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

Q1 Decision Sciences

Annals of Data Science Pub Date : 2023-01-19 DOI:10.1007/s40745-023-00462-8

Desmond Chekwube Bartholomew, Chrysogonus Chinagorom Nwaigwe, Ukamaka Cynthia Orumie, Godwin Onyeka Nwafor

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Abstract

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.

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尼日利亚新冠肺炎疫苗接种干预分析：天真的解决方案与中断的时间序列

本文对尼日利亚 COVID-19 的每日病例采用了干预分析方法，以评估该国 COVID-19 疫苗的使用情况和效果。本文收集了尼日利亚 COVID-19 病例的每日报告数据，并对其采用了两种模型：天真解决方案和间断时间序列（干预模型）。根据 Alkaike 信息准则（AIC）、sigma2 和对数似然值，中断时间序列模型优于天真解模型。带有外生变量的 ARIMA（4，1，4）被认为是最佳模型。据观察，自 2021 年 3 月 5 日开始接种疫苗至 2022 年 3 月 28 日，干预措施（接种疫苗）在 5%的显著性水平上对减少尼日利亚 COVID-19 每日病例数并不显著。此外，ARIMA（4，1，4）预测表明，在 2023 年 1 月至 4 月期间，尼日利亚 COVID-19 的每日病例数将会激增。因此，我们建议严格遵守 COVID-19 协议，并进一步开展疫苗接种和宣传计划，让人们了解接种疫苗的重要性，避免科罗娜病毒在 2023 年及以后传播。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty

CiteScore

6.50

自引率

0.00%

发文量

期刊介绍： Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed. ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.