The President’s Council of Advisors on Science and Technology (PCAST) has released a report assessing the government’s preparations for the resurgence of the H1N1 influenza this fall.
The report describes a “plausible scenario” in which 30 to 50% of Americans are infected, as many as 1.8 million people are admitted to the hospital, 300,000 patients require fill the available intensive care units, and between 30,000 and 90,000 Americans die from the flu.
An interesting point concerns the timing of vaccinations: the report states that the surge in the flu could begin in September and peak in mid-October, but the vaccines may not be available until mid-October.
The report also recommends changes to the decision-making processes so that a single individual is “responsible for coordinating all policy development for the 2009-H1N1 response; identifying the people, agencies, and processes for making key decisions; guaranteeing that all necessary decisions are made in a timely manner; and presenting recommended courses of action to the President.”
The report is online here.
The article by Brandeau et al. in the same issue presents recommendations for mathematical and simulation models:
1. Include stakeholder input to effectively address the spectrum of relevant real-world planning and response problems.
2. Include a user-friendly interface, the ability to customize model inputs to suit local needs, the ability to quickly and easily perform sensitivity analysis, and provide ongoing user support.
3. Build models that balance simplicity and complexity so that our models both adequately represent real-world scenarios and can be used and interpreted by the intended end users.
4. Include in our models the relevant outcomes, including outcomes that extend beyond those typically considered in cost-effectiveness analyses; these may include timeliness and efficiency of response, resource utilization, evacuation timing, and/or measures related to behavior of responders and the public.
5. Build models that address the fundamental uncertainties in disaster scenarios, including the likelihood and magnitude of an event, operational response capabilities, supply chain capacity and robustness, behavior of responders and the public, and countermeasure effectiveness.
6. In our reports, address the motivation for the study, define critical assumptions, explain the modeling methodology, discuss key sensitivity analyses, make available public-use versions of the model, and identify
all relevant partners in model creation.
The July/August 2009 issue of Medical Decision Making includes an article by Nathaniel Hupert and his colleagues describing a model that determines the expected number of individuals who develop symptomatic inhalational anthrax and require hospital-based intravenous antibiotic treatment. The key parameters in their study are delays in the dispensing of antibiotics and the effectiveness of the antibiotics. They conclude that extending the duration of the dispensing has less impact than delays in the start of dispensing or reductions in antibiotic effectiveness, both of which can dramatically increase the number of people who need to go to the hospital.
The model is a discrete-time model implemented in a Microsoft Excel workbook with a VBA macro.
The Montgomery County, MD, Advanced Practice Center for Public Health Emergency Preparedness and Response and I would like to remind everyone that POD planning tools like the Clinic Planning Model Generator (CPMG) can be used to plan staffing and predict queueing and throughput H1N1 vaccination in PODs and in schools.
Some of these will be very simple – for instance, a school POD (clinic) may have just a vaccination station staffed by nurses. The inputs to the CPMG are the number of students at a school and the number of hours for the vaccination activity. The model will determine how many staff are needed. If the number needed exceeds the number available at the school, then increase the number of hours that the POD will operate.
If you have any questions, let me know at jwh2@umd.edu.
(August 4, 2009) UPDATE: See also the CDC’s list of Tools and Models to Estimate Staffing.
The Advisory Committee on Immunization Practices (ACIP) has developed new new recommendations for vaccine allocation.
The target groups and priorities are different from those in the previously released Guidance on Allocating and Targeting Pandemic Influenza Vaccine.
Although the current version can be modified to take these into account, we will be releasing a new version of our Vaccine Allocation Model with new defaults to reflect the new recommendations.
If you have any questions, please let me know at jwh2@umd.edu.
As part of our collaboration with the Montgomery County, Maryland, Advanced Practice Center for Public Health Emergency Preparedness and Response, we have developed a Vaccine Allocation Model.
The Vaccine Allocation Model is intended to help public health officials determine how many persons in different target groups can receive treatment if the number of vaccinations available is limited. This can be done on a local, state, or national scale.
The Vaccine Allocation Model is a Microsoft Excel workbook that can be used either in the advance planning stages of a vaccination campaign or for support during a vaccination campaign.
It is based on information published in the Guidance on Allocating and Targeting Pandemic Influenza Vaccine.
I would like to thank Rachel Abbey at Montgomery County, Maryland, and Hiro Toiya at the Hawaii State Department of Health for their feedback on draft versions of the model.
To download the model and the user’s guide, visit http://www.isr.umd.edu/Labs/CIM/projects/clinic/vam.html.
Please send any comments or suggestions to me at jwh2@umd.edu.
NYU’s Center for Catastrophe Preparedness & Response has been developing an agent-based disaster simulation model.
The tool is called Planning with Large Agent-Networks against Catastrophes (PLAN C). According to its web site, PLAN C is designed to help “emergency managers, urban planners and public health officials to prepare and evaluate Pareto-optimal plans to respond to urban catastrophic situations.”
The Pareto-optimal part of the description refers to its ability to search for plans that optimize multiple objectives, including the number of casualties, economic impact, and time to recovery. “In this context, planning can be seen as the problem of adjusting the controllable parameters in the interaction between different classes of agents (hospitals, persons, on-site responders, ambulances, etc.) and available resources, in order to moderate the negative consequences of a catastrophic event.”
The 40-page information package available online contains an overview brochure and copies of their publications, including applications to scenarios such as a sarin release, food poisoning, and a smallpox outbreak.
(Thanks to Rachel Abbey for the tip.)
The University of Maryland and the Montgomery County, Maryland, Advanced Practice Center for Public Health Emergency Preparedness and Response are pleased to announce the release of a new version of eMedCheck.
This PDA-based software is designed for screening individuals at a hepatitis A vaccination clinic. The software asks questions about the patient’s medical history and determines whether the patient should be vaccinated. The software tracks the decisions made as well.
Tarrant County (Texas) Public Health tested this for us during a drive-through hepatitis A vaccination clinic on June 20, 2009. We appreciate their collaboration and feedback.
The CDC has a set of resources for planning vaccination clinics. These come from a message from CDC’s Division of State and Local Readiness.
U.S. 2009 H1N1 Vaccine Strategy, U.S. Department of Health and Human Services. A graphical depiction of the predicted waves of the outbreak and the expected time frames for vaccine development, manufacturing, and distribution and administration.
Guidance on priority groups for allocating and targeting pandemic influenza vaccine. (Priority groups for H1N1 have yet to be determined.)
Software and tips to consider when you design your mass influenza vaccination clinic, Michael Washington, CDC, National Immunization Conference, 2007.
Tools to Assist with Vaccination Clinic Planning:
BERM Model.
Clinic Planning Model Generator.
Maxi-Vac.
CDC’s Vaccine Storage and Handling Toolkit.
Safety and Security at Mass Influenza Vaccination Clinics, Wisconsin Department of Health Services. An example of items that need consideration.
The journal IIE Transactions now has a homeland security department. This department intends to publish descriptions of research efforts that develop and test innovative operations research models and methodologies that can help organizations design better homeland security systems, including those for planning, prevention, response, and recovery.
For more information, please see the department web site.
