Introduction
Genome epidemiology is a relatively young but promising sphere of scientific knowledge that appeals to genetics to anticipate and cease the spread of infectious diseases. One of the innovative methods it applies to study outbreaks is whole-genome sequencing. As apparent from the name, it enables depicting of the entire DNA sequence immediately, simplifying the identification of any specialties or changes in it. In practice, an approach of such a kind can improve not only public health and the quality of medical services but also save substantial amounts of money.
In epidemiological contexts, sequencing allows for identifying the genotypes of clinical isolates collected from patients, which, in turn, can help detect the origin of an infection and transmission events. Thus, in the case under review, the genotypes were possible to group into distinctive clusters (Tang & Gardy, 2014). First, that apparently marked sufficient similarity between them to ensure that the outbreak was single and large rather than comprised of several small and hardly interconnected occasions. Second, clustering served to identify the paths of infection transmission by tracing the genetic transformations of the agent.
Investigations of such a kind can be helpful in revealing the centers as well as ways of contagion more rapidly and precisely in comparison with the approaches conventional molecular epidemiology offers. Therefore, the researchers insist that whole-genome sequencing should become “a cornerstone of infection control;” every medical laboratory should have the equipment to perform it on a regular basis (Tang & Gardy, 2014, p. 3). Such an opinion, although quite idealistic, nevertheless may be considered relevant for several reasons.
Perspectives on Whole-Genome Sequencing
Patient Safety and Care
In one respect, molecular epidemiology, which currently occupies the leading position, presupposes genome research and generally performs in collaboration with genetics. Such examinations, however, predominantly are selective, targeting to provide answers to particular questions and, subsequently, focusing on genome fragments (Tümmler, 2020). Due to this lack of cohesion, as Tang & Gardy (2014) actually highlight, a considerable amount of pathogen transmission events may remain beyond attention or receive inadequate explanations. Localizing and overcoming infection outbreaks, therefore, becomes substantially more challenging than it would be otherwise.
The latter problem gains special importance in hospitals, where contagions spread rapidly due to constant interaction among patients and practitioners as well as active turnover. However, the seriousness of the issue frequently is underestimated, among the reasons which is the insufficient effectiveness of “conventional genotyping” in diagnostics (Tang & Gardy, 2014, p. 1). This puts patients, both as a population and each individually, at an unacceptably high risk of infection in case of a nosocomial outbreak. Furthermore, the false identification of transmissions frequently leads to the lack of proper care, increasing the probability and severity of complications.
Such techniques as whole-genome sequencing, meanwhile, can compensate for the above gaps, providing a full epidemiological view of the situation. Timely detection of the infection’s origin, as well as its paths from patient to patient, help predict its further spread and, consequently, interfere with it. This is essential not solely for public health but also in the ethical area, where hospitals are the providers of recovery.
Healthcare Delivery and Outcome
It is quite apparent that better awareness of the infection’s behavior enables controlling it more effectively than molecular epidemiology does. For instance, in the case under review, the conventional methodology would hardly have enabled identifying the particular contaminated bed as the center of infection (Tang & Gardy, 2014). Without discovering the latter, meanwhile, no healthcare delivery can be successful since the probability of transmission remains high, due to which care actually comes down to symptom management.
On the contrary, genome epidemiology enables tracing the infection from the center to the most recent patient. This enables anticipating its further way, switching the focus of healthcare delivery to preventative measures, whose effectiveness, in case of their appropriate design and implementation, is incomparably higher than that of symptomatic treatment. In addition, the above clustering is worth mentioning among the innovative methods of microbial diagnostics. Notably, it allows for a faster and more precise revelation of a certain disease as well as the identification of its type or forms a particular individual has (Hallgren et al., 2021). This, in turn, adds substantially to the effectiveness of treatment, enabling its maximal adjustment to the peculiarities of the patient’s state.
Cost Savings
Considering all of the above, genome epidemiology is outstandingly promising in outbreak management; with the use of its methods, localizing infections can become substantially easier. In such a case, the financial burden on the healthcare system will reduce noticeably, together with the need to invest in treatment. Regarding healthcare-associated infections (HAIs), as in the case under review, those continue to cause dramatically large financial losses even in developed countries. Thus, in 2014, Britain spent over £1 billion on overcoming nosocomial epidemics (Tang & Gardy, 2014, p. 1). Meanwhile, preventing those is able to save comparable sums, which are possible to invest in further development of the healthcare industry.
To summarize, the method of whole-genome sequencing, one of the techniques genome epidemiology utilizes, has several advantages in both medical and economic terms. First, it is helpful in preventing infection transmissions, which factors positively into public health. Second, it allows for more precise diagnostics and, consequently, more adequate healthcare. Finally, localizing epidemics at early stages is incomparably more appropriate financially than managing them when they reach a large scale.
References
Hallgren, M. B., Overballe-Petersen, S., Lund, O., Hasman, H., & Clausen, Ph. T. L. C. (2021). MINTyper: an outbreak-detection method for accurate and rapid SNP typing of clonal clusters with noisy long reads. Biology Methods and Protocols, 6(1), bpab008. Web.
Tang, P., & Gardy, J. L. (2014). Stopping outbreaks with real-time genomic epidemiology. Genome Medicine, 6, article 104. Web.
Tümmler, B. (2020). Molecular epidemiology in current times. Environmental Microbiology, 22(12), 4909-4918. Web.