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10.1155/2021/5543977 http://scihub22266oqcxt.onion/10.1155/2021/5543977 34012470!8086432!34012470     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Environ+Public+Health 2021 ; 2021 (ä): 5543977 Nephropedia Template TP {{tp|p=34012470|t=2021. Zero-Inflated Time Series Modelling of COVID-19 Deaths in Ghana |pdf=|usr=}}{{34012470}} gab.com Text Zero-Inflated Time Series Modelling of COVID-19 Deaths in Ghana JO: J Environ Public Health http://www.kidney.de/pget.php?&tw=1&pmid=34012470 #FOAvip 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(th) November 2020 and drastically fall just as in the observed data. Twit Text FOAVip Zero-Inflated Time Series Modelling of COVID-19 Deaths in Ghana ????J Environ Public Health http://www.kidney.de/pget.php?&tw=1&pmid=34012470 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34012470 #FOAvip English Wikipedia <ref>{{Cite pmid|34012470}}</ref> Zero-Inflated Time Series Modelling of COVID-19 Deaths in Ghana #MMPMID34012470 Tawiah K; Iddrisu WA; Asampana Asosega K J Environ Public Health 2021[]; 2021 (ä): 5543977 PMID34012470show ga 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(th) November 2020 and drastically fall just as in the observed data. |*Models, Statistical[MESH] |Binomial Distribution[MESH] |COVID-19/*mortality[MESH] |Ghana/epidemiology[MESH] |Humans[MESH] |Interrupted Time Series Analysis/*methods[MESH] |Mortality/trends[MESH] |Poisson Distribution[MESH] |Reproducibility of Results[MESH]Zero-Inflated Time Series Modelling of COVID-19 Deaths in Ghana (epmc).pdf DeepDyve Pubget Overpricing