Mathematical Modeling to Predict COVID-19 Infection and Vaccination Trends
Background: COVID-19 caused by the Severe Acute Respiratory Syndrome Coronavirus 2 placed the health systems around the entire world in a battle against the clock. While most of the existing studies aimed at forecasting the infections trends, our study focuses on vaccination trend(s). Material and m...
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| Format: | Article |
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MDPI AG
2022-03-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/11/6/1737 |
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| author | Bogdan Doroftei Ovidiu-Dumitru Ilie Nicoleta Anton Sergiu-Ioan Timofte Ciprian Ilea |
| author_facet | Bogdan Doroftei Ovidiu-Dumitru Ilie Nicoleta Anton Sergiu-Ioan Timofte Ciprian Ilea |
| author_sort | Bogdan Doroftei |
| collection | DOAJ |
| description | Background: COVID-19 caused by the Severe Acute Respiratory Syndrome Coronavirus 2 placed the health systems around the entire world in a battle against the clock. While most of the existing studies aimed at forecasting the infections trends, our study focuses on vaccination trend(s). Material and methods: Based on these considerations, we used standard analyses and ARIMA modeling to predict possible scenarios in Romania, the second-lowest country regarding vaccinations from the entire European Union. Results: With approximately 16 million doses of vaccine against COVID-19 administered, 7,791,250 individuals had completed the vaccination scheme. From the total, 5,058,908 choose <i>Pfizer–BioNTech</i>, 399,327 <i>Moderna</i>, 419,037 <i>AstraZeneca</i>, and 1,913,978 <i>Johnson & Johnson</i>. With a cumulative 2147 local and 17,542 general adverse reactions, the most numerous were reported in recipients of <i>Pfizer–BioNTech</i> (1581 vs. 8451), followed by <i>AstraZeneca</i> (138 vs. 6033), <i>Moderna</i> (332 vs. 1936), and <i>Johnson & Johnson</i> (96 vs. 1122). On three distinct occasions have been reported >50,000 individuals who received the first or second dose of a vaccine and >30,000 of a booster dose in a single day. Due to high reactogenicity in case of AZD1222, and time of launching between the <i>Pfizer–BioNTech</i> and <i>Moderna</i> vaccine could be explained differences in terms doses administered. Furthermore, ARIMA(1,1,0), ARIMA(1,1,1), ARIMA(0,2,0), ARIMA(2,1,0), ARIMA(1,2,2), ARI-MA(2,2,2), ARIMA(0,2,2), ARIMA(2,2,2), ARIMA(1,1,2), ARIMA(2,2,2), ARIMA(2,1,1), ARIMA(2,2,1), and ARIMA (2,0,2) for all twelve months and in total fitted the best models. These were regarded according to the lowest MAPE, <i>p</i>-value (<i>p</i> < 0.05, <i>p</i> < 0.01, and <i>p</i> < 0.001) and through the Ljung–Box test (<i>p</i> < 0.05, <i>p</i> < 0.01, and <i>p</i> < 0.001) for autocorrelations. Conclusions: Statistical modeling and mathematical analyses are suitable not only for forecasting the infection trends but the course of a vaccination rate as well. |
| first_indexed | 2024-03-09T19:36:28Z |
| format | Article |
| id | doaj.art-48e284b1996644358a762c0e780d3036 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-09T19:36:28Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-48e284b1996644358a762c0e780d30362023-11-24T01:51:48ZengMDPI AGJournal of Clinical Medicine2077-03832022-03-01116173710.3390/jcm11061737Mathematical Modeling to Predict COVID-19 Infection and Vaccination TrendsBogdan Doroftei0Ovidiu-Dumitru Ilie1Nicoleta Anton2Sergiu-Ioan Timofte3Ciprian Ilea4Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, University Street, No. 16, 700115 Iasi, RomaniaDepartment of Biology, Faculty of Biology, “Alexandru Ioan Cuza” University, Carol I Avenue, No. 20A, 700505 Iasi, RomaniaFaculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, University Street, No. 16, 700115 Iasi, RomaniaDepartment of Biology, Faculty of Biology, “Alexandru Ioan Cuza” University, Carol I Avenue, No. 20A, 700505 Iasi, RomaniaFaculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, University Street, No. 16, 700115 Iasi, RomaniaBackground: COVID-19 caused by the Severe Acute Respiratory Syndrome Coronavirus 2 placed the health systems around the entire world in a battle against the clock. While most of the existing studies aimed at forecasting the infections trends, our study focuses on vaccination trend(s). Material and methods: Based on these considerations, we used standard analyses and ARIMA modeling to predict possible scenarios in Romania, the second-lowest country regarding vaccinations from the entire European Union. Results: With approximately 16 million doses of vaccine against COVID-19 administered, 7,791,250 individuals had completed the vaccination scheme. From the total, 5,058,908 choose <i>Pfizer–BioNTech</i>, 399,327 <i>Moderna</i>, 419,037 <i>AstraZeneca</i>, and 1,913,978 <i>Johnson & Johnson</i>. With a cumulative 2147 local and 17,542 general adverse reactions, the most numerous were reported in recipients of <i>Pfizer–BioNTech</i> (1581 vs. 8451), followed by <i>AstraZeneca</i> (138 vs. 6033), <i>Moderna</i> (332 vs. 1936), and <i>Johnson & Johnson</i> (96 vs. 1122). On three distinct occasions have been reported >50,000 individuals who received the first or second dose of a vaccine and >30,000 of a booster dose in a single day. Due to high reactogenicity in case of AZD1222, and time of launching between the <i>Pfizer–BioNTech</i> and <i>Moderna</i> vaccine could be explained differences in terms doses administered. Furthermore, ARIMA(1,1,0), ARIMA(1,1,1), ARIMA(0,2,0), ARIMA(2,1,0), ARIMA(1,2,2), ARI-MA(2,2,2), ARIMA(0,2,2), ARIMA(2,2,2), ARIMA(1,1,2), ARIMA(2,2,2), ARIMA(2,1,1), ARIMA(2,2,1), and ARIMA (2,0,2) for all twelve months and in total fitted the best models. These were regarded according to the lowest MAPE, <i>p</i>-value (<i>p</i> < 0.05, <i>p</i> < 0.01, and <i>p</i> < 0.001) and through the Ljung–Box test (<i>p</i> < 0.05, <i>p</i> < 0.01, and <i>p</i> < 0.001) for autocorrelations. Conclusions: Statistical modeling and mathematical analyses are suitable not only for forecasting the infection trends but the course of a vaccination rate as well.https://www.mdpi.com/2077-0383/11/6/1737COVID-19SARS-CoV-2Romaniadosesvaccination schemereactogenicity |
| spellingShingle | Bogdan Doroftei Ovidiu-Dumitru Ilie Nicoleta Anton Sergiu-Ioan Timofte Ciprian Ilea Mathematical Modeling to Predict COVID-19 Infection and Vaccination Trends Journal of Clinical Medicine COVID-19 SARS-CoV-2 Romania doses vaccination scheme reactogenicity |
| title | Mathematical Modeling to Predict COVID-19 Infection and Vaccination Trends |
| title_full | Mathematical Modeling to Predict COVID-19 Infection and Vaccination Trends |
| title_fullStr | Mathematical Modeling to Predict COVID-19 Infection and Vaccination Trends |
| title_full_unstemmed | Mathematical Modeling to Predict COVID-19 Infection and Vaccination Trends |
| title_short | Mathematical Modeling to Predict COVID-19 Infection and Vaccination Trends |
| title_sort | mathematical modeling to predict covid 19 infection and vaccination trends |
| topic | COVID-19 SARS-CoV-2 Romania doses vaccination scheme reactogenicity |
| url | https://www.mdpi.com/2077-0383/11/6/1737 |
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