Optimizing the Allocation of COVID-19 Testing & Vaccine Resources in Florida

Authors: Ran Tao*, University of South Florida, Joni Downs Firat, University of South Florida, Theresa Beckie, University of South Florida, He Zhang, University of South Florida, Yuzhou Chen, University of South Florida, Elizabeth Dunn, University of South Florida
Topics: Spatial Analysis & Modeling, Geography and Urban Health, Geographic Information Science and Systems
Keywords: COVID-19, optimization, allocation, accessibility, inequality
Session Type: Virtual Paper
Day: 4/10/2021
Start / End Time: 4:40 PM / 5:55 PM
Room: Virtual 8
Presentation File: No File Uploaded

Massive and rapid testing for SARS-CoV-2 nucleic acid or antigen is crucial to halting transmission and death due to the pandemic. However, there exist severe inadequacy and inequality issues with respect to COVID-19 testing in Florida. First, the positive rate is still well above the general benchmark of adequate testing suggested by WHO. Second, according to our preliminary study, certain groups of people are facing disadvantages in getting access to the testing sites, e.g. the rural residents, the people without cars, and the elderly. In this study, we will develop a mathematical model to optimize the allocation of COVID-19 testing resources, in order to maximize the increase of accessibility to COVID-19 testing per person with a given monetary budget. Working closely with our community partners from FDOH and local emergency planners, our model considers a list of realistic solutions, such as setting up new testing sites at open space or existing facilities, dispatching testing vehicles to operate at disadvantaged neighborhoods, nursing homes and assisted living facilities. The outcome will be a mix of strategies that balance maximizing cost efficiency and promoting equal access. Considering that the vaccine will probably be approved in the near future and the distribution mechanism shares many similarities with the testing resources, the other objective of this study is to modify the allocation model to be ready for massive vaccine inoculation in the near future.

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