Simulations allowed people to withdraw to their homes because they felt ill or were following officials' instructions. Withdrawal could be "early," before anyone became contagious, or "never," meaning people continued moving about unless they died. "Late" withdrawal, 24 hours after becoming contagious, was less effective than early withdrawal, which prevented an epidemic without other intervention. Official responses included doing nothing, or targeted vaccination and quarantine with unlimited personnel, or targeted vaccination limited by only half the necessary personnel being available, or mass vaccination of the entire population. The interventions began four, seven or 10 days after the first victims became symptomatic.
addition, we allowed infected individuals to isolate themselves by withdrawing to their homes.
Each simulation ran for a virtual 100 days [see box on opposite page], and the precise casualty figures resulting from each scenario were less important than the relative effect different responses had on the death tolls. The results upheld our theoretical prediction based on the expander-graph structure of the social network: time was by far the most important factor in limiting deaths. The speed with which people withdrew to their homes or were isolated by health officials was the strongest determinant of the outbreak's extent. The second most influential factor was the length of the delay in officials' response. The actual response strategy chosen made little difference compared with the time element.
In the case of a smallpox outbreak, these simulations indicate that mass vaccination of the population, which carries its own risks, would be unnecessary. Targeted vaccination would be just as effective so long as it was combined with rapid detection of the outbreak and rapid response. Our results also support the importance of measures such as quarantine and making sure that health officials give enforcement adequate priority during highly infectious disease outbreaks.
Of course, appropriate public health responses will always depend on the disease, the types of interventions available and the setting. For example, we have simulated the intentional release of an in-halable form of plague in the city of Chicago to evaluate the costs and effects of different responses. In those simulations we found that contact tracing, school closures and city closures each incurred economic losses of billions of dollars but did not afford many health benefits over voluntary mass use of rapidly available antibiotics at a much lower economic cost.
Most recently, as part of a research network organized by the National Institute of General Medical Sciences called the Models of Infectious Disease Agent Study (MIDAS), we have been adapting EpiSims to model a naturally occurring disease that may threaten the entire planet: pandemic influenza.
Flu and the Future over the past year, a highly virulent strain of influenza has raged through bird populations in Asia and has infected more than 40 human beings in Japan, Thailand and Vietnam, killing more than 30 of those people. The World Health Organization has warned that it is only a matter of time before this lethal flu strain, designated H5N1, more easily infects people and spreads between them. That development could spark a global flu pandemic with a death toll reaching tens of millions [see SA Perspectives, Scientific American, January].
MIDAS collaborators will be studying the possibility that an H5N1 virus capable of spreading in humans might be contained or even eradicated by rapid intervention while it is still confined
to a small population. To simulate the appropriate conditions in which the strain would likely emerge among humans, we are constructing a model representing a hypothetical Southeast Asian community of some 500,000 people living on farms and in neighboring small towns. Our model of the influenza virus itself will be based both on historical data about pandemic flu strains and information about the H5N1 virus, whose biology is currently a subject of intense investigation.
We know, for example, that H5N1 is sensitive to antiviral drugs that inhibit one of its important enzymes, called
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