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Effective, Robust Design of Community Mitigation for Pandemic Influenza: A Systematic Examination of Proposed US Guidance

Laidback Al

Well-known member
Davey VJ, Glass RJ, Min HJ, Beyeler WE, Glass LM 2008 Effective, Robust Design of Community Mitigation for Pandemic Influenza: A Systematic Examination of Proposed US Guidance. PLoS ONE 3(7): e2606 doi:10.1371/journal.pone.0002606

Effective, Robust Design of Community Mitigation for Pandemic Influenza: A Systematic Examination of Proposed US Guidance
Victoria J. Davey, Robert J. Glass, H. Jason Min, Walter E. Beyeler, Laura M. Glass
Abstract
Background
The US government proposes pandemic influenza mitigation guidance that includes isolation and antiviral treatment of ill persons, voluntary household member quarantine and antiviral prophylaxis, social distancing of individuals, school closure, reduction of contacts at work, and prioritized vaccination. Is this the best strategy combination? Is choice of this strategy robust to pandemic uncertainties? What are critical enablers of community resilience?
Methods and Findings
We systematically simulate a broad range of pandemic scenarios and mitigation strategies using a networked, agent-based model of a community of explicit, multiply-overlapping social contact networks. We evaluate illness and societal burden for alterations in social networks, illness parameters, or intervention implementation. For a 1918-like pandemic, the best strategy minimizes illness to <1% of the population and combines network-based (e.g. school closure, social distancing of all with adults' contacts at work reduced), and case-based measures (e.g. antiviral treatment of the ill and prophylaxis of household members). We find choice of this best strategy robust to removal of enhanced transmission by the young, additional complexity in contact networks, and altered influenza natural history including extended viral shedding. Administration of age-group or randomly targeted 50% effective pre-pandemic vaccine with 7% population coverage (current US H5N1 vaccine stockpile) had minimal effect on outcomes. In order, mitigation success depends on rapid strategy implementation, high compliance, regional mitigation, and rigorous rescinding criteria; these are the critical enablers for community resilience.
Conclusions
Systematic evaluation of feasible, recommended pandemic influenza interventions generally confirms the US community mitigation guidance yields best strategy choices for pandemic planning that are robust to a wide range of uncertainty. The best strategy combines network- and case-based interventions; network-based interventions are paramount. Because strategies must be applied rapidly, regionally, and stringently for greatest benefit, preparation and public education is required for long-lasting, high community compliance during a pandemic.

full article at: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0002606

pdf file at: http://www.plosone.org/article/fetc....1371/journal.pone.0002606&representation=PDF


Full article and discussion in this thread: http://www.flutrackers.com/forum/showthread.php?t=76931
 
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Re: Effective, Robust Design of Community Mitigation for Pandemic Influenza: A Systematic Examination of Proposed US Guidance

no interest ?

looks important
 
Re: Effective, Robust Design of Community Mitigation for Pandemic Influenza: A Systematic Examination of Proposed US Guidance

especially now
 
Re: Effective, Robust Design of Community Mitigation for Pandemic Influenza: A Systematic Examination of Proposed US Guidance

excellent find, gs. I'm keeping that page open so I can reread it later when my brain is a little more functional and then pass it on.

Thanks, this deserves a bump. Our readers and members that may be involved with containment on their community level should find much here to consider. Every individual can use this to anticipate what some local containment strategies may look like and plan accordingly.

I must admit, though, I find the assumed 90% compliance rate much higher than I anticipated; and much higher than I expect in many areas.
 
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