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Panalysis: A New Spreadsheet-Based Tool for Pandemic Planning

Mark N. Abramovich, Eric S. Toner, Jason Matheny
Date posted:
March 04, 2008
Publication type:

Biosecur Bioterror 2008;6(1):78-92

Mary Ann Liebert, Inc.
Available on publisher's site
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Full article on publisher's site: PDF


Publicly available influenza modeling tools are of limited use to hospitals and local communities in planning for a severe pandemic. We developed Panalysis, a new tool to estimate the likely healthcare consequences of a pandemic and to aid hospitals in the development of mitigation and response strategies. By way of example, we demonstrate how Panalysis can be used to plan for a 1918-like flu pandemic. We discuss potential future applications of this tool.

Effective planning for an influenza pandemic at the hospital or community level requires the ability to project the impact of an outbreak on available local medical resources, such as inpatient hospital beds and mechanical ventilators. To make such projections, the planner must (1) make assumptions about the specific nature of the outbreak (e.g., attack rate, severity, duration, and epidemiologic curve), and (2) gather or estimate relevant information about the hospital’s capacity, capabilities, and resources. Since the specifics of a future pandemic are unknown and could vary over a wide range, different scenarios must be considered. The projected number of patients must be matched, day-by-day or week-by-week, with the resources projected to be available. These projections require thousands of individual calculations. Therefore, effective planning is facilitated by use of a computerized modeling tool that can quickly and accurately calculate the consequences of different scenarios.

In addition to projecting the effects of an outbreak, such a tool also could be used to evaluate potential mitigation strategies and to guide response. The value of this tool would be expected to increase with the number of users, by providing a common modeling platform with a uniform set of input and result fields. Users would be able to compare assumptions, data inputs, and results. In addition, if the tool were widely used, a future web-based central database of compiled anonymous data could enable the development and use of benchmarks to further aid pandemic planning



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