work effort should be extended to include analysis of data derived in these settings.
Improved precision in defining disease is of particular importance in regions of the world with under-developed health-care systems. Disease misdiagnosis in such settings has contributed to the improper use of therapy and the establishment of widespread drug resistance among disease-causing infectious agents. Malaria is one disease where misdiagnosis is common with dramatic consequences and costs (D’Acremont et al. 2009). In general, patients presenting with fever in regions where malaria is endemic are administered anti-malarial treatment without direct evidence that the patient actually has malaria. In part, this practice is due to limited resources—the state-of-the-art diagnostic test in most areas is a microscopy-based blood-smear diagnosis, which requires expert training. The lack of adequate point-of-care diagnostic tests to ascertain whether the patient has malaria represents a significant impediment to the selection of appropriate targeted therapy. As a consequence, major efforts are under way to develop molecular diagnostics for malaria and other major killers such as tubercuolosis (Boehme et al. 2010; Small and Pai 2010). Ultimately, such diagnostics will need to include tests that differentiate among various disease agents and also take into account genetic or molecular markers in the host that influence host responses to the infection or potential treatments. A globally relevant Information Commons and Knowledge Network could be useful in facilitating this process—for example, to distinguish between malaria caused by Plasmodium falciparum versus Plasmodium vivax, which are susceptible to different anti-malarial drugs (malERA Consultative Group on Diagnoses and Diagnostics 2011). The Knowledge Network and its associated taxonomy should not be designed exclusively to meet the needs of countries with advanced medical systems. Indeed, the individual-centric character of the Information Commons—and the inclusion of available data about contributing individuals, including information about where and in what circumstances they live—offers an unprecedented path toward a Knowledge Network of Disease that can meet global needs for health care and disease prevention.