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10.1007/s40808-021-01136-1 http://scihub22266oqcxt.onion/10.1007/s40808-021-01136-1 33655020!7906761!33655020     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Model+Earth+Syst+Environ 2022 ; 8 (1): 961-966 Nephropedia Template TP {{tp|p=33655020|t=2022. Modelling COVID-19 incidence in the African sub-region using smooth transition autoregressive model |pdf=|usr=}}{{33655020}} gab.com Text Modelling COVID-19 incidence in the African sub-region using smooth transition autoregressive model JO: Model Earth Syst Environ http://www.kidney.de/pget.php?&tw=1&pmid=33655020 #FOAvip 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. Twit Text FOAVip Modelling COVID-19 incidence in the African sub-region using smooth transition autoregressive model ????Model Earth Syst Environ http://www.kidney.de/pget.php?&tw=1&pmid=33655020 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33655020 #FOAvip English Wikipedia <ref>{{Cite pmid|33655020}}</ref> Modelling COVID-19 incidence in the African sub-region using smooth transition autoregressive model #MMPMID33655020 Aidoo EN; Ampofo RT; Awashie GE; Appiah SK; Adebanji AO Model Earth Syst Environ 2022[]; 8 (1): 961-966 PMID33655020show ga 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. äModelling COVID-19 incidence in the African sub-region using smooth tr(epmc).pdf DeepDyve Pubget Overpricing