A., Tabrez Siddiqui, S., et al. (2022). COVID-19 Pandemic Prediction and Forecasting Using Machine Learning Classifiers. Intelligent Automation & Soft Computing, 32(2), 1007–1024. https://doi.org/10.32604/iasc.2022.021507
52. Waheed, Y., Sah, R., & Muhammad, K. (2023). Recent Developments in Vaccines for Viral Diseases. Vaccines, 11(2), 198. https://doi.org/10.3390/vaccines11020198
53. Wang, S., Pan, Y., Wang, Q., et al. (2020). Modeling the viral dynamics of SARS-CoV-2 infection. Mathematical Biosciences, 328, 108438. https://doi.org/10.1016/j.mbs.2020.108438
54. Xiang, Y., Jia, Y., Chen, L., et al. (2021). COVID-19 epidemic prediction and the impact of public health interventions: A review of COVID-19 epidemic models. Infectious Disease Modelling, 6, 324–342. https://doi.org/10.1016/j.idm.2021.01.001
55. Yang, X.-D., Li, H.-L., & Cao, Y.-E. (2021). Influence of Meteorological Factors on the COVID-19 Transmission with Season and Geographic Location. International Journal of Environmental Research and Public Health, 18(2), 484. https://doi.org/10.3390/ijerph18020484
56. Ye, Z.-W., Yuan, S., Yuen, K. S., et al. (2020). Zoonotic origins of human coronaviruses. International journal of biological sciences, 16, 1686.