Ming Yi1, Achla Marathe2. 1. School of Economics, Huazhong University of Science and Technology, Wuhan, Hubei, China. 2. Institute and Department of Agricultural and Applied Economics, Virginia Tech, Blacksburg, VA, USA. Electronic address: amarathe@vbi.vt.edu.
Abstract
OBJECTIVES: To develop a framework to objectively measure the degree of fairness of any allocation rule aimed at distributing a limited stockpile of vaccines to contain the spread of influenza. METHODS: The trade-off between the efficiency and fairness of allocation strategies was demonstrated through an illustrative simulation study of an influenza epidemic in Southwestern Virginia. A Susceptible-Exposed-Infectious-Recovered model was used to represent the disease progression within the host. RESULTS: Our findings showed that among all the criteria considered here, the household size (largest first) combined with age (youngest first)-based strategy leads to the best outcome. At 80% fairness, highest efficiency can be achieved but in order to be 100% fair, disease prevalence will have to rise by approximately 1.5%. CONCLUSIONS: This research provides a framework to objectively determine the degree of fairness of vaccine allocation strategies.
OBJECTIVES: To develop a framework to objectively measure the degree of fairness of any allocation rule aimed at distributing a limited stockpile of vaccines to contain the spread of influenza. METHODS: The trade-off between the efficiency and fairness of allocation strategies was demonstrated through an illustrative simulation study of an influenza epidemic in Southwestern Virginia. A Susceptible-Exposed-Infectious-Recovered model was used to represent the disease progression within the host. RESULTS: Our findings showed that among all the criteria considered here, the household size (largest first) combined with age (youngest first)-based strategy leads to the best outcome. At 80% fairness, highest efficiency can be achieved but in order to be 100% fair, disease prevalence will have to rise by approximately 1.5%. CONCLUSIONS: This research provides a framework to objectively determine the degree of fairness of vaccine allocation strategies.
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Authors: M Elizabeth Halloran; Neil M Ferguson; Stephen Eubank; Ira M Longini; Derek A T Cummings; Bryan Lewis; Shufu Xu; Christophe Fraser; Anil Vullikanti; Timothy C Germann; Diane Wagener; Richard Beckman; Kai Kadau; Chris Barrett; Catherine A Macken; Donald S Burke; Philip Cooley Journal: Proc Natl Acad Sci U S A Date: 2008-03-10 Impact factor: 11.205
Authors: John S Brownstein; Shuyu Chu; Achla Marathe; Madhav V Marathe; Andre T Nguyen; Daniela Paolotti; Nicola Perra; Daniela Perrotta; Mauricio Santillana; Samarth Swarup; Michele Tizzoni; Alessandro Vespignani; Anil Kumar S Vullikanti; Mandy L Wilson; Qian Zhang Journal: JMIR Public Health Surveill Date: 2017-11-01