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.

10.1016/j.apgeog.2020.102375 http://scihub22266oqcxt.onion/10.1016/j.apgeog.2020.102375 33318717!7724310!33318717     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33318717&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 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Appl+Geogr 2021 ; 126 (ä): 102375 Nephropedia Template TP {{tp|p=33318717|t=2021. Regionalization for infection control: An algorithm for delineating containment zones considering the regularity of human mobility |pdf=|usr=}}{{33318717}} gab.com Text Regionalization for infection control: An algorithm for delineating containment zones considering the regularity of human mobility JO: Appl Geogr http://www.kidney.de/pget.php?&tw=1&pmid=33318717 #FOAvip Restricting human movement to decrease contact probability and frequency helps mitigate large-scale epidemics. Movement-based zoning can be implemented to delineate the boundaries for movement restrictions. Previous studies used network community detection methods, which capture cohesive within-region movements, to delineate containment zones. However, most people usually travel and spend most of their time in several fixed locations, which implies that an infected person could transmit the pathogens to only a specific group of people with whom s/he usually has a contact in frequently-visited locations. Existing network community detection methods cannot reflect the regularity of the flow of people; thus, this study aims to use land-use patterns to reflect trip purposes to measure the regularity of human mobility. We propose a novel network community detection method, the Human Mobility Regularity-based Zoning (HuMoRZ) algorithm, to delineate containment zones incorporating mobility regularity. The Taipei metropolitan area in Taiwan is used to demonstrate the feasibility of the proposed algorithm. The spatial diffusion of an emerging respiratory disease, novel influenza A/H1N1, is simulated for comparing three different quarantine zoning systems: (1) a minimum zoning unit, (2) optimal zoning without considering mobility regularity, and (3) optimal zoning considering mobility regularity. Two epidemiological performance indicators are used to compare simulation results: namely, the accumulated infected number (AN) on the 30th day, reflecting the severity of an epidemic, and the critical time (CT), the moment at which half of the population becomes infected, measuring the diffusion speed of an epidemic. To measure the variety of different facility types within a containment zone, we further use Shannon's entropy scores, representing a self-contained zone, and the boxplot of all zones' entropy scores, reflecting geospatial homogeneity of life functions across zones. Our results suggest that containment zones that incorporate mobility regularity could significantly delay the epidemic peak and critical time and decrease the severity of an epidemic. The zoning patterns proposed in our algorithm could also allow for more life functions in a zone and more evenly distributed life resources across zones than those of zones generated by other methods. These findings could provide insightful implications for fighting the COVID-19 pandemic. Twit Text FOAVip Regionalization for infection control: An algorithm for delineating containment zones considering the regularity of human mobility ????Appl Geogr http://www.kidney.de/pget.php?&tw=1&pmid=33318717 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33318717 #FOAvip English Wikipedia <ref>{{Cite pmid|33318717}}</ref> Regionalization for infection control: An algorithm for delineating containment zones considering the regularity of human mobility #MMPMID33318717 Kuo FY; Wen TH Appl Geogr 2021[Jan]; 126 (ä): 102375 PMID33318717show ga Restricting human movement to decrease contact probability and frequency helps mitigate large-scale epidemics. Movement-based zoning can be implemented to delineate the boundaries for movement restrictions. Previous studies used network community detection methods, which capture cohesive within-region movements, to delineate containment zones. However, most people usually travel and spend most of their time in several fixed locations, which implies that an infected person could transmit the pathogens to only a specific group of people with whom s/he usually has a contact in frequently-visited locations. Existing network community detection methods cannot reflect the regularity of the flow of people; thus, this study aims to use land-use patterns to reflect trip purposes to measure the regularity of human mobility. We propose a novel network community detection method, the Human Mobility Regularity-based Zoning (HuMoRZ) algorithm, to delineate containment zones incorporating mobility regularity. The Taipei metropolitan area in Taiwan is used to demonstrate the feasibility of the proposed algorithm. The spatial diffusion of an emerging respiratory disease, novel influenza A/H1N1, is simulated for comparing three different quarantine zoning systems: (1) a minimum zoning unit, (2) optimal zoning without considering mobility regularity, and (3) optimal zoning considering mobility regularity. Two epidemiological performance indicators are used to compare simulation results: namely, the accumulated infected number (AN) on the 30th day, reflecting the severity of an epidemic, and the critical time (CT), the moment at which half of the population becomes infected, measuring the diffusion speed of an epidemic. To measure the variety of different facility types within a containment zone, we further use Shannon's entropy scores, representing a self-contained zone, and the boxplot of all zones' entropy scores, reflecting geospatial homogeneity of life functions across zones. Our results suggest that containment zones that incorporate mobility regularity could significantly delay the epidemic peak and critical time and decrease the severity of an epidemic. The zoning patterns proposed in our algorithm could also allow for more life functions in a zone and more evenly distributed life resources across zones than those of zones generated by other methods. These findings could provide insightful implications for fighting the COVID-19 pandemic. äRegionalization for infection control: An algorithm for delineating co(epmc).pdf DeepDyve Pubget Overpricing