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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Bibliographic Details
Main Authors: Bogdan Doroftei, Ovidiu-Dumitru Ilie, Nicoleta Anton, Sergiu-Ioan Timofte, Ciprian Ilea
Format: Article
Language:English
Published: MDPI AG 2022-03-01
Series:Journal of Clinical Medicine
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Online Access:https://www.mdpi.com/2077-0383/11/6/1737
Description
Summary: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.
ISSN:2077-0383