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work as evidence that human-generated greenhouse gases played a beneficial role for several thousand years by keeping the earth's climate more hospitable than it would otherwise have been. Others might counter that if so few humans with relatively primitive technologies were able to alter the course of climate so significantly, then we have reason to be concerned about the current rise of greenhouse gases to unparalleled concentrations at unprecedented rates.

The rapid warming of the past century is probably destined to persist for at least 200 years, until the economically accessible fossil fuels become scarce. Once that happens, the earth's climate should begin to cool gradually as the deep ocean slowly absorbs the pulse of excess CO2 from human activities. Whether global climate will cool enough to produce the long-overdue glaciation or remain warm enough to avoid that fate is impossible to predict. ®

Plagues and Peoples. William McNeill. Doubleday, 1976.

Ice Ages: Solving the Mystery. John Imbrie and Katherine Palmer Imbrie. Enslow, 1979. Guns, Germs, and Steel: The Fates of Human Societies. Jared Diamond. W. W. Norton, 1999. Earth's Climate: Past and Future. William F. Ruddiman. W. H. Freeman, 2001. The Anthropogenic Greenhouse Era Began Thousands of Years Ago. William F. Ruddiman in Climatic Change, Vol. 61, No. 3, pages 261-293; 2003.

Deforesting the Earth: From Prehistory to Global Crisis. Michael A. Williams. University of Chicago Press, 2003.

Plows, Plagues, and Petroleum: How Humans Took Control of Climate. William F. Ruddiman. Princeton University Press (in press).



SIMULATING the social interactions that spread disease shows the course a pathogen might take from an individual (circled) through a population.

Infected individual

Infected individual

W^i uppose terrorists were to release plague in Chicago, and health officials, faced with limited resources and personnel, had to quickly choose the most effective response. Would mass administration of antibiotics be the best way to halt an outbreak? Or mass quarantines? What if a chance to nip a global influenza pandemic in the bud meant sending national stockpiles of antiviral drugs to Asia where a deadly new flu strain was said to be emerging? If the strategy succeeded, a worldwide crisis would be averted; if it failed, the donor countries would be left with less protection.

Public health officials have to make choices that could mean life or death for thousands, even millions, of people, as well as massive economic and social disruption. And history offers them only a rough guide. Methods that eradicated smallpox in African villages in the 1970s, for example, might not be the most effective tactics against smallpox released in a U.S. city in the 21st century. To identify the best responses under a variety of conditions in advance of disasters, health officials need a laboratory where "what if" scenarios can be tested as realistically as possible. That is why our group at Los Alamos National Laboratory (LANL) set out to build EpiSims, the largest individual-based epidemiology simulation model ever created.

Modeling the interactions of each individual in a population allows us to go beyond estimating the number of people likely to be infected; it lets us simulate the paths a disease would take through the population and thus where the outbreak could be intercepted most effectively.

The networks that support everyday life and provide employment, transportation infrastructure, necessities and luxuries are the same ones that infectious diseases exploit to spread among human hosts. By modeling this social network in fine detail, we can understand its structure and how to alter it to disrupt the spread of disease while inflicting the least damage to the social fabric.

Virtual Epidemiology long before the germ theory of disease, London physician John Snow argued that cholera, which had killed tens of thousands of people in England during the preceding 20 years, spread via the water supply. In the summer of 1854 he tested that theory during an outbreak in the Soho district. On a map, he marked the location of the homes of each of the

500 victims who had died in the preceding 10 days and noted where each victim had gotten water. He discovered that every one of them drank water from the Broad Street pump, so Snow convinced officials to remove the pump handle. His action limited the death toll to 616.

Tracing the activities and contacts of individual disease victims, as Snow did, remains an important tool for modern epidemiologists. And it is nothing new for health authorities to rely on models ber is a best guess based on historical situations, even though the culture, physical conditions and health status of people in those events may differ greatly from the present situation.

In real epidemics, these details matter. The rate at which susceptible people become infected depends on their individual state of health, the duration and nature of their interactions with contagious people, and specific properties of the disease pathogen itself. Truer models

Truer models must capture the probability of disease transmission from one person to another.

when developing policies to protect the public. Yet most mathematical models for understanding and predicting the course of disease outbreaks describe only the interactions of large numbers of people in aggregate. One reason is that modelers have often lacked detailed knowledge of how specific contagious diseases spread. Another is that they have not had realistic models of the social interactions in which people have contact with one another. And a third is that they have not had the computational and methodological means to build models of diseases interacting with dynamic human populations.

As a result, epidemiology models typically rely on estimates of a particular disease's "reproductive number"—the number of people likely to be infected by one contagious person or contaminated location. Often this reproductive num of outbreaks must capture the probability of disease transmission from one person to another, which means simulating not only the properties of the disease and the health of each individual but also detailed interactions between every pair of individuals in the group.

Attempts to introduce such epide-miological models have, until recently, considered only very small groups of 100 to 1,000 people. Their size has been limited because they are based on actual populations, such as the residents, visitors and staff of a nursing home, so they require detailed data about individuals and their contacts over days or weeks. Computing such a large number of interactions also presents substantial technical difficulties.

Our group was able to construct this kind of individual-based epidemic model on a scale of millions of people by using

Overview/Simulating Society

Epidemiological simulations provide virtual laboratories where health officials can test the effectiveness of different responses in advance of disease outbreaks.

Modeling the movements of every individual in a large population produces a dynamic picture of the social network—the same network of contacts used by infectious diseases to spread among human hosts. Knowing the paths a disease could take through society enables officials to alter the social network through measures such as school closings and quarantines or by targeting individuals for medical treatment.

high-performance supercomputing clusters and by building on an existing model called TRANSIMS developed over more than a decade at Los Alamos for urban planning [see "Unjamming Traffic with Computers," by Kenneth R. Howard; Scientific American, October 1997]. The TRANSIMS project started as a means of better understanding the potential effects of creating or rerouting roads and other transportation infrastructure. By giving us a way to simulate the movements of a large population through a realistic urban environment, TRANSIMS provided the foundation we needed to model the interactions of millions of individuals for EpiSims.

Although EpiSims can now be adapted to different cities, the original TRANSIMS model was based on Portland, Ore. The TRANSIMS virtual version of Portland incorporates detailed digital maps of the city, including representations of its rail lines, roads, signs, traffic signals and other transportation infrastructure, and produces information about traffic patterns and travel times. Publicly available data were used to generate 180,000 specific locations, a synthetic population of 1.6 million residents, and realistic daily activities for those people [see box on opposite page].

Integrating all this information into a computer model provides the best estimate of physical contact patterns for large human populations ever created. With EpiSims, we can release a virtual pathogen into these populations, watch it spread and test the effects of different interventions. But even without simulating a disease outbreak, the model provides intriguing insights into human social networks, with potentially important implications for epidemic response.

Social Networks to understand what a social network really is and how it can be used for epidemiology, imagine the daily activities and contacts of a single hypothetical adult, Ann. She has short brushes with family members during breakfast and then with other commuters or carpool-ers on her way to work. Depending on her job, she might meet dozens of people

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