Intervention Analysis of COVID-19 Vaccination in Nigeria: The Naive Solution Versus Interrupted Time Series

被引：3
作者：
Bartholomew D.C. [1 ]
Nwaigwe C.C. [1 ]
Orumie U.C. [2 ]
Nwafor G.O. [1 ]
机构：
[1] Department of Statistics, Federal University of Technology Owerri, Imo State, Owerri
[2] Department of Mathematics and Statistics, University of Port Harcourt, Rivers State, Port Harcourt
来源：
Annals of Data Science | 2024年 / 11卷 / 05期
关键词：
COVID-19; Effect; Interrupted time series; Intervention analysis; Naïve solution; Vaccination;
D O I：
10.1007/s40745-023-00462-8
中图分类号：
学科分类号：
摘要：
In this paper, an intervention analysis approach was applied to daily cases of COVID-19 in Nigeria in order to evaluate the utilization and effect of the COVID-19 vaccine administered in the country. Data on the daily report of COVID-19 cases in Nigeria were collected and subjected to two models: the naïve solution and the interrupted time series (the intervention model). Based on the Alkaike Information Criterion (AIC), sigma2, and log likelihood values, the interrupted time series model outperformed the Naïve solution model. ARIMA (4, 1, 4) with exogenous variables was identified as the best model. It was observed that the intervention (vaccination) was not significant at the 5% level of significance in reducing the number of daily COVID-19 cases in Nigeria since the start of the vaccination on March 5, 2021, until March 28, 2022. Also, the ARIMA (4, 1, 4) forecasts indicated that there will be surge in the number of daily COVID-19 cases in Nigeria between January and April 2023. As a result, we recommend strict adherence to COVID-19 protocols as well as further vaccination and sensitization programs to educate people on the importance of vaccine uptake and avoid Corona virus spread in the year 2023 and beyond. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.
引用
收藏
页码：1609 / 1634
页数：25
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