What is the relationship between Interdisciplinary Solutions, LLC and the University of Pittsburgh Medical Center's Center for BioSecurity?

The UPMC Center for BioSecurity is not officially associated with Interdisciplinary Solutions, LLC. Center staff advised in Panalysis' development for the purpose of better carrying the Center's mission to "lessen the illness, death, and civil disruption that would follow large-scale epidemics". We are grateful for their advice and support and strongly believe in the Center and its mission.

I've seen other models, some distributed by government agencies and some that are privately held. How is Panalysis different?

A quick litmus test to determine if a pandemic projection model works is to double the attack rate - say from 30% to 60%. If the number of fatalities doubles in line with the attack rate, the model may be conceptually flawed. That is to say the model may be creating fatalities based on pre-defined constants and does not take into account a hospital's or region's capacity to handle incoming patient flow. Therefore, all associated projected shortages and supply requirements could also be wrong.

Another test is the epidemiological curve. If the curve changes but total fatalities do not change the model likely distributes a pre-assigned number of total fatalities without regard to hospital capacity.

Furthermore, many of the other models we've seen do not show their code or methods of calculation. We question what these models actually do "behind the scenes". Additionally, many of the models we've seen do not produce results in readily manipulable formats.

Panalysis uses a core data approach in which virtually every calculation is interlinked. Our results do not depend on constants and ratios that the user cannot see. Our approach is based on the fundamental principles of operations management such as process analysis, utilization, capacity and demand.

If you have questions about a specific model, please contact us and we will give you an honest appraisal of its strengths and weaknesses.

What about computational epidemiological models and models that demonstrate disease spread through the dynamic use of mathematics and statistics?

We are compatible with these types of models and welcome their use with Panalysis!

In many ways we view ourselves as the practical application that makes computational epidemiological tools more useful and more powerful for the betterment of public health. Because Panalysis models the dynamic relationship between patient surge and limited resources with respect to capacity, we can work with computational epidemiologists to measure just how effective concepts like social distancing actually are.

Is Panalysis Scalable?

Yes. Not only can macro data inputs produce macro snapshots of regions but also, hospital data can be added together to build macro snapshots with granularity down to individual hospital size. See the Governments section of our Consulting page or ping us in the Panalysis Users Group for more details on these techniques.

Why is augmentation and resource allocation important?

We estimate that effective augmentation strategies in tandem with proper resource allocation can reduce fatalities by over 40% during a severe pandemic.

Do I have to use an augmentation algorithm? I want to consider results without any type of augmentation.

No, the user does not have to use augmentation of any type and there are two different ways to turn augmentation off.

Method 1: On worksheet 5, the Bed Capacity and Ventilator Augmentation Worksheet, set all inputs to 0% and then use the recommended solution.

Method 2: Use the manual solution and on the manual solution inputs section, set Non-ICU and ICU bed capacity to the bed capacity under non-pandemic conditions and then set Ventilators Freed to 0 on a week by week basis.

How do I turn off staff augmentation settings?

On Worksheet 4, set both the staff available and hours available inputs to pre-pandemic levels.

How well does the recommended solution on the resource allocation algorithm work?

The recommended solution never allocates fewer than the optimal number of beds or ventilators required. We recently built into the recommended solution a cross-augmentation function that pools resources and allocates Non-ICU beds to the ICU and vice versa as needed. On occasion, the recommended solution allocates more resources than optimal when one of the theaters overcompensates for shortages in the other. The user can adjust for such situations through use of the manual solution.

As an analytical tool, use of the recommended, non-prioritized and manual solutions in tandem work extremely well and allows the user to more quickly execute augmentation strategies that consider patient care, economic and operational constraints than they could through manual analysis alone.

Why do increases in certain hospital capacity inputs during non-pandemic conditions increase the number of fatalities during pandemic conditions?

Because our core data method links virtually every calculation to one another.

For example, if the user raises bed capacity under non-pandemic conditions, without accordingly adjusting the number of ventilators available, they are actually raising the required number of ventilators needed but are not providing the hospital or region with the additional resources to compensate. It would be as if a hospital added more beds with the intent of admitting more patients but did not procure any of the other resources needed to care for the additional patients. Panalysis picks up these shortages and patients that need a ventilator but do not have one become fatalities.

Do other shortages, like triage shortages or staff shortages contribute to preventable fatalities?

When a pandemic occurs, such shortages contribute to fatalities. Operations managers refer to areas where capacity cannot meet demand as bottlenecks. The analytical user can use PanalysisBasic to identify other bottlenecks, like triage shortages and adjust fatalities accordingly.

Customized versions of Panalysis can be created to automatically account for supply, staff, triage and other bottlenecks.



"The in-hospital fatality rate is linked to the magnitude of bed and ventilator shortages. And the magnitude of shortages is related to the magnitude of the surge wave which, in turn, is a function of both the attack rate and the shape of the epidemiological curve (distribution of casualties)"

Thus "Two scenarios with the same number of patients may produce very different magnitudes of shortages depending on the distribution of the patients. Thus, attempts to estimate the consequences of a future pandemic without considering the shape of the surge wave can be misleading."

--- From "Panalysis: New Spreadsheet-Based Tool for Pandemic Planning"


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