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10.1016/j.epidem.2021.100441 http://scihub22266oqcxt.onion/10.1016/j.epidem.2021.100441 33667878!7899011!33667878     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 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Epidemics 2021 ; 35 (ä): 100441 Nephropedia Template TP {{tp|p=33667878|t=2021. The impact of mobility network properties on predicted epidemic dynamics in Dhaka and Bangkok |pdf=|usr=}}{{33667878}} gab.com Text The impact of mobility network properties on predicted epidemic dynamics in Dhaka and Bangkok JO: Epidemics http://www.kidney.de/pget.php?&tw=1&pmid=33667878 #FOAvip Properties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV-2) propagating on these commuting networks, and find that differences in network structure between the two cities drive divergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics. Twit Text FOAVip The impact of mobility network properties on predicted epidemic dynamics in Dhaka and Bangkok ????Epidemics http://www.kidney.de/pget.php?&tw=1&pmid=33667878 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33667878 #FOAvip English Wikipedia <ref>{{Cite pmid|33667878}}</ref> The impact of mobility network properties on predicted epidemic dynamics in Dhaka and Bangkok #MMPMID33667878 Brown TS; Engo-Monsen K; Kiang MV; Mahmud AS; Maude RJ; Buckee CO Epidemics 2021[Jun]; 35 (ä): 100441 PMID33667878show ga Properties of city-level commuting networks are expected to influence epidemic potential of cities and modify the speed and spatial trajectory of epidemics when they occur. In this study, we use aggregated mobile phone user data to reconstruct commuter mobility networks for Bangkok (Thailand) and Dhaka (Bangladesh), two megacities in Asia with populations of 16 and 21 million people, respectively. We model the dynamics of directly-transmitted infections (such as SARS-CoV-2) propagating on these commuting networks, and find that differences in network structure between the two cities drive divergent predicted epidemic trajectories: the commuting network in Bangkok is composed of geographically-contiguous modular communities and epidemic dispersal is correlated with geographic distance between locations, whereas the network in Dhaka has less distinct geographic structure and epidemic dispersal is less constrained by geographic distance. We also find that the predicted dynamics of epidemics vary depending on the local topology of the network around the origin of the outbreak. Measuring commuter mobility, and understanding how commuting networks shape epidemic dynamics at the city level, can support surveillance and preparedness efforts in large cities at risk for emerging or imported epidemics. |*Epidemics[MESH] |*Transportation[MESH] |Bangladesh[MESH] |COVID-19/epidemiology/transmission[MESH] |Cities/epidemiology[MESH] |Communicable Diseases/*epidemiology/transmission[MESH] |Disease Outbreaks[MESH] |Geography[MESH] |Humans[MESH] |Models, Theoretical[MESH] |SARS-CoV-2[MESH]The impact of mobility network properties on predicted epidemic dynami(epmc).pdf DeepDyve Pubget Overpricing