The initial COVID-19 vaccinations were created and distributed to the general population in 2020 thanks to emergency authorization and conditional approval. Consequently, numerous countries followed the process that is currently a global campaign. Taking into account the fact that people are being vaccinated, there are concerns about the effectiveness of that medical solution. Actually, this study is the first one focusing on how the number of vaccinated people might influence the spread of the pandemic in the world. From the Global Change Data Lab "Our World in Data", we were able to get data sets about the number of new cases and vaccinated people. This study is a longitudinal one from 14/12/2020 to 21/03/2021. In addition, we computed Generalized log-Linear Model on count time series (Negative Binomial distribution due to over dispersion in data) and implemented validation tests to confirm the robustness of our results. The findings revealed that when the number of vaccinated people increases by one new vaccination on a given day, the number of new cases decreases significantly two days after by one. The influence is not notable on the same day of vaccination. Authorities should increase the vaccination campaign to control well the pandemic. That solution has effectively started to reduce the spread of COVID-19 in the world.
Citation: Mustafa Kamal, Mintodê Nicodème Atchadé, Yves Morel Sokadjo, Sabir Ali Siddiqui, Fathy H. Riad, M. M. Abd El-Raouf, Ramy Aldallal, Eslam Hussam, Huda M. Alshanbari, Hassan Alsuhabi, Ahmed M. Gemeay. Influence of COVID-19 vaccination on the dynamics of new infected cases in the world[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 3324-3341. doi: 10.3934/mbe.2023156
The initial COVID-19 vaccinations were created and distributed to the general population in 2020 thanks to emergency authorization and conditional approval. Consequently, numerous countries followed the process that is currently a global campaign. Taking into account the fact that people are being vaccinated, there are concerns about the effectiveness of that medical solution. Actually, this study is the first one focusing on how the number of vaccinated people might influence the spread of the pandemic in the world. From the Global Change Data Lab "Our World in Data", we were able to get data sets about the number of new cases and vaccinated people. This study is a longitudinal one from 14/12/2020 to 21/03/2021. In addition, we computed Generalized log-Linear Model on count time series (Negative Binomial distribution due to over dispersion in data) and implemented validation tests to confirm the robustness of our results. The findings revealed that when the number of vaccinated people increases by one new vaccination on a given day, the number of new cases decreases significantly two days after by one. The influence is not notable on the same day of vaccination. Authorities should increase the vaccination campaign to control well the pandemic. That solution has effectively started to reduce the spread of COVID-19 in the world.
[1] | Y. Diao, S. Kodera, D. Anzai, J. Gomez-Tames, E. A. Rashed, A. Hirata, Influence of population density, temperature, and absolute humidity on spread and decay durations of COVID-19: a comparative study of scenarios in China, England, Germany, and Japan, One Health, 12 (2021), 100203. https://doi.org/10.1016/j.onehlt.2020.100203 doi: 10.1016/j.onehlt.2020.100203 |
[2] | Y. Kubota, T. Shiono, B. Kusumoto, J. Fujinuma, Multiple drivers of the COVID-19 spread: the roles of climate, international mobility, and region-specific conditions, PLoS One, 15 (2020), e0239385. https://doi.org/10.1371/journal.pone.0239385 doi: 10.1371/journal.pone.0239385 |
[3] | Y. M. Sokadjo, M. N. Atchadé, The influence of passenger air traffic on the spread of COVID-19 in the world, Transp. Res. Interdiscip. Perspect., 8 (2020), 100213. https://doi.org/10.1016/j.trip.2020.100213 doi: 10.1016/j.trip.2020.100213 |
[4] | A. Miller, M. J. Reandelar, K. Fasciglione, V. Roumenova, Y. Li, G. H. Otazu, Correlation between universal bcg vaccination policy and reduced mortality for COVID-19, medRxiv, 2020. https://doi.org/10.1101/2020.03.24.20042937 doi: 10.1101/2020.03.24.20042937 |
[5] | N. Curtis, A. Sparrow, T. A. Ghebreyesus, M. G. Netea, Considering bcg vaccination to reduce the impact of COVID-19, Lancet, 395 (2020), 1545–1546. https://doi.org/10.1016/S0140-6736(20)31025-4 doi: 10.1016/S0140-6736(20)31025-4 |
[6] | P. K. Hegarty, A. M. Kamat, H. Zafirakis, A. Dinardo, Bcg vaccination may be protective against COVID-19, 2020. Available from: https://www.researchgate.net/publication/340224580. |
[7] | S. Shivendu, S. Chakraborty, A. Onuchowska, A. Patidar, A. Srivastava, Is there evidence that bcg vaccination has non-specific protective effects for COVID-19 infections or is it an illusion created by lack of testing, medRxiv, 2020. https://doi.org/10.1101/2020.04.18.20071142 doi: 10.1101/2020.04.18.20071142 |
[8] | J. Hensel, K. M. McAndrews, D. J. McGrail, D. P. Dowlatshahi, V. S. LeBleu, R. Kalluri, Protection against SARS-CoV-2 by bcg vaccination is not supported by epidemiological analyses, Sci. Rep., 10 (2020), 1–9. https://doi.org/10.1038/s41598-020-75491-x doi: 10.1038/s41598-020-75491-x |
[9] | A. Gulati, C. Pomeranz, Z. Qamar, S. Thomas, D. Frisch, G. George, et al., A comprehensive review of manifestations of novel coronaviruses in the context of deadly COVID-19 global pandemic, Am. J. Med. Sci., 360 (2020), 5–34. https://doi.org/10.1016/j.amjms.2020.05.006 doi: 10.1016/j.amjms.2020.05.006 |
[10] | A. Atkeson, How deadly is COVID-19? Understanding the difficulties with estimation of its fatality rate, Nat. Bur. Econ. Res., 2020 (2020). https://doi.org/10.3386/w26965 doi: 10.3386/w26965 |
[11] | Z. Zaharah, G. I. Kirilova, A. Windarti, Impact of corona virus outbreak towards teaching and learning activities in indonesia, SALAM: J. Sosial Budaya Syar-I, 7 (2020), 269–282. https://doi.org/10.15408/sjsbs.v7i3.15104 doi: 10.15408/sjsbs.v7i3.15104 |
[12] | R. Saini, Impact of corona virus on indian economy, 2020. Available from: https://ssrn.com/abstract=3595300. |
[13] | P. P. Sahoo, S. Rath, Potential impact of corona virus on agriculture sector, Biotica Res. Today, 2 (2020), 64–65. Available from: https://www.biospub.com/index.php/biorestoday/article/view/52. |
[14] | R. Siche, What is the impact of COVID-19 disease on agriculture, Sci. Agropecu., 11 (2020), 3–6. https://doi.org/10.17268/sci.agropecu.2020.01.00 doi: 10.17268/sci.agropecu.2020.01.00 |
[15] | S. Dubey, P. Biswas, R. Ghosh, S. Chatterjee, M. J. Dubey, S. Chatterjee, et al., Psychosocial impact of COVID-19, Diabetes Metab. Syndr., 14 (2020), 779–788. https://doi.org/10.1016/j.dsx.2020.05.035 doi: 10.1016/j.dsx.2020.05.035 |
[16] | S. Grover, S. Sahoo, A. Mehra, A. Avasthi, A. Tripathi, A. Subramanyan, et al., Psychological impact of COVID-19 lockdown: an online survey from India, Indian J. Psychiatry, 62 (2020), 354. https://doi.org/10.4103/psychiatry.IndianJPsychiatry_427_20 doi: 10.4103/psychiatry.IndianJPsychiatry_427_20 |
[17] | Q. Wang, M. Su, A preliminary assessment of the impact of COVID-19 on environment–a case study of China, Sci. Total Environ., 728 (2020), 138915. https://doi.org/10.1016/j.scitotenv.2020.138915 doi: 10.1016/j.scitotenv.2020.138915 |
[18] | H. Alsuhabi, I. Alkhairy, E. M. Almetwally, H. M. Almongy, A. M. Gemeay, E. Hafez, et al., A superior extension for the lomax distribution with application to COVID-19 infections real data, Alexandria Eng. J., 61 (2022), 11077–11090. https://doi.org/10.1016/j.aej.2022.03.067 doi: 10.1016/j.aej.2022.03.067 |
[19] | B. Meriem, A. M. Gemeay, E. M. Almetwally, Z. Halim, E. Alshawarbeh, A. T. Abdulrahman, et al., The power xlindley distribution: statistical inference, fuzzy reliability, and COVID-19 application, J. Funct. Spaces, 2022 (2022). https://doi.org/10.1155/2022/9094078 doi: 10.1155/2022/9094078 |
[20] | P. Jiménez-Rodríguez, G. A. Muñoz-Fernández, J. C. Rodrigo-Chocano, J. B. Seoane-Sepúlveda, A. Weber, A population structure-sensitive mathematical model assessing the effects of vaccination during the third surge of COVID-19 in italy, J. Math. Anal. Appl., 514 (2022), 125975. https://doi.org/10.1016/j.jmaa.2021.125975 doi: 10.1016/j.jmaa.2021.125975 |
[21] | J. Panovska-Griffiths, B. Swallow, R. Hinch, J. A. Cohen, K. Rosenfeld, R. M. Stuart, et al., Statistical and agent-based modelling of the transmissibility of different SARS-CoV-2 variants in england and impact of different interventions, Philos. Trans. A Math. Phys. Eng. Sci., 380 (2022). https://doi.org/10.1098/rsta.2021.0315 doi: 10.1098/rsta.2021.0315 |
[22] | B. Tang, W. Zhou, X. Wang, H. Wu, Y. Xiao, Controlling multiple COVID-19 epidemic waves: an insight from a multi-scale model linking the behaviour change dynamics to the disease transmission dynamics, Bull. Math. Biol., 84 (2022), 1–31. https://doi.org/10.1007/s11538-022-01061-z doi: 10.1007/s11538-022-01061-z |
[23] | C. A. Varotsos, V. F. Krapivin, A new model for the spread of COVID-19 and the improvement of safety, Saf. Sci., 132 (2020), 104962. https://doi.org/10.1016/j.ssci.2020.104962 doi: 10.1016/j.ssci.2020.104962 |
[24] | M. N. Atchadé, Y. M. Sokadjo, Overview and cross-validation of COVID-19 forecasting univariate models, Alexandria Eng. J., 61 (2021), 3021–3036. https://doi.org/10.1016/j.aej.2021.08.028 doi: 10.1016/j.aej.2021.08.028 |
[25] | M. N. Atchadé, Y. M. Sokadjo, A. D. Moussa, S. V. Kurisheva, M. V. Bochenina, Cross-validation comparison of COVID-19 forecast models, SN Comput. Sci., 2 (2021), 1–9. https://doi.org/10.1007/s42979-021-00699-1 doi: 10.1007/s42979-021-00699-1 |
[26] | O. Agossou, M. N. Atchadé, A. M. Djibril, Modeling the effects of preventive measures and vaccination on the COVID-19 spread in benin republic with optimal control, Results Phys., 2021 (2021), 104969. https://doi.org/10.1016/j.rinp.2021.104969 doi: 10.1016/j.rinp.2021.104969 |
[27] | T. T. Le, Z. Andreadakis, A. Kumar, R. G. Román, S. Tollefsen, M. Saville, et al., The COVID-19 vaccine development landscape, Nat. Rev. Drug Discov., 19 (2020), 305–306. https://doi.org/10.1038/d41573-020-00073-5 doi: 10.1038/d41573-020-00073-5 |
[28] | S. Loomba, A. de Figueiredo, S. J. Piatek, K. de Graaf, H. J. Larson, Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA, Nat. Hum. Behav., 5 (2021), 337–348. https://doi.org/10.1038/s41562-021-01056-1 doi: 10.1038/s41562-021-01056-1 |
[29] | R. Schiavo, Vaccine communication in the age of COVID-19: getting ready for an information war, J. Commun. Healthcare, 13 (2020), 73–75. https://doi.org/10.1080/17538068.2020.1778959 doi: 10.1080/17538068.2020.1778959 |
[30] | I. Benati, M. Coccia, Global analysis of timely COVID-19 vaccinations: improving governance to reinforce response policies for pandemic crises, Int. J. Health Governance, 2022 (2022). https://doi.org/10.1108/ijhg-07-2021-0072 doi: 10.1108/ijhg-07-2021-0072 |
[31] | A. A. Malik, S. M. McFadden, J. Elharake, S. B. Omer, Determinants of COVID-19 vaccine acceptance in the US, EClinicalMedicine, 26 (2020), 100495. https://doi.org/10.1016/j.eclinm.2020.100495 doi: 10.1016/j.eclinm.2020.100495 |
[32] | G. Troiano, A. Nardi, Vaccine hesitancy in the era of COVID-19, Public Health, 2021 (2021). https://doi.org/10.1016/j.puhe.2021.02.025 doi: 10.1016/j.puhe.2021.02.025 |
[33] | P. Peretti-Watel, V. Seror, S. Cortaredona, O. Launay, J. Raude, P. Verger, et al., A future vaccination campaign against COVID-19 at risk of vaccine hesitancy and politicisation, Lancet Infect. Dis., 20 (2020), 769–770. https://doi.org/10.1016/S1473-3099(20)30426-6 doi: 10.1016/S1473-3099(20)30426-6 |
[34] | S. M. Saied, E. M. Saied, I. A. Kabbash, S. A. E. F. Abdo, Vaccine hesitancy: beliefs and barriers associated with COVID-19 vaccination among egyptian medical students, J. Med. Virol., 93 (2021), 4280–4291. https://doi.org/10.1002/jmv.26910 doi: 10.1002/jmv.26910 |
[35] | C. L. Lackner, C. H. Wang, Demographic, psychological, and experiential correlates of SARS-CoV-2 vaccination intentions in a sample of canadian families, Vaccine: X, 8 (2021), 100091. https://doi.org/10.1016/j.jvacx.2021.100091 doi: 10.1016/j.jvacx.2021.100091 |
[36] | P. Verger, E. Dubé, Restoring confidence in vaccines in the COVID-19 era, Expert Rev. Vaccines, 19 (2020), 991–993. https://doi.org/10.1080/14760584.2020.1825945 doi: 10.1080/14760584.2020.1825945 |
[37] | H. M. Almongy, E. M. Almetwally, H. H. Ahmad, A. H. Al-Nefaie, Modeling of COVID-19 vaccination rate using odd lomax inverted nadarajah-haghighi distribution, PLoS One, 17 (2022), e0276181. https://doi.org/10.1371/journal.pone.0276181 doi: 10.1371/journal.pone.0276181 |
[38] | H. M. Alshanbari, O. H. Odhah, E. M. Almetwally, E. Hussam, M. Kilai, A. A. H. El-Bagoury, Novel type I half logistic burr-weibull distribution: application to COVID-19 data, Comput. Math. Methods Med., 18 (2022). https://doi.org/10.1155/2022/1444859 doi: 10.1155/2022/1444859 |
[39] | E. M. Almetwally, S. Dey, S. Nadarajah, An overview of discrete distributions in modelling COVID-19 data sets, Sankhya A, 2022 (2022), 1–28. https://doi.org/10.1007/s13171-022-00291-6 doi: 10.1007/s13171-022-00291-6 |
[40] | B. Meriem, A. M. Gemeay, E. M. Almetwally, Z. Halim, E. Alshawarbeh, A. T. Abdulrahman, et al., The power xlindley distribution: Statistical inference, fuzzy reliability, and covid-19 application, J. Funct. Spaces, 2022 (2022). https://doi.org/10.1155/2022/9094078 doi: 10.1155/2022/9094078 |
[41] | F. H. Riad, B. Alruwaili, E. M. Almetwally, E. Hussam, Fuzzy reliability analysis of the COVID-19 mortality rate using a new modified kies kumaraswamy model, J. Math., 2022 (2022). https://doi.org/10.1155/2022/3427521 doi: 10.1155/2022/3427521 |
[42] | E. M. Almetwally, The odd weibull inverse topp–leone distribution with applications to COVID-19 data, Ann. Data Sci., 9 (2022), 121–140. https://doi.org/10.1007/s40745-021-00329-w doi: 10.1007/s40745-021-00329-w |
[43] | M. M. Higdon, A. Baidya, K. K. Walter, M. K. Patel, H. Issa, E. Espié, et al., Duration of effectiveness of vaccination against COVID-19 caused by the omicron variant, Lancet Infect. Dis., 22 (2022), 1114–1116. https://doi.org/10.1016/S1473-3099(22)00409-1 doi: 10.1016/S1473-3099(22)00409-1 |
[44] | M. Coccia, Optimal levels of vaccination to reduce COVID-19 infected individuals and deaths: a global analysis, Environ. Res., 204 (2022), 112314. https://doi.org/10.1016/j.envres.2021.112314 doi: 10.1016/j.envres.2021.112314 |
[45] | G. N. Ioannou, E. R. Locke, A. M. O'Hare, A. S. Bohnert, E. J. Boyko, D. M. Hynes, et al., COVID-19 vaccination effectiveness against infection or death in a national us health care system: a target trial emulation study, Ann. Intern. Med., 175 (2022), 352–361. https://doi.org/10.7326/M21-3256 doi: 10.7326/M21-3256 |
[46] | L. Lin, Y. Zhao, B. Chen, D. He, Multiple COVID-19 waves and vaccination effectiveness in the united states, Int. J. Environ. Res. Public Health, 19 (2022), 2282. https://doi.org/10.3390/ijerph19042282 doi: 10.3390/ijerph19042282 |
[47] | E. Mathieu, H. Ritchie, L. Rodés-Guirao, C. Appel, C. Giattino, J. Hasell, et al., Coronavirus pandemic (COVID-19), Our World in Data, 2020. Available from: https://ourworldindata.org/coronavirus. |
[48] | T. Liboschik, K. Fokianos, R. Fried, tscount: An R package for analysis of count time series following generalized linear models, Universitätsbibliothek Dortmund, Dortmund, Germany, 2015. https://doi.org/10.17877/DE290R-7239 |
[49] | R. I. Harris, Testing for unit roots using the augmented dickey-fuller test: some issues relating to the size, power and the lag structure of the test, Econ. Lett., 38 (1992), 381–386. https://doi.org/10.1016/0165-1765(92)90022-Q doi: 10.1016/0165-1765(92)90022-Q |
[50] | C. Laine, D. Cotton, D. V. Moyer, COVID-19 vaccine: promoting vaccine acceptance, Ann. Intern. Med., 174 (2021), 252–253. https://doi.org/10.7326/M20-8008 doi: 10.7326/M20-8008 |
[51] | S. Su, L. Du, S. Jiang, Learning from the past: development of safe and effective COVID-19 vaccines, Nat. Rev. Microbiol., 19 (2020), 211–219. https://doi.org/10.1038/s41579-020-00462-y doi: 10.1038/s41579-020-00462-y |
[52] | R. N. Kostoff, M. B. Briggs, A. L. Porter, D. A. Spandidos, A. Tsatsakis, [Comment] COVID-19 vaccine safety, Int. J. Mol. Med., 46 (2020), 1599–1602. https://doi.org/10.3892/ijmm.2020.4733 doi: 10.3892/ijmm.2020.4733 |
[53] | I. Jones, P. Roy, Sputnik V COVID-19 vaccine candidate appears safe and effective, Lancet, 397 (2021), 642–643. https://doi.org/10.1016/S0140-6736(21)00191-4 doi: 10.1016/S0140-6736(21)00191-4 |
[54] | W. E. Wei, Z. Li, C. J. Chiew, S. E. Yong, M. P. Toh, V. J. Lee, Presymptomatic transmission of SARS-CoV-2-singapore, January 23-March 16, 2020, Morb. Mortal. Wkly. Rep., 69 (2020), 411–415. https://doi.org/10.15585/mmwr.mm6914e1 doi: 10.15585/mmwr.mm6914e1 |
[55] | L. Luo, D. Liu, X. L. Liao, X. B. Wu, Q. L. Jing, J. Z. Zheng, et al., Modes of contact and risk of transmission in COVID-19 among close contacts, Ann. Intern. Med., 2020 (2020). https://doi.org/10.7326/m20-2671 doi: 10.7326/m20-2671 |
[56] | S. Patrikar, A. Kotwal, V. Bhatti, A. Banerjee, K. Chatterjee, R. Kunte, et al., Incubation period and reproduction number for novel coronavirus (COVID-19) infections in India, Asia Pac. J. Public Health, 32 (2020), 458–460. https://doi.org/10.1177/1010539520956427 doi: 10.1177/1010539520956427 |
[57] | S. Khailaie, T. Mitra, A. Bandyopadhyay, M. Schips, P. Mascheroni, P. Vanella, et al., Development of the reproduction number from coronavirus SARS-CoV-2 case data in germany and implications for political measures, BMC Med., 19 (2020), 32. https://doi.org/10.1186/s12916-020-01884-4 doi: 10.1186/s12916-020-01884-4 |
[58] | J. Riou, C. L. Althaus, Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020, Euro. Surveill., 25 (2020), 2000058. https://doi.org/10.2807/1560-7917.ES.2020.25.4.2000058 doi: 10.2807/1560-7917.ES.2020.25.4.2000058 |
[59] | B. Machado, L. Antunes, C. Caetano, J. F. Pereira, B. Nunes, P. Patrício, et al., The impact of vaccination on the evolution of COVID-19 in Portugal, Math. Biosci. Eng., 19 (2022), 936–952. https://doi.org/10.3934/mbe.2022043 doi: 10.3934/mbe.2022043 |
[60] | V. Piccirillo, COVID-19 pandemic control using restrictions and vaccination, Math. Biosci. Eng., 19 (2022), 1355–1372. https://doi.org/10.3934/mbe.2022062 doi: 10.3934/mbe.2022062 |
[61] | T. Kobayashi, H. Nishiura, Prioritizing COVID-19 vaccination. Part 2: Real-time comparison between single-dose and double-dose in Japan, Math. Biosci. Eng., 19 (2022), 7410–7424. https://doi.org/10.3934/mbe.2022350 doi: 10.3934/mbe.2022350 |
[62] | M. V. Reyes, The disproportional impact of COVID-19 on African Americans, Health Hum. Rights, 22 (2020), 299–307. |
[63] | R. Cappi, L. Casini, D. Tosi, M. Roccetti, Questioning the seasonality of SARS-CoV-2: a fourier spectral analysis, BMJ Open, 12 (2022), e061602. https://doi.org/10.1136/bmjopen-2022-061602 doi: 10.1136/bmjopen-2022-061602 |
[64] | F. F. Zhang, Z. Jin, Effect of travel restrictions, contact tracing and vaccination on control of emerging infectious diseases: transmission of COVID-19 as a case study, Math. Biosci. Eng., 19 (2022), 3177–3201. https://doi.org/10.3934/mbe.2022147 doi: 10.3934/mbe.2022147 |
[65] | E. Iboi, O. O. Sharomi, C. Ngonghala, A. B. Gumel, Mathematical modeling and analysis of COVID-19 pandemic in Nigeria, Math. Biosci. Eng., 17 (2020), 7192–7220. https://doi.org/10.3934/mbe.2020369 doi: 10.3934/mbe.2020369 |