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10.3390/biology9090299 http://scihub22266oqcxt.onion/10.3390/biology9090299 32962157!7563214!32962157     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       Biology+(Basel) 2020 ; 9 (9): ä Nephropedia Template TP {{tp|p=32962157|t=2020. Pandaesim: An Epidemic Spreading Stochastic Simulator |pdf=|usr=}}{{32962157}} gab.com Text Pandaesim: An Epidemic Spreading Stochastic Simulator JO: Biology (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=32962157 #FOAvip Many methods have been used to model epidemic spreading. They include ordinary differential equation systems for globally homogeneous environments and partial differential equation systems to take into account spatial localisation and inhomogeneity. Stochastic differential equations systems have been used to model the inherent stochasticity of epidemic spreading processes. In our case study, we wanted to model the numbers of individuals in different states of the disease, and their locations in the country. Among the many existing methods we used our own variant of the well known Gillespie stochastic algorithm, along with the sub-volumes method to take into account the spatial localisation. Our algorithm allows us to easily switch from stochastic discrete simulation to continuous deterministic resolution using mean values. We applied our approaches on the study of the Covid-19 epidemic in France. The stochastic discrete version of Pandaesim showed very good correlations between the simulation results and the statistics gathered from hospitals, both on day by day and on global numbers, including the effects of the lockdown. Moreover, we have highlighted interesting differences in behaviour between the continuous and discrete methods that may arise in some particular conditions. Twit Text FOAVip Pandaesim: An Epidemic Spreading Stochastic Simulator ????Biology (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=32962157 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32962157 #FOAvip English Wikipedia <ref>{{Cite pmid|32962157}}</ref> Pandaesim: An Epidemic Spreading Stochastic Simulator #MMPMID32962157 Amar P Biology (Basel) 2020[Sep]; 9 (9): ä PMID32962157show ga Many methods have been used to model epidemic spreading. They include ordinary differential equation systems for globally homogeneous environments and partial differential equation systems to take into account spatial localisation and inhomogeneity. Stochastic differential equations systems have been used to model the inherent stochasticity of epidemic spreading processes. In our case study, we wanted to model the numbers of individuals in different states of the disease, and their locations in the country. Among the many existing methods we used our own variant of the well known Gillespie stochastic algorithm, along with the sub-volumes method to take into account the spatial localisation. Our algorithm allows us to easily switch from stochastic discrete simulation to continuous deterministic resolution using mean values. We applied our approaches on the study of the Covid-19 epidemic in France. The stochastic discrete version of Pandaesim showed very good correlations between the simulation results and the statistics gathered from hospitals, both on day by day and on global numbers, including the effects of the lockdown. Moreover, we have highlighted interesting differences in behaviour between the continuous and discrete methods that may arise in some particular conditions. äPandaesim: An Epidemic Spreading Stochastic Simulator (epmc).pdf DeepDyve Pubget Overpricing