Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1017/dmp.2020.323 http://scihub22266oqcxt.onion/10.1017/dmp.2020.323 32900401!7642915!32900401     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Disaster+Med+Public+Health+Prep 2022 ; 16 (1): 223-231 Nephropedia Template TP {{tp|p=32900401|t=2022. Modeling of COVID-19 Outbreak Indicators in China Between January and June |pdf=|usr=}}{{32900401}} gab.com Text Modeling of COVID-19 Outbreak Indicators in China Between January and June JO: Disaster Med Public Health Prep http://www.kidney.de/pget.php?&tw=1&pmid=32900401 #FOAvip OBJECTIVES: The objective of this study is to compare the various nonlinear and time series models in describing the course of the coronavirus disease 2019 (COVID-19) outbreak in China. To this aim, we focus on 2 indicators: the number of total cases diagnosed with the disease, and the death toll. METHODS: The data used for this study are based on the reports of China between January 22 and June 18, 2020. We used nonlinear growth curves and some time series models for prediction of the number of total cases and total deaths. The determination coefficient (R2), mean square error (MSE), and Bayesian Information Criterion (BIC) were used to select the best model. RESULTS: Our results show that while the Sloboda and ARIMA (0,2,1) models are the most convenient models that elucidate the cumulative number of cases; the Lundqvist-Korf model and Holt linear trend exponential smoothing model are the most suitable models for analyzing the cumulative number of deaths. Our time series models forecast that on 19 July, the number of total cases and total deaths will be 85,589 and 4639, respectively. CONCLUSION: The results of this study will be of great importance when it comes to modeling outbreak indicators for other countries. This information will enable governments to implement suitable measures for subsequent similar situations. Twit Text FOAVip Modeling of COVID-19 Outbreak Indicators in China Between January and June ????Disaster Med Public Health Prep http://www.kidney.de/pget.php?&tw=1&pmid=32900401 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32900401 #FOAvip English Wikipedia <ref>{{Cite pmid|32900401}}</ref> Modeling of COVID-19 Outbreak Indicators in China Between January and June #MMPMID32900401 Celik S; Ankarali H; Pasin O Disaster Med Public Health Prep 2022[Feb]; 16 (1): 223-231 PMID32900401show ga OBJECTIVES: The objective of this study is to compare the various nonlinear and time series models in describing the course of the coronavirus disease 2019 (COVID-19) outbreak in China. To this aim, we focus on 2 indicators: the number of total cases diagnosed with the disease, and the death toll. METHODS: The data used for this study are based on the reports of China between January 22 and June 18, 2020. We used nonlinear growth curves and some time series models for prediction of the number of total cases and total deaths. The determination coefficient (R2), mean square error (MSE), and Bayesian Information Criterion (BIC) were used to select the best model. RESULTS: Our results show that while the Sloboda and ARIMA (0,2,1) models are the most convenient models that elucidate the cumulative number of cases; the Lundqvist-Korf model and Holt linear trend exponential smoothing model are the most suitable models for analyzing the cumulative number of deaths. Our time series models forecast that on 19 July, the number of total cases and total deaths will be 85,589 and 4639, respectively. CONCLUSION: The results of this study will be of great importance when it comes to modeling outbreak indicators for other countries. This information will enable governments to implement suitable measures for subsequent similar situations. |*COVID-19/epidemiology[MESH] |Bayes Theorem[MESH] |China/epidemiology[MESH] |Disease Outbreaks[MESH] |Humans[MESH]Modeling of COVID-19 Outbreak Indicators in China Between January and (epmc).pdf DeepDyve Pubget Overpricing