tetano
Editor, Senior Moderator
Emerg Infect Dis. 2017 Jun;23(6):914-921. doi: 10.3201/eid2306.161417.
[h=1]Stockpiling Ventilators for Influenza Pandemics.[/h] Huang HC, Araz OM, Morton DP, Johnson GP, Damien P, Clements B, Meyers LA.
[h=3]Abstract[/h] In preparing for influenza pandemics, public health agencies stockpile critical medical resources. Determining appropriate quantities and locations for such resources can be challenging, given the considerable uncertainty in the timing and severity of future pandemics. We introduce a method for optimizing stockpiles of mechanical ventilators, which are critical for treating hospitalized influenza patients in respiratory failure. As a case study, we consider the US state of Texas during mild, moderate, and severe pandemics. Optimal allocations prioritize local over central storage, even though the latter can be deployed adaptively, on the basis of real-time needs. This prioritization stems from high geographic correlations and the slightly lower treatment success assumed for centrally stockpiled ventilators. We developed our model and analysis in collaboration with academic researchers and a state public health agency and incorporated it into a Web-based decision-support tool for pandemic preparedness and response.
[h=4]KEYWORDS:[/h] Texas; United States; Ventilators; influenza; optimization; pandemic; viruses
PMID: 28518041 DOI: 10.3201/eid2306.161417
Free full text
[h=1]Stockpiling Ventilators for Influenza Pandemics.[/h] Huang HC, Araz OM, Morton DP, Johnson GP, Damien P, Clements B, Meyers LA.
[h=3]Abstract[/h] In preparing for influenza pandemics, public health agencies stockpile critical medical resources. Determining appropriate quantities and locations for such resources can be challenging, given the considerable uncertainty in the timing and severity of future pandemics. We introduce a method for optimizing stockpiles of mechanical ventilators, which are critical for treating hospitalized influenza patients in respiratory failure. As a case study, we consider the US state of Texas during mild, moderate, and severe pandemics. Optimal allocations prioritize local over central storage, even though the latter can be deployed adaptively, on the basis of real-time needs. This prioritization stems from high geographic correlations and the slightly lower treatment success assumed for centrally stockpiled ventilators. We developed our model and analysis in collaboration with academic researchers and a state public health agency and incorporated it into a Web-based decision-support tool for pandemic preparedness and response.
[h=4]KEYWORDS:[/h] Texas; United States; Ventilators; influenza; optimization; pandemic; viruses
PMID: 28518041 DOI: 10.3201/eid2306.161417
Free full text