The precision public health

The term “precision public health” has been defined in various ways. It’s a new multidisciplinary discipline that combines genetics, big data, and machine learning/artificial intelligence to anticipate health risks and outcomes and enhance population health. Precision public health aims to offer the appropriate intervention to the right population at the right time, similar to how precision medicine seeks to deliver the proper intervention to the right patient at the right time to enhance everyone’s health.


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Genomic technologies have been at the forefront of clinical medicine applications and can transform public health. I am pleased to announce the publication of this special issue of Genome Medicine on the influence of genomics on precision public health. The approach focuses on the value of genetic technologies in public health research and practice. Given the fight against the COVID-19 pandemic, which has necessitated the use of genomic approaches to track the origin, transmission, and evolution of the SARS-CoV-2 virus globally and understand differential host susceptibility response, severity, and outcomes, this is especially timely. In addition to genetics, granular data from population monitoring methods are being used to target public health initiatives. Furthermore, machine learning and artificial intelligence have been used to define the natural history of COVID-19 and discover prognostic variables using big data, digital technologies, and mobile health applications.

In clinical and public health research, genomics is becoming increasingly important. A growing number of genomic diagnostics, such as tumor genome sequencing for targeted treatment, non-invasive prenatal screening, and genomic tests for juvenile and uncommon illnesses, are already available in practice. More broadly, public health research is needed to determine how genomics fits into an entire ecological model of health that considers gene-environment, behavioral, and social determinants. Many common chronic diseases have known environmental, social, and behavioral risk factors (e.g., smoking, physical activity, diet, racial, ethnic, and economic factors, and healthcare access). It’s critical to weigh the benefits, drawbacks, and costs of using human genomic data to prevent and control these diseases. Implementing genomic technologies in practice will necessitate a collaborative research effort involving epidemiologists, behavioral, social, communication scientists, health services researchers, and others.


Poor acceptance of evidence-based therapies and the possibility for expanding existing health inequities are two barriers to exploiting human genetic information to improve population health. To discover the most successful approaches and strategies for integrating genomic applications in public health. Partnerships between healthcare organizations and public health programs can aid in bridging the implementation gap and reducing inequities in health.


The most successful human genomic application in public health to date is newborn screening for curable inherited disorders. Still, population screening for other genetic conditions across the lifetime is becoming more feasible. The addition of genomic disorders with high incidence in non-European populations to a genomic screening program, according to Abul-Husn et al. in this issue, increases the number of non-European participants who choose to obtain findings. Stranneheim et al. also incorporate whole genome sequencing into the Stockholm-area healthcare system to improve rare disease diagnosis, demonstrating the importance of clinical-academic collaboration.


Other recent research has emphasized the need for public health programs to identify persons with hereditary cancers in populations (e.g., Lynch syndrome, hereditary breast, and ovarian cancer). Public health activities must include identifying those at risk in health systems and cascade screening, which involves testing families of those impacted. Public health initiatives can also aid in monitoring genomic medicine implementation, quantifying health inequities in performance, and developing strategies to address them.


Other emerging applications include pharmacogenomics, carrier testing of prospective parents, illness identification, and prevention. The latter is achieved using polygenic risk scores (PRS). Isgur et al., for example, show that PRS improves cardiovascular risk assessment when later-life risk factors are unclear. Improvement ascribed to PRS is minimal for the general population by middle age when several risk variables are known. A simple family health history evaluation can improve the delivery of precision medicine in health care and population settings, even in the genomics age. Furthermore, with millions of people seeking direct-to-consumer genetic tests, public health programs can educate the general public about the promise and limitations of emerging difficulties in improving health and tracking the impact of genomic tests at the population level.


The special issue includes studies on the ethical implications of genetics in public health in the COVID-19 age, which is noteworthy. Most of the moral, legal, and social considerations (ELSI) that apply to single-gene diseases—such as the relevance of results to family members, the approach to secondary and incidental findings, and the role of expert mediators—apply to PRS applications in practice, according to Lewis and Green.


I will discuss the ethical issues and consequences of ongoing genomic investigations that look at host variables that influence susceptibility, infectivity, and disease severity in COVID-19 patients and those exposed to it. Juengst et al. [11] offer a new ethical paradigm to ensure that the hazards to individuals, families do not overshadow the benefits of precision public health treatments based on genomics research and vulnerable populations.

Public health and infectious disease genomics


Genomics may be utilized to obtain accuracy about both the pathogen and the infected host population, making it useful for understanding and managing infectious diseases at both the individual and population levels.


Whole-genome sequencing (WGS) quickly becomes the standard assay for defining infectious illnesses. It has the potential to be an enormously rich source of information to guide public health initiatives. For example, pathogen genomics is increasingly being employed in surveillance for clinically significant features like vaccine antigens (to influence vaccine formulation) and antibiotic resistance determinants and the investigation of foodborne or hospital outbreaks (to inform treatment). In the clinic, genomics is rapidly used for disease identification and diagnoses. In this context, DNA sequencing can improve the precision of diagnosis beyond species to identify lineages or variants that may be associated with varying degrees of risk. The latter prompts different clinical management of the individual patient (e.g., antimicrobial treatment choice) or public health management responses (e.g., triggering infection control or contact tracing).


Infection is a two-way street, and functional genomic characterization of the host response can be used for diagnostic and predictive purposes, revealing the nature and intensity of immune responses and assisting in selecting appropriate treatments for various patient groups. Aschenbrenner et al., for example, looked analyzed the transcriptomes of COVID-19 patients [13], gaining insight into the disease’s natural history and showing disparities in the immune response between patients with severe and mild disease. I will investigate particular response profiles to find possible therapeutic repurposing medications selectively targeted to patients with severe disease. The ability to precisely define which host populations are most at risk of infection through genomic risk prediction is also an appealing proposition that could have significant implications for population health by allowing preventative measures to be targeted to the most at-risk subgroups. However, this raises the same ethical concerns as genomic risk prediction for non-communicable disease risk and may even intersect. In this special issue, Kachori et al. [14] discovered genetic variations linked to antibody responses to antigens for 16 distinct viruses, many related to non-communicable disorders, including cancer.


The COVID-19 pandemic has prompted the development of new methods for rapid high-throughput pathogen sequencing, such as the CoronaHIT approach for SARS-CoV-2, which demonstrates how researchers may use real-time pathogen genome data to inform public health strategy amid an active epidemic. Genomics is the primary method for monitoring pathogen population variation and identifying variants that may be a public health concern. The latter is due to increased transmissibility, (ii) differences in sensitivity/specificity of current diagnostic tests, and (iv) escape from interventions such as host immunity (vaccine-induced or natural) or drug sensitivity. Identifying such variants can improve the precision of public health responses by directing scarce resources to the most concerning variants. The latter includes community-based testing in areas of England where potential SARS-CoV-2 vaccine-escape variants were discovered or updating diagnostics and vaccines to ensure coverage of the constantly evolving pathogen population. Furthermore, phylogenomic analyses can be used to follow virus transmission at various spatial scales, such as determining the role of travel-associated strain introductions in the emergence of an epidemic in a particular country or analyzing virus transmission within hospitals. The epidemic has also brought to light problems such as obtaining and disseminating sequence data in a timely, equitable, and ethical manner, data visualization, labeling genetic variations, and communicating results both within and outside the public health sector.

The COVID-19 pandemic has been hypothesized to be increasing another crucial global health concern, antibiotic resistance [18]. The importance of genetics in identifying and reducing the burden of antibiotic-resistant infections in hospitals is highlighted in two papers in this special edition. By combining genetic and epidemiological data, BerbeAntimicrobial resistance genetics can be complicated. For most pathogens, the overall burden of resistant infections is the consequence of the distribution of resistance genes and plasmids among bacterial strains and the transmission of bacterial strains between patients. Researchers employ genomics to detangle and quantify these contributing characteristics, which may aid future infection control efforts.

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