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                <text>Objectives&#13;
Ghana’s quest to reduce neonatal mortality, in hospital facilities and communities, continues to be a nightmare. The pursuit of achieving healthy lives and well-being for neonates as enshrined in Sustainable Development Goal three lingered in challenging hospital facilities and communities. Notwithstanding that, there have been increasing efforts in that direction. This study examines the contributing factors that hinder the fight against neonatal mortality in all hospital facilities in the Sunyani and Sunyani West Municipal Assemblies in Bono Region, Ghana.&#13;
Methods&#13;
The study utilized neonatal mortality data consisting of neonatal deaths, structural facility related variables, medical human resources, types of hospital facilities and natal care. The data was collected longitudinally from 2014 to 2019. These variables were analysed using the negative binomial hurdle regression (NBH) model to determine factors …</text>
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                <text>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 …</text>
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