population abundances, and HPS in New Mexico (Parmenter et al., 1993) and between the 1997-1998 El Niño and rodent population densities in New Mexico and Colorado (Yates et al., 2002; Mills and Calisher, unpublished data).

Complicating our understanding of this moving target is climate change. Should mean temperatures and precipitation patterns continue to deviate upward from the current norms, increases in rodent population densities, the geographic distribution of rodents, and the prevalence of the viruses they harbor are likely to increase significantly, to the detriment of humans (Anyamba et al., 2006b). What is needed is a great deal more effort and funding invested in basic studies of the biology of both virus and vertebrate host—on their interactions, on the relative interactions of environmental variables, and on the variables that account for meaningful deviations from the norm.

Conclusions

The data accumulated during these and associated longitudinal studies of hantaviruses, specifically Sin Nombre virus, suggest that longitudinal studies may be the only current means available to identify predictors of risk for rodent acquisition of this virus and for subsequent transmission to humans.

We know that rodent populations fluctuate, sometimes considerably, but we do not know all the variables that impact those fluctuations. We know that virus (antibody) prevalence fluctuates, sometimes from 0 to 40 or 50 percent, but we do not know all the variables that impact those fluctuations. Nonetheless, these data suggest that the “trophic cascade” hypothesis is an innovative and acceptable one to test further. A simple and obviously correct hypothesis is not yet within our grasp but we seem to be intriguingly close.

It has long been accepted that certain ecologic and/or environmental conditions are associated with emergent transmission of agents causing zoonotic diseases. Recent development of predictive models for plague in the American southwest (Enscore et al., 2002; Eisen et al., 2007) are an example. In addition, Linthicum et al. (1999) offered a predictive model for Rift Valley fever in Kenya, and Anyamba et al. (2006b) predicted Rift Valley fever and malaria in East Africa, dengue fever and respiratory illnesses in specific areas of Asia, malaria in South America, cholera in Bangladesh and coastal India, southwestern United States for increased risk for HPS and plague, southern California for increased West Nile virus transmission, and northeast Brazil for increased dengue fever and respiratory illness. The recent extensive epizoodemic of Rift Valley fever, recognized in Kenya in December 2006, spread to Sudan, Tanzania, Somalia, and Burundi by May 2007 (ProMED-Mail, 2007). This disease outbreak alone indicates the utility of such predictive models.

It is possible that the trophic cascade hypothesis is a conceptual umbrella, and that using key elements of the predictive modeling systems mentioned earlier, and of other systems, might be useful in establishing models of other emerging



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