ArticleModeling earth systems and environment2022
Modelling COVID-19 incidence in the African sub-region using smooth transition autoregressive model.
Article in Modeling earth systems and environment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Variegated spatial-temporal landscape of COVID-19 infection in England: findings from spatially filtered multilevel models.Journal of public health (Oxford, England) · 2023Article
- Structural equation modelling of COVID-19 knowledge and attitude as determinants of preventive practices among university students in Ghana.Scientific African · 2022Article
- A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning.Computers in biology and medicine · 2022Article
- The influence of awareness campaigns on the spread of an infectious disease: a qualitative analysis of a fractional epidemic model.Modeling earth systems and environment · 2022Article
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5 authors.
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Abstract
Prediction of COVID-19 incidence and transmissibility rates are essential to inform disease control policy and allocation of limited resources (especially to hotspots), and also to prepare towards healthcare facilities demand. This study demonstrates the capabilities of nonlinear smooth transition autoregressive (STAR) model for improved forecasting of COVID-19 incidence in the Africa sub-region were investigated. Data used in the study were daily confirmed new cases of COVID-19 from February 25 to August 31, 2020. The results from the study showed the nonlinear STAR-type model with logistic transition function aptly captured the nonlinear dynamics in the data and provided a better fit for the data than the linear model. The nonlinear STAR-type model further outperformed the linear autoregressive model for predicting both in-sample and out-of-sample incidence.
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