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Annals of Data Science
Erschienen in:

19.01.2023

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

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

Erschienen in: Annals of Data Science | Ausgabe 5/2024

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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.
Vorheriger Artikel Bayesian Hierarchical Spatial Modeling of COVID-19 Cases in Bangladesh
Nächster Artikel A Bayes Analysis of Random Walk Model Under Different Error Assumptions

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Metadaten
Titel
Intervention Analysis of COVID-19 Vaccination in Nigeria: The Naive Solution Versus Interrupted Time Series
verfasst von
Desmond Chekwube Bartholomew
Chrysogonus Chinagorom Nwaigwe
Ukamaka Cynthia Orumie
Godwin Onyeka Nwafor
Publikationsdatum
19.01.2023
Verlag
Springer Berlin Heidelberg
Erschienen in
Annals of Data Science / Ausgabe 5/2024
Print ISSN: 2198-5804
Elektronische ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-023-00462-8

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