capable of controlling the earliest stages of a pandemic, assessing how sure we can be of the outcome of a containment strategy, and quantifying the resources which would be necessary to deliver these measures.

We have undertaken modeling to address such questions as part of a study called Models of Infectious Disease Agents Study,(MIDAS), funded by the National Institute of General Medical Sciences in NIH. The project includes three basic research groups, one at Virginia Tech led by Dr. Steve Eubank, one at Emory led by Dr. Ira Longini, and one at Johns Hopkins led by Dr. Donald Burke. I am part of the latter study, which includes researchers at Imperial College. I will talk about the work I have been doing with Don's group in modeling pandemic spread in Thailand. Ira Longini has been working on the same topic, and we hope that both these studies will be published in the next few months.

I will talk a bit about the structure of our model and its frailties and assumptions. A key difference between this model and many infectious disease models in the past is its scale. Our approach is to simulate entire countries as realistically as possible, so our computationally intensive simulation models a population of over 85 million. We tried to capture social structure by modeling individuals and households, because—as with many other infectious diseases—households are key location for transmission of influenza. Transmission of influenza also occurs within peer groups at schools and workplaces, and a separate component of the model captures those. We also know that community-based journeys to shops and other locations in the country are key to the longer-range spread of the pathogen. The model captures those by modeling a random contact process between individuals in the population that depends on distance.

We can think of the population as a set of individuals residing within households, in which adults travel to workplaces and children attend schools. An important aspect of this model is data we have collected on how far people in a household typically travel to schools or work. Thus the model tries to capture both the social structure and the real geography of populations—both of which are key to understanding and predicting the spatial and geographic spread of an emergent strain.

How do we simulate the population? We use probably the most detailed dataset available on global population density, called Landscan, which is now being used in Iraq and was used for the tsunami relief effort. This dataset, prepared by Oak Ridge National Laboratory, gives sub-kilometer data on population density globally. We clearly do not know precisely how many people live on every square kilometer of the planet, but the Landscan predicted density figures have been validated using remote-sensing as well as census data. One output of the simulation model we have constructed is maps of population density, which use colour to represent reas of the modeled region in which infection is present or trestment being undertaken..

Capturing age and household structure are also critical for realistically modeling influenza transmission, and the model incorporates data we have collected for Thailand. We chose Thailand not because we felt it was the country of highest risk for emergence of a pandemic, but because it is representative of Southeast Asia, and data on population structure and movements in that country are available. However, we intend to generalize the model to examine Vietnam, among other countries.

The model also incorporates school and workplace demographics, including the distribution of school and worksplace sizes. Dr. Derek Cummings at Johns Hopkins collected these data, which are also important for realistically describing disease transmission.

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