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Rapid molecular profiling for multi-resistant Staphylococus aureus (MRSA) spa typing as a bioinformatics approach is currently being practiced as a dependable method replacing the alternative traditional approaches used for hospital-acquired infection control.
By applying bioinformatics, microbial genomics are integrated with clinical data to achieve groups of patients who belong to the same outbreak cluster thereby “enabling predictive treatment” (Shortliffe 36). Bioinformatics relies on pathogenic genetic and spatiotemporal data to derive the respective pathogenic history, which identifies the possible migration routes around the world.
First, a pathogenic profile analysis is conducted at the local level to identify recent transmissions. Afterwards a global analysis follows and information is collected on the pathogens’ distribution patterns. This is done to isolate the pathogens’ transmission pathways, which can be used to determine a suitable clinical policy formulation.
The basis of a successful pathogenic profiling process depends on the ability for the current tools of science to support knowledge representation and text mining.
Sintchenko, Gallego, Chung and Coiera assert that “pathogen profiling efforts can benefit from integration with knowledge representations systems that express their relationships with other entities from the biological hierarchy” (3).
Additionally, ontologies and controlled vocabularies have emerged as tools for knowledge management within this profiling process.
In order to achieve the desired levels of success, bioinformatics also involves antimicrobial therapy optimization achieved through the use of tools that can predict drug resistance and suitable response therapy, which can be used within a clinical decision support system.
This is a statistical oriented system comprising tools that analyze various pathogen occurrences using regression and learning algorithms. This process primarily depends on what Sintchenko et al. refer to as “the microbial profile therapeutic decision and response therapy” (8).
Certainly, technology has introduced higher precision in many processes. Bioinformatics practices expose the inefficiencies of traditional approaches to epidemic detection, treatment and control. Studies show that epidemic control remains an expensive undertaking in terms of research, control and treatment.
Accordingly, a biosurveillance approach that makes use of microbial profiling is likely to reduce the cost of epidemic control, treatment and aversion in the near future. However, it is evident that this approach definitely requires high expertise and expensive tools to carry out. As such, microbial profiling remains a suitable approach to adopt in health care and research-oriented organizations.
Such research organizations can draw funds to acquire the tools and use these tools to acquire the data needed for the research. The strength in microbial profiling relates to its near accuracy tendencies in prediction, which can determine the response and treatment of an epidemic in a timely manner.
Therefore, is the future cost of epidemic control likely to favour the use of microbial profiling and if so to what extend?
Works Cited
Shortliffe, Edward. Biomedical Informatics: Computer Applications in Healthcare and Biomedicine (Health Informatics). 3rd ed. New York: Springer, 2006. Print.
Sintchenko, Vitali, Blanca Gallego, Grace Chung, and Enrico Coiera. “BMC Bioinformatics.” Towards bioinformatics assisted infectious disease control 2009: 1-9. Print.
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