To do this, our group has used a database approach pioneered by Mark Woolhouse’s group in Edinborough and based on the database analyses commonly used in ecological studies of animal life history traits. In this approach, global spatial data on environmental changes (e.g., agricultural land-use change) and the outcomes of these changes (in this case of the occurrence of an emerging disease) are tested for correlation. To do this for disease emergence, we expanded a database of all pathogens known to emerge in people (Woolhouse, 2008). The distribution of the types of newly emerging pathogens offers a glimpse of what sort of pathogens are more likely to cause the next emerging disease. A disproportionate amount of these pathogens are drug-resistant bacteria (e.g., MRSA) and viruses (mainly RNA viruses, e.g., HIV-1, SARS CoV, and Chikungunya virus). This is not entirely surprising because of the recent rise in global use of a diverse array of antibiotics, and because of the mutation rates and lack of copy editing mechanisms in the RNA viruses, which make them better able to produce more diverse strains capable of establishing in new host species. The origins of emerging pathogens are also informative, with the majority being zoonotic (e.g., SARS CoV, the Lyme disease spirochete, and Ebola virus) and these zoonoses include many of the most significant infections to emerge recently. This likely reflects our increasingly close association with animals, a factor that may appear counterintuitive in developed countries where our meat is bought prepackaged in plastic, but is a virtue of the unprecedentedly large global human population and our globalized travel and trade networks. Even as we eat our lunch here at the Institute of Medicine workshop, we may be eating beef produced in Australia, anchovies from Peru, and blackberries grown in Guatemala. Thus, in our database of emerging diseases, we find zoonotic diseases emerging from this complex network of globalized agriculture and trade.
Taking the database of emerging diseases, we surveyed the literature for the most accurate information available on the geographic origin of the first known outbreaks for each pathogen. In plotting out the origin of each of the more than 400 emerging disease “events,” we find a strong bias toward the developed countries of Europe, North America, and the Far East. This likely reflects the increased ability of these richer countries to identify emerging disease outbreaks and is perhaps due to their higher spending on healthcare. To correct for geographic and temporal biases in global reporting, we trawled through every paper published in Journal of Infectious Diseases18 from 1980 to 2002, collated each author’s geographic origin and the date of the work, and then incorporated these data into our analyses. Next, we developed a strategy to estimate the global spread of the vast diversity of unknown pathogens. To do this, we used a global database of the distribution of every mammalian species (Jones et al., 2008) and made the simple assumption that every species will carry a roughly equal number of pathogens,