Comparing Time-Series Analysis Approaches Utilized in Research Papers to Forecast COVID-19 Cases in Africa: A Literature Review

TL;DR

This literature review compares various time-series analysis methods used in forecasting COVID-19 cases in Africa, highlighting their effectiveness and limitations to guide future research and improve public health decision-making.

Contribution

It systematically evaluates and compares different time-series approaches used in COVID-19 forecasting in Africa, providing insights for future model improvements.

Findings

Different methodologies have varying effectiveness in forecasting COVID-19 cases.

Limitations exist in current time-series models for COVID-19 prediction.

Insights from this review can guide future research and model development.

Abstract

This literature review aimed to compare various time-series analysis approaches utilized in forecasting COVID-19 cases in Africa. The study involved a methodical search for English-language research papers published between January 2020 and July 2023, focusing specifically on papers that utilized time-series analysis approaches on COVID-19 datasets in Africa. A variety of databases including PubMed, Google Scholar, Scopus, and Web of Science were utilized for this process. The research papers underwent an evaluation process to extract relevant information regarding the implementation and performance of the time-series analysis models. The study highlighted the different methodologies employed, evaluating their effectiveness and limitations in forecasting the spread of the virus. The result of this review could contribute deeper insights into the field, and future research should consider these insights to improve time series analysis models and explore the integration of different approaches for enhanced public health decision-making.