forward looking individuals adapt their behavior to take account of the prevalence of a disease and the threat it poses to them. These changes in behavior will differ according to the disease, but they will generally have both economic and epidemiological consequences, in the latter case by feeding back into the rate of new infections and disease prevalence. We set out a simplified version of a standard S-I-R epidemiological model to illustrate how such models can be adapted to reflect choice of preventive behaviors by utility-maximizing individuals. Section 3 then focuses on the primordial facts of high uncertainty and highly imperfect information that typically prevail during infectious disease outbreaks. One implication of these facts is that the nature of protective changes in behavior will depend critically on people’s subjective judgments of probability about their uncertain environment, for example on the probability that an outbreak has occurred, or on the conditional probability that they will become infected given a certain level of protective action.

How do people form these subjective probabilities? A useful benchmark assumption is that of rational expectations, that people do not make persistent and systematic errors about the key probabilities governing their environment. On the other hand research in psychology and behavioral economics over recent decades amply demonstrates the presence of substantial biases in probability judgments in many contexts. Recent theoretical work on information cascades and herding behavior also suggests the possibility that in situations of imperfect information people may rationally look at the behavior of others as a source of information, and that this procedure can lead large numbers to jump to the same erroneous conclusions and sub-optimal decisions. It seems quite likely that under the conditions of high uncertainty, poor information, rapid change and emotional stress that prevail during an infectious disease outbreak, individuals could well arrive at significantly biased subjective assessments on key factual issues, at least for a time, leading them to panic and take less than optimal decisions, resulting, in the aggregate, in an excessively high cost of private preventive actions. Section 3 looks at public opinion surveys taken during SARS which provide suggestive (though not conclusive) evidence that people did indeed at times hold excessively high perceptions of the risk of becoming infected with SARS, or, if infected, of dying from the disease. However some survey evidence from SARS also suggests that disease risk perceptions are quite sensitive to new information, and that people are constantly trying to update and improve their subjective probability assessments. This makes sense, since being consistently wrong can be expensive in an infectious disease setting.

Section 4 looks more closely at the role of information and communications in public health policy. The likelihood that people can and do make economically costly mistakes about how best to protect themselves from infectious disease suggests a rationale for public information and risk communication strategies that help reduce unwarranted panic by providing the public with more accurate and timely information. The need for transparency in public risk communications strategy is widely promoted nowadays. Why then is it that not infrequently a government’s initial impulse is to try and _conceal_ information about an outbreak from the public and from international bodies like the WHO? To achieve more transparent information strategies it is first necessary to understand the incentives facing governments. In the early stages of a limited disease outbreak there may be considerable uncertainty about whether it will turn into an epidemic or simply fizzle out, so that there is an incentive for the government to simply 3