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Model of H5N1 Pandemic

Mellie

Well-known member
http://www.nigms.nih.gov/News/Results/FluModel040306

Computer Model Examines Strategies to Mitigate Potential U.S. Flu Pandemic


FOR IMMEDIATE RELEASE:
April 3, 2006
Contact:
Emily Carlson
301-496-7301
carlsone@nigms.nih.gov
U.S. Pandemic Flu Model

These simulations show the hypothetical spread of a moderately contagious pandemic flu in the United States. Each dot represents a Census tract and changes color from green to red as more people in that tract become infected. The dots change back to green as people recover. With no intervention (top), the pandemic peaks around day 85. With the distribution of 10 million doses per week of a vaccine that?s poorly matched to the emerging virus (bottom), the pandemic peaks around day 108. View with RealPlayer or Windows Media Player.


All graphics are reproduced from supplementary information on this work published in the Proceedings of the National Academy of Sciences and are courtesy of Ira M. Longini, Jr., Ph.D., of the Fred Hutchinson Cancer Research Center and the University of Washington in Seattle.


If pandemic flu were to emerge in the United States, what interventions might slow its spread and minimize the impact? With support from the National Institutes of Health (NIH), researchers from the Fred Hutchinson Cancer Research Center in Seattle, Wash., and the Los Alamos National Laboratory have used computer models to suggest possible answers. The findings appear in the April 11, 2006, issue of the Proceedings of the National Academy of Sciences and will be available in the online edition the week of April 3.
By developing a model that represents the U.S. population and tests different properties of a potential pandemic flu virus, the researchers evaluated the effectiveness of different intervention strategies. They found that, depending on the contagiousness of the virus, a variety of approaches could reduce the number of cases to less than that of an annual flu season.
?Preparing for a potential pandemic is tremendously challenging, given the potential scope and the large number of unknowns,? said NIH Director Elias A. Zerhouni, M.D. ?The best approach is to use all of the tools available to us, including computer modeling. By predicting the impact of intervention strategies, these models can help health officials and policymakers plan for a real pandemic.?
The recent modeling work is part of an ongoing research program called the Models of Infectious Disease Agent Study (MIDAS), supported by NIH?s National Institute of General Medical Sciences (NIGMS). Researchers in the network develop computer models to better understand the spread of infectious diseases, whether they occur naturally or deliberately. With growing concerns that the H5N1 strain of the avian flu virus, initially found in birds throughout Southeast Asia, could eventually be transmitted easily between people, the research network has been modeling pandemic flu in different parts of the world.
?The MIDAS researchers previously developed models of a potential pandemic flu outbreak in Thailand and surrounding areas that showed containment at the source is feasible,? explained Jeremy M. Berg, Ph.D., NIGMS director. ?But we need to consider the possibility that if the outbreak isn?t contained, it could quickly spread globally.?
Using data from the 2000 U.S. Census and the U.S. Department of Transportation, the researchers developed a model that represents the demographics and travel patterns of 281 million people living in the United States. They also incorporated information about the potential virus based on previous flu pandemics, including different assumptions about its possible contagiousness (but not its potential effects on mortality). The researchers then introduced a small number of hypothetical travelers, who are infected but not yet symptomatic, arriving daily at 14 major U.S. international airports. With these assumptions in place, the scientists simulated a virtual outbreak on high-performance computers at the Los Alamos National Laboratory.
?The goal for the U.S. modeling project was to determine how to slow spread long enough so that a well-matched vaccine could be developed and distributed,? said the research team?s leader, Ira M. Longini, Jr., Ph.D., a biostatistician at the Fred Hutchinson Cancer Research Center and the University of Washington School of Public Health and Community Medicine. An additional guideline was to reduce the number of overall cases to or below 10 percent of the population, the average percentage reported during an annual flu season.
To identify such measures, the researchers tested different interventions: distributing antiviral treatments to infected individuals and others near them to reduce symptoms and susceptibility; vaccinating people, possibly children first, with either one or two shots of a vaccine not well matched to the strain that may emerge; social distancing, such as restricting travel and quarantining households; and closing schools.
The results showed that with no intervention a pandemic flu with low contagiousness could peak after 117 days and infect about 33 percent of the U.S. population. A highly contagious virus could peak after 64 days and infect about 54 percent of people.
The researchers then compared what might happen in scenarios involving the use of different interventions. When the simulated virus was less contagious, the three most effective single measures included distributing several million courses of antiviral treatment to targeted groups seven days after a pandemic alert, school closures, and vaccinating 10 million people per week with one dose of a poorly matched vaccine. The results also showed that vaccinating school children first is more effective than random vaccination when the vaccine supply is limited. Regardless of contagiousness, social distancing measures alone had little effect.
But when the virus was highly contagious, all single intervention strategies left nearly half the population infected. In this instance, the only measures that reduced the number of cases to below the annual flu rate involved a combination of at least three different interventions, including a minimum of 182 million courses of antiviral treatment.
While the results are specific to the United States, the researchers said the general findings can apply to other developed countries and could aid the drafting of preparedness plans both here and abroad. Because computer models can?t capture all the complexities of real communities and outbreaks, MIDAS scientists will continue to refine their models and test different scenarios as new information becomes available.
Other members of the Longini team who contributed to the recent findings include Los Alamos National Laboratory scientists Timothy C. Germann, Ph.D.; Kai Kadau, Ph.D.; and Catherine A. Macken, Ph.D.
###​
To arrange an interview with NIGMS Director Jeremy M. Berg, Ph.D., contact the NIGMS Office of Communications and Public Liaison at 301-496-7301. To arrange an interview with Ira M. Longini, Jr., Ph.D., please call 206-667-2896. More information about MIDAS is available at http://www.nigms.nih.gov/Initiatives/MIDAS/.
NIGMS (http://www.nigms.nih.gov/) is one of 27 components of the National Institutes of Health, U.S. Department of Health and Human Services. The NIGMS mission is to support basic biomedical research that lays the foundation for advances in disease diagnosis, treatment, and prevention.
The National Institutes of Health (NIH)--The Nation's Medical Research Agency--includes 27 Institutes and Centers and is a component of the U. S. Department of Health and Human Services. It is the primary Federal agency for conducting and supporting basic, clinical, and translational medical research, and it investigates the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit http://www.nih.gov.
 
Abstract describing model of H5N1 Pandemic

Abstract describing model of H5N1 Pandemic





Mitigation strategies for pandemic influenza in the United States

[SIZE=-1]( antiviral agents | infectious diseases | simulation modeling | social network dynamics | vaccines )[/SIZE]
Timothy C. Germann *
dagger.gif
, Kai Kadau *, Ira M. Longini Jr.
Dagger.gif
, and Catherine A. Macken *
*Los Alamos National Laboratory, Los Alamos, NM 87545; and
Dagger.gif
Program of Biostatistics and Biomathematics, Fred Hutchinson Cancer Research Center and Department of Biostatistics, School of Public Health and Community Medicine, University of Washington, Seattle, WA 98109


Communicated by G. Balakrish Nair, International Centre for Diarrhoeal Disease Research Bangladesh, Dhaka, Bangladesh, February 16, 2006 (received for review January 10, 2006)
Recent human deaths due to infection by highly pathogenic (H5N1) avian influenza A virus have raised the specter of a devastating pandemic like that of 1917-1918, should this avian virus evolve to become readily transmissible among humans. We introduce and use a large-scale stochastic simulation model to investigate the spread of a pandemic strain of influenza virus through the U.S. population of 281 million individuals for R0 (the basic reproductive number) from 1.6 to 2.4. We model the impact that a variety of levels and combinations of influenza antiviral agents, vaccines, and modified social mobility (including school closure and travel restrictions) have on the timing and magnitude of this spread. Our simulations demonstrate that, in a highly mobile population, restricting travel after an outbreak is detected is likely to delay slightly the time course of the outbreak without impacting the eventual number ill. For R0 < 1.9, our model suggests that the rapid production and distribution of vaccines, even if poorly matched to circulating strains, could significantly slow disease spread and limit the number ill to <10% of the population, particularly if children are preferentially vaccinated. Alternatively, the aggressive deployment of several million courses of influenza antiviral agents in a targeted prophylaxis strategy may contain a nascent outbreak with low R0, provided adequate contact tracing and distribution capacities exist. For higher R0, we predict that multiple strategies in combination (involving both social and medical interventions) will be required to achieve similar limits on illness rates.

Author contributions: T.C.G., K.K., I.M.L., and C.A.M. designed research, performed research, contributed new reagents/analytic tools, analyzed data, and wrote the paper.
Conflict of interest statement: No conflicts declared.
dagger.gif
To whom correspondence should be addressed.
Timothy C. Germann, E-mail: tcg@lanl.gov
[SIZE=-1]www.pnas.org/cgi/doi/10.1073/pnas.0601266103[/SIZE]
 
Los Alamos Website on Model of H5N1 Pandemic

Los Alamos Website on Model of H5N1 Pandemic

From Los Alamos website:
http://www.lanl.gov/news/index.php?fuseaction=home.story&story_id=8171
Avian flu modeled on supercomputer, explores vaccine and isolation options for thwarting a pandemic

Contact: Nancy Ambrosiano, nwa@lanl.gov, (505) 667-0471 (04-220)

LOS ALAMOS, N.M., April 3, 2006 -- Using supercomputers to respond to a potential national health emergency, scientists have developed a simulation model that makes stark predictions about the possible future course of an avian influenza pandemic, given today?s environment of world-wide connectivity. The research, by a team of scientists from Los Alamos National Laboratory in New Mexico, the University of Washington and the Fred Hutchinson Cancer Research Center in Seattle, is presented in the Proceedings of the National Academy of Science online the week of April 3-7, and in the print issue of April 11.

The large-scale, stochastic simulation model examines the nationwide spread of a pandemic influenza virus strain, such as an evolved avian H5N1 virus, should it become transmissible human-to-human. The simulation rolls out a city- and census-tract-level picture of the spread of infection through a synthetic population of 281 million people over the course of 180 days, and examines the impact of interventions, from antiviral therapy to school closures and travel restrictions, as the vaccine industry struggles to catch up with the evolving virus.

?Based on the present work ... we believe that a large stockpile of avian influenza-based vaccine containing potential pandemic influenza antigens, coupled with the capacity to rapidly make a better-matched vaccine based on human strains, would be the best strategy to mitigate pandemic influenza,? say the authors, Timothy Germann, Kai Kadau, Ira Longini and Catherine Macken.

Longini is a biostatistician with the Fred Hutchinson Cancer Research Center and the University of Washington, while the rest of the team is at Los Alamos. Their collaboration is supported by grants from the Department of Homeland Security and the National Institute of General Medical Sciences MIDAS (Models of Infectious Disease Agent Study) program.

?It's probably not going to be practical to contain a potential pandemic by merely trying to limit contact between people (such as by travel restrictions, quarantine or even closing schools), but we find that these measures are useful in buying time to produce and distribute sufficient quantities of vaccine and antiviral drugs,? said Germann of Los Alamos? Applied Physics Division.

?Based on our results, combinations of mitigation strategies such as stockpiling vaccines or antiviral agents, along with social distancing measures could be particularly effective in slowing pandemic flu spread in the U.S.,? added Longini.

The results show that advance preparation of a modestly effective vaccine in large quantities appears to be preferable to waiting for the development of a well-matched vaccine that may not become available until a pandemic has already reached the United States.

?Because it is currently impossible to predict which of the diverging strains of avian H5N1 influenza virus is most likely to adapt to human transmission, studies of broadly cross-reactive avian-influenza based vaccines with even modest immunogenicity in humans are important,? said Macken, an influenza researcher in the Los Alamos Theoretical Division. Ideally, both vaccine strategies would be done in parallel: Stockpile a modestly effective vaccine to use while the better-matched one is being developed, the authors suggest.

How it all computes

The computer simulation models a synthetic population that matches U.S. census demographics and worker mobility data by randomly assigning the simulated individuals to households, workplaces, schools, and the like. Department of Transportation travel data is used to model long-distance trips during the course of the simulation, realistically capturing the spread of the pandemic virus by airplane and other passenger travel across the United States.

?In the highly mobile U.S. population, travel restrictions alone will not be enough to stop the spread; a mixture of many mitigation strategies is more likely to be effective than a few strictly enforced ones,? said Kadau, also of Los Alamos? Theoretical Division.

The model of disease transmission involves probabilities that any two people in a community will meet on any given day in any one of a number of settings, such as home or workplace. Thus, simulated disease transmission is more likely for two people in the same household and less likely for two people who have less in common. ?So we are only computing the probability of any person becoming infected on any given day, and a roll of the dice is needed to decide whether they are infected or not,? said Germann.

Other elements of randomness modify the simulated disease course. A significant fraction of infected people (33 percent in the present model) never develop clinical symptoms, although they are themselves infectious. In addition, the durations of the incubation and infectious periods can vary and are randomly chosen from distribution functions for each individual, involving more throws of the virtual dice.

?Computer models serve as virtual laboratories where researchers can study how infectious diseases might spread and what intervention strategies may lessen the impact of a real outbreak,? said Jeremy M. Berg, director of the National Institute of General Medical Sciences. ?This new work exemplifies the power of such models and could aid policymakers and health officials as they plan for a possible future pandemic.?

The pandemic simulation model has been implemented in the Laboratory?s celebrated Scalable Parallel Short-range Molecular dynamics (SPaSM) large-scale simulation platform developed for the nuclear weapons program. It runs on the Los Alamos supercomputer known as Pink, a 1,024-node (2,048 processor) LinuxBIOS/BProc ?Science Appliance? running Clustermatic 3, the largest single-system image Linux cluster in the world. Pink's nodes have dual 2.4 GHz Intel Xeon processors (Pentium 4) with 2 gigabytes of memory per node. The purchase of the Science Appliance was funded by the National Nuclear Security Administration's Advanced Simulation and Computing program.Pink is currently a system software research platform, a science appliance cluster concept invented at Los Alamos in the Computer and Computational Science Division. Los Alamos has four science appliance clusters in use at this time for a variety of projects across the full range of Laboratory mission areas.

Images and Quicktime video of the computer simulation are available at http://www.lanl.gov/news/images/avianflu.shtml online.

The text of the NIGMS press release can be accessed at http://www.nigms.nih.gov/News/Results/FluModel040306 .

Los Alamos National Laboratory is operated by the University of California for the National Nuclear Security Administration of the U.S. Department of Energy and works in partnership with NNSA's Sandia and Lawrence Livermore national laboratories to support NNSA in its mission.
Los Alamos enhances global security by ensuring the safety and reliability of the U.S. nuclear deterrent, developing technologies to reduce threats from weapons of mass destruction, and solving problems related to defense, energy, environment, infrastructure, health and national security concerns.
 
Re: Model of H5N1 Pandemic

Just using this site as my file cabinet, so to speak. :D

I was wanting this info all in one place for easy access!

I'll be off and away mostly for the next week, hopefully checking in time to time.

Mellie
 
Re: Model of H5N1 Pandemic

Teach 'em good, Mellie! :D :smartass:

This is the news we've been waiting for, and it's oh say "ugly"?

The relevant part to me, the layperson:
>The results showed that with no intervention a pandemic flu with low contagiousness could peak after 117 days and infect about 33 percent of the U.S. population. A highly contagious virus could peak after 64 days and infect about 54 percent of people.
>The researchers then compared what might happen in scenarios involving the use of different interventions. When the simulated virus was less contagious, the three most effective single measures included distributing several million courses of antiviral treatment to targeted groups seven days after a pandemic alert, school closures, and vaccinating 10 million people per week with one dose of a poorly matched vaccine. The results also showed that vaccinating school children first is more effective than random vaccination when the vaccine supply is limited. Regardless of contagiousness, social distancing measures alone had little effect.
>But when the virus was highly contagious, all single intervention strategies left nearly half the population infected. In this instance, the only measures that reduced the number of cases to below the annual flu rate involved a combination of at least three different interventions, including a minimum of 182 million courses of antiviral treatment.

The US has at maximum, max, max, maximum, 15 million cartons of Tamiflu, and maybe 30 million courses of rimantadine/amantadine. That's 45 million courses (assuming one carton of Tamiflu per event, when we know the minimum is two and the probable dosage is imo four cartons, which will reduce the total from 15 to 4 million courses).

How does one spell the word, "Screwed"? Is that in all caps?

Hope you picked up on the fact that social distancing alone is a worthless endeavor. Either one hunkers down and ducks for 2 months to 4 months...per wave (this is again per wave, not in total) or one gets slammed with a 33% or 54% chance of exposure. The virus, in its kindest moments, kills only 20% of those infected, and in its more vigorous moments, which appear to me 70% of the time, kills 65-80% of those infected.

These are the facts folks.

As a moment of comment, I'm pleased that finally the MIDAS Study did what it was supposed to have done; it's late in this announcement by well over a year; but this is a significant service to the people of the USA and the first world. Thank you to the entire MIDAS staff.
 
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