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This situation is unfortunate as there is an increasing need, as recently evidenced by the severe acute respiratory syndrome outbreak, to detect, study, develop effective treatments for, and ultimately control and prevent viral infections.
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Therefore, space-time disease-surveillance methods should be proposed as a dynamic supplement to purely spatial statistical methods for outbreak detection to detect and predict localized outbreaks before they spread to larger regions.
Further, space-time disease-surveillance methods have been proposed as a dynamic supplement to purely spatial statistical methods for outbreak detection to detect and predict localized outbreaks before they spread to larger regions.
Repeat sampling and multimodality testing may be chosen in an outbreak situation to detect all cases, effectively controlling nosocomial spread.
The objectives of the investigation were to identify the cause of the outbreak and to detect sporadic cases of NiV infection.
Thus, such uncertainty affects the ability to reproduce correctly the observed outbreak pattern and to detect the correct outbreak source.
As the outbreak size increases, the outbreak is easier to detect for a given false positive rate.
However, in developing countries like Pakistan, epidemic outbreaks are difficult to detect due to scarcity of public health data and absence of automated surveillance systems.
Such outbreaks are difficult to detect when the primary introduction of viruses through food occurs simultaneously in several countries or continents (7 – 9 ).
Early work focused on attempts to detect outbreaks (early event detection) by using broadly defined syndromes (e.g., respiratory syndrome) based on chief complaints and diagnoses.
The goal is to show how observed outbreak patterns can be analyzed with the same physical-based models to detect outbreak sources and design surveillance networks considering the underlying transmission networks; thus, linking structure and functions of networks (Newman 2003) related to "invisible" outbreak dynamics over space and time.
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