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10.1016/j.mbs.2021.108621 http://scihub22266oqcxt.onion/10.1016/j.mbs.2021.108621 33915160!8076816!33915160     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       Math+Biosci 2021 ; 337 (ä): 108621 Nephropedia Template TP {{tp|p=33915160|t=2021. Optimal allocation of limited vaccine to control an infectious disease: Simple analytical conditions |pdf=|usr=}}{{33915160}} gab.com Text Optimal allocation of limited vaccine to control an infectious disease: Simple analytical conditions JO: Math Biosci http://www.kidney.de/pget.php?&tw=1&pmid=33915160 #FOAvip When allocating limited vaccines to control an infectious disease, policy makers frequently have goals relating to individual health benefits (e.g., reduced morbidity and mortality) as well as population-level health benefits (e.g., reduced transmission and possible disease eradication). We consider the optimal allocation of a limited supply of a preventive vaccine to control an infectious disease, and four different allocation objectives: minimize new infections, deaths, life years lost, or quality-adjusted life years (QALYs) lost due to death. We consider an SIR model with n interacting populations, and a single allocation of vaccine at time 0. We approximate the model dynamics to develop simple analytical conditions characterizing the optimal vaccine allocation for each objective. We instantiate the model for an epidemic similar to COVID-19 and consider n=2 population groups: one group (individuals under age 65) with high transmission but low mortality and the other group (individuals age 65 or older) with low transmission but high mortality. We find that it is optimal to vaccinate younger individuals to minimize new infections, whereas it is optimal to vaccinate older individuals to minimize deaths, life years lost, or QALYs lost due to death. Numerical simulations show that the allocations resulting from our conditions match those found using much more computationally expensive algorithms such as exhaustive search. Sensitivity analysis on key parameters indicates that the optimal allocation is robust to changes in parameter values. The simple conditions we develop provide a useful means of informing vaccine allocation decisions for communicable diseases. Twit Text FOAVip Optimal allocation of limited vaccine to control an infectious disease: Simple analytical conditions ????Math Biosci http://www.kidney.de/pget.php?&tw=1&pmid=33915160 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33915160 #FOAvip English Wikipedia <ref>{{Cite pmid|33915160}}</ref> Optimal allocation of limited vaccine to control an infectious disease: Simple analytical conditions #MMPMID33915160 Rao IJ; Brandeau ML Math Biosci 2021[Jul]; 337 (ä): 108621 PMID33915160show ga When allocating limited vaccines to control an infectious disease, policy makers frequently have goals relating to individual health benefits (e.g., reduced morbidity and mortality) as well as population-level health benefits (e.g., reduced transmission and possible disease eradication). We consider the optimal allocation of a limited supply of a preventive vaccine to control an infectious disease, and four different allocation objectives: minimize new infections, deaths, life years lost, or quality-adjusted life years (QALYs) lost due to death. We consider an SIR model with n interacting populations, and a single allocation of vaccine at time 0. We approximate the model dynamics to develop simple analytical conditions characterizing the optimal vaccine allocation for each objective. We instantiate the model for an epidemic similar to COVID-19 and consider n=2 population groups: one group (individuals under age 65) with high transmission but low mortality and the other group (individuals age 65 or older) with low transmission but high mortality. We find that it is optimal to vaccinate younger individuals to minimize new infections, whereas it is optimal to vaccinate older individuals to minimize deaths, life years lost, or QALYs lost due to death. Numerical simulations show that the allocations resulting from our conditions match those found using much more computationally expensive algorithms such as exhaustive search. Sensitivity analysis on key parameters indicates that the optimal allocation is robust to changes in parameter values. The simple conditions we develop provide a useful means of informing vaccine allocation decisions for communicable diseases. |*Mass Vaccination/methods/standards[MESH] |*Models, Theoretical[MESH] |*Viral Vaccines/administration & dosage/supply & distribution[MESH] |Adult[MESH] |Age Factors[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |COVID-19/prevention & control[MESH] |Epidemics/*prevention & control[MESH] |Humans[MESH] |Middle Aged[MESH]Optimal allocation of limited vaccine to control an infectious disease(epmc).pdf DeepDyve Pubget Overpricing