Title : Precision genome-driven epidemiology and biostatistics for infectious disease control

Personalized and Precision Medicine (PPM) focuses on predictive and preventive measures that contribute to the development of individualized strategies for managing a healthy lifestyle that stabilize morbidity rates and can help to improve the working capacity of the population. PPM provides procedures for chronic (including communicable) disease prediction and for the prediction of consequences and complications. In this regard, the biomarker-based analysis is intended as a first step towards a more PPM-driven  treatment and clinical utility.

Meanwhile, despite being a forerunner PPM is not yet routinely applied in infectious patient care. Since, for instance, with the increase in antimicrobial agent resistance and a decreasing antimicrobial pipeline, there is a need for coordinated efforts to promote appropriate use of antimicrobial agents. Such “antimicrobial agent stewardship” measures encourage the appropriate use of antimicrobials by promoting the selection of the optimal drug regimen. PPM can help solve the crisis of antimicrobial resistance (AMR) by changing the way antimicrobial agents are developed and prescribed.

To date, the field of PPM has primarily focused on the use of patients’ own genomic information to make personalized decisions about disease treatment. During infectious disease outbreaks, however, genomic sequence information from the pathogen is arguably more important than an individual’s genomic data for designing appropriate treatment and intervention strategies. And new biodesign-driven technologies are thus supporting the rapid identification of infective agents and targeted approaches based on the genetic resistance of pathogens to antibiotics.

Pathogen genomes can also be used to inform population-level intervention strategies for infectious disease outbreaks. In contrast to the design of individual-level treatment strategies, outbreak-scale genomic analyses use pathogen mutations as markers of transmission events. Genomic epidemiology exploits the rapid evolution of pathogens, which often accumulate mutations on the same timescale as their epidemiological spread, to reconstruct outbreak dynamics from genomic data. With sufficient sampling, relevant metadata (such as location and date) and an appropriate statistical framework, pathogen genomes can reveal patterns of epidemic transmission at a fine-scale resolution, thus enabling the design of targeted interventions that are more precise than those based on traditional epidemiological data alone. This information can lead to revising the data that can be used for personalized predicting diseases, improving the usage of precision biomarkers and personalized treatment, and also personalized prevention strategies specific to infectious pathogens.

One of the most advanced population-level applications of PPM-related epidemiology is food safety, where it is used for pathogen identification and source attribution. Genome sequencing of foodborne bacterial pathogens now forms part of many surveillance systems, and through near-real-time genome sequencing and public data deposition of clinical, environmental, and food-related bacterial isolates, this network is streamlining the process of recognizing, investigating, and reducing the impact of foodborne disease outbreaks.

Adoption of genomic epidemiology into effective outbreak responses, however, will require the establishment of improved mechanisms for coordination between academic researchers and public health agencies. This includes changes to research practice regarding the benefits for rapid and open sharing of data and results as well as a focus on building capacity for sequencing and analysis within public health agencies and the regions most severely impacted by infectious disease.

A symbiotic relationship between infectious diseases, their risks, epidemiological studies, public health and PPM may exist. In this sense, accurate diagnosis of malaria and the resilient capacity that the malaria parasite has in acquiring resistance to anti-malarial drugs (based on the phenotypic variations) form immediate barriers to the control and elimination of this disease. Those variations mentioned could be dependent on geo location or differential transmission setting or even the different ring developmental stage. So, PPM-based and driven OMICS- and IT-armamentarium would secure the advances in spectroscopic-based technologies which is able to reveal unique ‘molecular fingerprint’, and thus providing the much needed rapid phenotyping (rather than genotyping) platform in the field.

Improving the express (rapid) control of infectious diseases requires the evaluation of interventions that prevent disease at the population level and successfully treat infections at the individual level. This proposal brings together advanced biostatistical research with mathematical modelling to discover novel methods for evaluating antimalarial treatments and malaria vaccine candidates, leading to new insights in infectious disease control and building capacity in this emerging cross-disciplinary field. the statistical method is used to explain and predict some of the health outcomes and the direction of epidemics and pandemics, and it definitely influences decision-makers in public health. Those who are working on, proposing and advising the mitigation strategies in response to contagions use biostatistics data and results to guide public health and other healthcare practitioners on how to go about controlling these diseases.

Because of the great importance of biostatistics in healthcare, the field is continuously evolving and developing. The upgraded advancements are enabling us to see trends, especially in molecular (genomics-related) epidemiology and other disease-related research. In addition to infectious diseases, biostatistics are teaching us more about cancer and chronic conditions like diabetes and cardiovascular disease, and have led to the development of better treatment plans to control them. Healthcare and public health have been developing based on the ability of biostatistics to provide us with evidence, and that trend will continue in the future. This is the essence of any graduate program related to the health science fields.

Sergey Suchkov has completed his Graduation from Astrakhan State Medical University and was awarded with MD. He has completed PhD from I.M. Sechenov Moscow Medical Acad-emy and Institute of Medical Enzymology. He is currently a Professor, Director and Center for Personalized Medicine, I.M. Sechenov First Moscow State Medical University and De-partment of Clinical Immunology, A.I. Evdokimov Moscow State Medical and Dental Uni-versity; and Professor, Chair, Department for Translational Medicine, Moscow Engineering Physical Institute, Russia and also Secretary General, United Cultural Convention, UK.