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.

10.1088/1478-3975/abb115 http://scihub22266oqcxt.onion/10.1088/1478-3975/abb115 32959788!ä!32959788 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32959788&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Phys+Biol 2020 ; 17 (6): 065001 Nephropedia Template TP {{tp|p=32959788|t=2020. Using posterior predictive distributions to analyse epidemic models: COVID-19 in Mexico City |pdf=|usr=}}{{32959788}} gab.com Text Using posterior predictive distributions to analyse epidemic models: COVID-19 in Mexico City JO: Phys Biol http://www.kidney.de/pget.php?&tw=1&pmid=32959788 #FOAvip Epidemiological models usually contain a set of parameters that must be adjusted based on available observations. Once a model has been calibrated, it can be used as a forecasting tool to make predictions and to evaluate contingency plans. It is customary to employ only point estimators of model parameters for such predictions. However, some models may fit the same data reasonably well for a broad range of parameter values, and this flexibility means that predictions stemming from them will vary widely, depending on the particular values employed within the range that gives a good fit. When data are poor or incomplete, model uncertainty widens further. A way to circumvent this problem is to use Bayesian statistics to incorporate observations and use the full range of parameter estimates contained in the posterior distribution to adjust for uncertainties in model predictions. Specifically, given an epidemiological model and a probability distribution for observations, we use the posterior distribution of model parameters to generate all possible epidemic curves, whose information is encapsulated in posterior predictive distributions. From these, one can extract the worst-case scenario and study the impact of implementing contingency plans according to this assessment. We apply this approach to the evolution of COVID-19 in Mexico City and assess whether contingency plans are being successful and whether the epidemiological curve has flattened. Twit Text FOAVip Using posterior predictive distributions to analyse epidemic models: COVID-19 in Mexico City ????Phys Biol http://www.kidney.de/pget.php?&tw=1&pmid=32959788 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32959788 #FOAvip English Wikipedia <ref>{{Cite pmid|32959788}}</ref> Using posterior predictive distributions to analyse epidemic models: COVID-19 in Mexico City #MMPMID32959788 Mena RH; Velasco-Hernandez JX; Mantilla-Beniers NB; Carranco-Sapiens GA; Benet L; Boyer D; Castillo IP Phys Biol 2020[Sep]; 17 (6): 065001 PMID32959788show ga Epidemiological models usually contain a set of parameters that must be adjusted based on available observations. Once a model has been calibrated, it can be used as a forecasting tool to make predictions and to evaluate contingency plans. It is customary to employ only point estimators of model parameters for such predictions. However, some models may fit the same data reasonably well for a broad range of parameter values, and this flexibility means that predictions stemming from them will vary widely, depending on the particular values employed within the range that gives a good fit. When data are poor or incomplete, model uncertainty widens further. A way to circumvent this problem is to use Bayesian statistics to incorporate observations and use the full range of parameter estimates contained in the posterior distribution to adjust for uncertainties in model predictions. Specifically, given an epidemiological model and a probability distribution for observations, we use the posterior distribution of model parameters to generate all possible epidemic curves, whose information is encapsulated in posterior predictive distributions. From these, one can extract the worst-case scenario and study the impact of implementing contingency plans according to this assessment. We apply this approach to the evolution of COVID-19 in Mexico City and assess whether contingency plans are being successful and whether the epidemiological curve has flattened. |*Betacoronavirus[MESH] |*Epidemics/statistics & numerical data[MESH] |Bayes Theorem[MESH] |COVID-19[MESH] |Coronavirus Infections/*epidemiology/mortality[MESH] |Databases, Factual[MESH] |Humans[MESH] |Mathematical Concepts[MESH] |Mexico/epidemiology[MESH] |Models, Biological[MESH] |Models, Statistical[MESH] |Pandemics[MESH] |Pneumonia, Viral/*epidemiology/mortality[MESH] |Probability[MESH] |SARS-CoV-2[MESH] |Time Factors[MESH]Using posterior predictive distributions to analyse epidemic models: C(epmc).pdf DeepDyve Pubget Overpricing