Wednesday, January 16, 2008

Pandemic Preparedness and Response Models

Last month, Gerardo Chowell (School of Human Evolution and Social Change, Arizona State University) and Hiroshi Nishiura (University of Utrecht) published their work on quantifying the transmission of a pandemic influenza. Although we have learned a lot from the 1918 Spanish flu pandemic, we still lack the understanding of some basic probabilities necessary to plan an effective response to a pandemic. For example, if one has the flu, what is the probability that the disease will manifest itself clinically? Once the disease manifest itself clinically in an individual, what is the probability of dying from the flu. In the case of bird flu, the virus can spread across different host species, what is the probability of transmission? Right now we don't have all these answers and we can only make assumptions. This is what makes it very difficult for responding authorities to have an effective response plan to a pandemic, like the H5N1 potential pandemic influenza. Additionally, current intervention optimization tools are limited by the fact that they can't maximize realism, generality and precision at the same time. Public health authorities and planners are in need of an optimal and realistic combination of these properties in order to form the appropriate and timely response plan. Is closing schools the right strategy? When do we issue a quarantine? etc. etc.

At minimum, a pandemic planning model (and supporting tools) should:
  1. allow investigation of time-dependent variables (such as; surge capacity, incidence, height of a pandemic peak, availability of drugs or vaccination)
  2. be adjustable to support different local conditions and assumptions, localities and countries will vary in their preparedness and response plans
  3. be publicly available

5 comments:

  1. Hi!

    I humbly suggest you take a look at what a self-organised bunch of "newshounds" are doing over at newfluwiki2.com and other forum sites.

    (There's also a dedicated wikipage with current summary at fluwikie.com.)

    Newshounds comb internet news-sites, even in foreign languages (they help each other use automatic translation tools), and come up with summaries, maps and thoughtful and exploratory commentary.

    Someone over at CDC called them "they who bring us the news".

    The suggestion to get in touch goes to both sides: http://newfluwiki2.com/showComment.do?commentId=80837

    FWIW. :-)

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  2. http://newfluwiki2.com/showComment.do?commentId=82351

    How could InSTEDD help newshounds? What should be ready when there's a surge in news?

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  3. Absolutely fascinating! The ramifications, the possibilities for really assisting local public health astounding.

    Knowing when to impose a quarantine in a given area lifesaving. When the all-clear could be given could also save lives as people can move about to restock or go back to work....days, weeks could be saved.

    Delivery of goods could be adapted to route around epidemic outbreaks.

    Amazing tool. Kudos and thanks.

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  4. MIDAS is actually a collection of research teams and of models and methods. I've been a MIDAS Co-I since the beginning (as part of what was the Johns Hopkins team and is now the Pittsburgh team). We've used our GeoGraphs spatial agent-based computational laboratory to build and run a spatial network pandemic model (see sections 2.3-2.6) to rank cities by pandemic risks and to optimize the geographic allocation of limited intervention resources (questions in section 2.5).

    I am especially interested in collaborating with Google.com/clouds, Google.org/InSTEDD, and other colleagues to help identify especially vulnerable populations and communities.

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  5. Dear Catherine, I can summarize our effort in that we are bringing a social network approach to the biosurveillance problem and taking a hybrid surveillance approach: indicator-based and event based surveillance.

    I'm very interested in your thoughts and help given your expertise in this field so we can better address the problem.

    Taha

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