public information. This would explain why cascades tend to be short lived and also points to the policy role of public communications strategy.

Among the more significant criticisms of the model is its dependence on the assumption that people have only a discrete binary choice of actions (flee or stay put). If, however, individuals have more choices or can act along a continuum (for example by varying the number of contacts per day with other people), then their actions can communicate more information and the chances of a cascade are reduced. (Gale 1996). Information cascade models also do not admit of _prices_ , which generally serve as powerful aggregators of information and whose presence could preclude information cascades. (Avery and Zemsky 1998). This criticism does not seem a major flaw in an infectious disease context, where there are generally no markets or prices reflecting information about the disease situation.<sup>9</sup> More recently Chari and Kehoe (2003) have argued that both these critiques of the information cascade/herding model (i.e. lack of continuous choices and lack of prices) can be rebutted when another assumption of the model is relaxed: the assumption that choices are made in some exogenously fixed sequence. When individuals are allowed to choose the timing of their actions (“endogenous timing”) – a reasonable assumption in a disease context - then cascades and herding are found to occur even in models with continuous choices and prices. Overall it seems that information cascade models should provide a useful direction for further research into herding behavior during infectious disease outbreaks.

# **4.  Information Strategies:  Some Policy Considerations**

In addition to the actions of private individuals, the assessments made by governments, their capabilities, actions and omissions also play a critical role in determining the economic and health impacts of infectious disease outbreaks. The preceding discussion in particular suggests a strong rationale for public information and risk communication strategies that could help people update mistaken risk perceptions more quickly, or, in a herding context, break erroneous information cascades more promptly, thereby reducing the scope for over-costly disease prevention strategies. We note two issues. First, governments, especially in developing countries, are themselves sometimes woefully ill-informed about disease situations. Strengthening weak disease surveillance systems is therefore an important priority in many countries. Second, if there are such benefits from providing the public with accurate information, why is it that 9 It does however point to the possibility of creating artificial _information aggregation_ or _prediction markets_ as a deliberate policy instrument to help pool the many bits of information about the existing and likely future disease situation that may be scattered among thousands of individuals in society. An experimental Influenza Prediction Market is already functioning in the United States, with health care professionals trading contracts on the level of seasonal influenza activity in several US states in the coming year, with the price of the contract equaling the probability that a given influenza outcome will occur. An Avian Influenza Prediction Market also began trading in 2007. Experts in 40 countries trade contracts on, for example, whether the WHO will confirm a first human H5N1 case in North or South America by some given time, or whether the WHO will increase its phase of pandemic influenza alert by a given time. (On November 29, 2007 the contract on an increase in the WHO’s alert level occurring by July 1, 2008 was trading at 7 cents on the dollar, i.e. a 7 percent probability.) Such prediction markets for political races are found to significantly outperform opinion polls. They could provide an important tool to strengthen infectious disease surveillance. (University of Iowa Health Prediction Markets 2007, Forsythe et al 1992).

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