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10.3390/ijerph17093119 http://scihub22266oqcxt.onion/10.3390/ijerph17093119 32365775!7246727!32365775     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       Int+J+Environ+Res+Public+Health 2020 ; 17 (9): ä Nephropedia Template TP {{tp|p=32365775|t=2020. A Model for the Spread of Infectious Diseases in a Region |pdf=|usr=}}{{32365775}} gab.com Text A Model for the Spread of Infectious Diseases in a Region JO: Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32365775 #FOAvip In understanding the dynamics of the spread of an infectious disease, it is important to understand how a town's place in a network of towns within a region will impact how the disease spreads to that town and from that town. In this article, we take a model for the spread of an infectious disease in a single town and scale it up to simulate a region containing multiple towns. The model is validated by looking at how adding additional towns and commuters influences the outbreak in a single town. We then look at how the centrality of a town within a network influences the outbreak. Our main finding is that the commuters coming into a town have a greater effect on whether an outbreak will spread to a town than the commuters going out. The findings on centrality of a town and how it influences an outbreak could potentially be used to help influence future policy and intervention strategies such as school closure policies. Twit Text FOAVip A Model for the Spread of Infectious Diseases in a Region ????Int J Environ Res Public Health http://www.kidney.de/pget.php?&tw=1&pmid=32365775 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32365775 #FOAvip English Wikipedia <ref>{{Cite pmid|32365775}}</ref> A Model for the Spread of Infectious Diseases in a Region #MMPMID32365775 Hunter E; Namee BM; Kelleher JD Int J Environ Res Public Health 2020[Apr]; 17 (9): ä PMID32365775show ga In understanding the dynamics of the spread of an infectious disease, it is important to understand how a town's place in a network of towns within a region will impact how the disease spreads to that town and from that town. In this article, we take a model for the spread of an infectious disease in a single town and scale it up to simulate a region containing multiple towns. The model is validated by looking at how adding additional towns and commuters influences the outbreak in a single town. We then look at how the centrality of a town within a network influences the outbreak. Our main finding is that the commuters coming into a town have a greater effect on whether an outbreak will spread to a town than the commuters going out. The findings on centrality of a town and how it influences an outbreak could potentially be used to help influence future policy and intervention strategies such as school closure policies. |*Communicable Diseases/transmission[MESH] |Cities[MESH] |Disease Outbreaks[MESH] |Humans[MESH] |Models, Theoretical[MESH]A Model for the Spread of Infectious Diseases in a Region (epmc).pdf DeepDyve Pubget Overpricing