ArticleJournal of environmental and public health2021
Zero-Inflated Time Series Modelling of COVID-19 Deaths in Ghana.
Article in Journal of environmental and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Recent advancements in integer-valued autoregressive models for count data time series: A comprehensive review.MethodsX · 2026Review
- Analysis of risk factors for recurrence of Budd-Chiari syndrome: A retrospective study with zero-inflated model.Medicine · 2025Observational
- A Comparison Between Markov Switching Zero-Inflated and Hurdle Models for Spatio-Temporal Infectious Disease Counts.Statistics in medicine · 2025Article
- Assessment of Neonatal Mortality and Associated Hospital-Related Factors in Healthcare Facilities Within Sunyani and Sunyani West Municipal Assemblies in Bono Region, Ghana.Health services insights · 2024Article
- No Excess of Mortality from Lung Cancer during the COVID-19 Pandemic in an Area at Environmental Risk: Results of an Explorative Analysis.International journal of environmental research and public health · 2023Observational
- Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data.Journal of healthcare engineering · 2022Article
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Authors and funding
3 authors.
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Abstract
Discrete count time series data with an excessive number of zeros have warranted the development of zero-inflated time series models to incorporate the inflation of zeros and the overdispersion that comes with it. In this paper, we investigated the characteristics of the trend of daily count of COVID-19 deaths in Ghana using zero-inflated models. We envisaged that the trend of COVID-19 deaths per day in Ghana portrays a general increase from the onset of the pandemic in the country to about day 160 after which there is a general decrease onward. We fitted a zero-inflated Poisson autoregressive model and zero-inflated negative binomial autoregressive model to the data in the partial-likelihood framework. The zero-inflated negative binomial autoregressive model outperformed the zero-inflated Poisson autoregressive model. On the other hand, the dynamic zero-inflated Poisson autoregressive model performed better than the dynamic negative binomial autoregressive model. The predicted new death based on the zero-inflated negative binomial autoregressive model indicated that Ghana's COVID-19 death per day will rise sharply few days after 30
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