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Algorithms for identifying the infection states of nodes in a network are crucial for understanding and containing epidemics and cascades.
Hence, the problem of effectively identifying the infection states of unobserved nodes given a set of observed nodes is of central importance in the study of infection cascades.
The IRS was categorized into three levels with the object of identifying the infection risk: level I (low risk), patients with 3, 4 or 5 points in the IRS; level II (intermediate risk) for patients with 6, 7, 8 or 9 points in the IRS; and level III (high risk) for patients with 10, 11 and 12 points.
As expected, body temperature and plasma leukocyte counts were ineffective in identifying the infection focus.
Selection bias in identifying the infection may exist, diminishing the reported incidence to only patients with severe disease.
In addition, given the severity of melioidosis and importance of identifying the infection to guide treatment, several other factors should be considered in order to increase detection of the disease.
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In contrast, our work tries to identify the infection state of each unobserved node given a limited number of nodes with known infection state and no infection timestamps.
Several living birds had respiratory diseases, and lab testing was underway to identify the infection, officials said.
After a laboratory-confirmed case of Mycobacterium haemophilum skin infection in a recently tattooed immunocompetent adult was reported, we investigated to identify the infection source and additional cases.
Investigations associated with clinical sign of infection were done in order to identify the infection source, including X-ray, ultrasound, CT, urine, and other body fluid test.
In addition, we could identify the infection focus of all but 8.8% of the patients with true CA infection (in previously defined CA infection, 25.7% patients were unidentified for infection focus) [ 18].
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