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In the past, surveillance has been highly dependent on diagnostic facilities and health infrastructure. Reporting was often spotty and delayed . In recent years, there have been major improvements both in diagnostic technology and in syndromic and digital surveillance capabilities, greatly increasing the timeliness of reporting.49 These systems have less specificity than the traditional systems but can be used to target additional efforts and greatly complement conventional capabilities. In the meantime, diagnostic capabilities have also markedly improved. Molecular tests, including increasingly widespread use of PCR, array-based (‘‘chip’’) technologies, and even genomic sequencing directly from specimens without the need to isolate the pathogen, allow the identification of pathogens from previously unstudied sources, such as many wildlife species, and a better understanding of pathogen background in other species and the environment.47,48 A number of these technologies are adaptable to resource limited settings. Improved surveillance for early warning of infectious
disease threats, whether in a human population somewhere in the world or waiting in the wings as a potential zoonotic introduction, is therefore both essential and at least conceptually possible. The ability to identify pathogens, at times in mere hours or days, in virtually any species we can test is an impressive improvement in capability, but it also emphasizes the nascent state of our risk assessment ability. Pathogen discovery provides many insights but will also bring many hitherto unknown pathogens to light. It is likely that most of them are relatively well adapted to their hosts and probably would not cause human disease. If we wish to identify potential threats to public health, how to determine which of the many pathogens we will discover in other species are of concern? In other words, how do we predict rather than discover and watch? These are among
the issues that surveillance programs, such as PREDICT, will be wrestling with. As capabilities increase, the perennial need to distinguish signal from noise will become increasingly critical. This requires interpreting the surveillance data, and we are in the early stages of the learning curve. Our shortcomings in predicting avian influenza H5N1 (or, for that matter, influenza generally) exemplify our current state of knowledge. This infection clearly is devastating to poultry farmers’ livelihoods and to those people who are unfortunate enough to acquire the disease, usually through contact with infected poultry. As of late January 2012, there have
been some 583 human cases, with 344 deaths, in 15 countries.60 At one time, there was considerable concern
that the virus might evolve to spread efficiently person-toperson and become the next pandemic influenza, but fortunately this has not happened (and may never occur in nature, although recent reports, widely covered in the press, of adapting H5N1 to mammalian transmission in the laboratory have caused concern in the scientific and biosecurity communities). Whether it might happen some day, and if so whether the virus will remain highly virulent in humans, is unknown and, at this point, essentially unpre-dictable. This is but one of many examples demonstrating our general inability to make accurate predictions about infections from other species. Clearly, surveillance is essential to determine whether there are changes in the behavior of the pathogen. Once an infection is in humans, the surveillance strategies discussed above clearly apply, and epidemiology can be used to determine whether person-toperson transmission is occurring.
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