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10.3906/sag-2005-378 http://scihub22266oqcxt.onion/10.3906/sag-2005-378 32530587!7991878!32530587     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Turk+J+Med+Sci 2021 ; 51 (1): 16-27 Nephropedia Template TP {{tp|p=32530587|t=2021. Projecting the course of COVID-19 in Turkey: A probabilistic modeling approach |pdf=|usr=}}{{32530587}} gab.com Text Projecting the course of COVID-19 in Turkey: A probabilistic modeling approach JO: Turk J Med Sci http://www.kidney.de/pget.php?&tw=1&pmid=32530587 #FOAvip BACKGROUND/AIM: The COVID-19 pandemic originated in Wuhan, China, in December 2019 and became one of the worst global health crises ever. While struggling with the unknown nature of this novel coronavirus, many researchers and groups attempted to project the progress of the pandemic using empirical or mechanistic models, each one having its drawbacks. The first confirmed cases were announced early in March, and since then, serious containment measures have taken place in Turkey. MATERIALS AND METHODS: Here, we present a different approach, a Bayesian negative binomial multilevel model with mixed effects, for the projection of the COVID-19 pandemic and we apply this model to the Turkish case. The model source code is available at https:// github.com/kansil/covid-19. We predicted the confirmed daily cases and cumulative numbers from June 6th to June 26th with 80%, 95%, and 99% prediction intervals (PI). RESULTS: Our projections showed that if we continued to comply with the measures and no drastic changes were seen in diagnosis or management protocols, the epidemic curve would tend to decrease in this time interval. Also, the predictive validity analysis suggests that the proposed model projections should have a PI around 95% for the first 12 days of the projections. CONCLUSION: We expect that drastic changes in the course of COVID-19 in Turkey will cause the model to suffer in predictive validity, and this can be used to monitor the epidemic. We hope that the discussion on these projections and the limitations of the epidemiological forecasting will be beneficial to the medical community, and policy makers. Twit Text FOAVip Projecting the course of COVID-19 in Turkey: A probabilistic modeling approach ????Turk J Med Sci http://www.kidney.de/pget.php?&tw=1&pmid=32530587 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32530587 #FOAvip English Wikipedia <ref>{{Cite pmid|32530587}}</ref> Projecting the course of COVID-19 in Turkey: A probabilistic modeling approach #MMPMID32530587 Acar AC; Er AG; Burduroglu HC; Sulku SN; Aydin Son Y; Akin L; Unal S Turk J Med Sci 2021[Feb]; 51 (1): 16-27 PMID32530587show ga BACKGROUND/AIM: The COVID-19 pandemic originated in Wuhan, China, in December 2019 and became one of the worst global health crises ever. While struggling with the unknown nature of this novel coronavirus, many researchers and groups attempted to project the progress of the pandemic using empirical or mechanistic models, each one having its drawbacks. The first confirmed cases were announced early in March, and since then, serious containment measures have taken place in Turkey. MATERIALS AND METHODS: Here, we present a different approach, a Bayesian negative binomial multilevel model with mixed effects, for the projection of the COVID-19 pandemic and we apply this model to the Turkish case. The model source code is available at https:// github.com/kansil/covid-19. We predicted the confirmed daily cases and cumulative numbers from June 6th to June 26th with 80%, 95%, and 99% prediction intervals (PI). RESULTS: Our projections showed that if we continued to comply with the measures and no drastic changes were seen in diagnosis or management protocols, the epidemic curve would tend to decrease in this time interval. Also, the predictive validity analysis suggests that the proposed model projections should have a PI around 95% for the first 12 days of the projections. CONCLUSION: We expect that drastic changes in the course of COVID-19 in Turkey will cause the model to suffer in predictive validity, and this can be used to monitor the epidemic. We hope that the discussion on these projections and the limitations of the epidemiological forecasting will be beneficial to the medical community, and policy makers. |Bayes Theorem[MESH] |COVID-19/*epidemiology[MESH] |Epidemiologic Methods[MESH] |Forecasting[MESH] |Humans[MESH] |Models, Statistical[MESH] |Pandemics/*statistics & numerical data[MESH] |Probability[MESH]Projecting the course of COVID-19 in Turkey: A probabilistic modeling (epmc).pdf DeepDyve Pubget Overpricing