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.

10.1073/pnas.2011656117 http://scihub22266oqcxt.onion/10.1073/pnas.2011656117 32913057!7533653!32913057     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Proc+Natl+Acad+Sci+U+S+A 2020 ; 117 (39): 24180-24187 Nephropedia Template TP {{tp|p=32913057|t=2020. Spatial heterogeneity can lead to substantial local variations in COVID-19 timing and severity |pdf=|usr=}}{{32913057}} gab.com Text Spatial heterogeneity can lead to substantial local variations in COVID-19 timing and severity JO: Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=32913057 #FOAvip Standard epidemiological models for COVID-19 employ variants of compartment (SIR or susceptible-infectious-recovered) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations of unrestricted COVID-19 diffusion in 19 US cities, we conclude that heterogeneity in population distribution can have large impacts on local pandemic timing and severity, even when aggregate behavior at larger scales mirrors a classic SIR-like pattern. Impacts observed include severe local outbreaks with long lag time relative to the aggregate infection curve, and the presence of numerous areas whose disease trajectories correlate poorly with those of neighboring areas. A simple catchment model for hospital demand illustrates potential implications for health care utilization, with substantial disparities in the timing and extremity of impacts even without distancing interventions. Likewise, analysis of social exposure to others who are morbid or deceased shows considerable variation in how the epidemic can appear to individuals on the ground, potentially affecting risk assessment and compliance with mitigation measures. These results demonstrate the potential for spatial network structure to generate highly nonuniform diffusion behavior even at the scale of cities, and suggest the importance of incorporating such structure when designing models to inform health care planning, predict community outcomes, or identify potential disparities. Twit Text FOAVip Spatial heterogeneity can lead to substantial local variations in COVID-19 timing and severity ????Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=32913057 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32913057 #FOAvip English Wikipedia <ref>{{Cite pmid|32913057}}</ref> Spatial heterogeneity can lead to substantial local variations in COVID-19 timing and severity #MMPMID32913057 Thomas LJ; Huang P; Yin F; Luo XI; Almquist ZW; Hipp JR; Butts CT Proc Natl Acad Sci U S A 2020[Sep]; 117 (39): 24180-24187 PMID32913057show ga Standard epidemiological models for COVID-19 employ variants of compartment (SIR or susceptible-infectious-recovered) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations of unrestricted COVID-19 diffusion in 19 US cities, we conclude that heterogeneity in population distribution can have large impacts on local pandemic timing and severity, even when aggregate behavior at larger scales mirrors a classic SIR-like pattern. Impacts observed include severe local outbreaks with long lag time relative to the aggregate infection curve, and the presence of numerous areas whose disease trajectories correlate poorly with those of neighboring areas. A simple catchment model for hospital demand illustrates potential implications for health care utilization, with substantial disparities in the timing and extremity of impacts even without distancing interventions. Likewise, analysis of social exposure to others who are morbid or deceased shows considerable variation in how the epidemic can appear to individuals on the ground, potentially affecting risk assessment and compliance with mitigation measures. These results demonstrate the potential for spatial network structure to generate highly nonuniform diffusion behavior even at the scale of cities, and suggest the importance of incorporating such structure when designing models to inform health care planning, predict community outcomes, or identify potential disparities. |Betacoronavirus[MESH] |COVID-19[MESH] |Cities/epidemiology[MESH] |Coronavirus Infections/*epidemiology/prevention & control/*transmission[MESH] |Delivery of Health Care[MESH] |Demography[MESH] |Health Status Disparities[MESH] |Humans[MESH] |Models, Statistical[MESH] |Pandemics/prevention & control[MESH] |Pneumonia, Viral/*epidemiology/prevention & control/*transmission[MESH] |SARS-CoV-2[MESH] |Social Networking[MESH]Spatial heterogeneity can lead to substantial local variations in COVI(epmc).pdf DeepDyve Pubget Overpricing