The vision is to develop a global system to aggregate, share, mine and use microbiological genomic data to address global public health and clinical challenges, a high impact area in need of focused effort. Such a system should be deployed in a manner which promotes equity in access and use of the current technology worldwide, enabling cost-effective improvements in plant, animal, environmental and human health.
Objective
A global system to aggregate, share, mine and translate genomic data for microorganisms in real-time is a realistic goal. A system enabling a direct link from end-users in academia, industry and government (e.g. clinicians, veterinarians, epidemiologist, microbiologists) to main databases through user-friendly platforms would provide significant societal advantages, including a safe and nutritious food supply. This system could include a reference database which could be accessed both for single clinical tasks (simple microbiological identification) as well as for national and international public health surveillance and outbreak investigation and response.
Rationale and context
Recent disease outbreaks have reinforced the notion that infectious diseases, including diseases transmitted via animals, food, environment etc., remain a global challenge. Fifteen million (>25%) of 57 million annual deaths worldwide are the direct result of infectious disease and it is generally agreed that current and new infectious disease challenges will continue to arise, given the trend towards globalization of travel and trade, coupled with demographic changes (urbanization, ageing) and increasing impact of the human population on natural environments.
The rapid development of genomic technologies and the potential replacement of century-old culture diagnostic techniques in clinical laboratories holds great promise for improving the early detection, prevention and control of current and emerging infectious diseases, thus contributing to improved health of the global population. Developing such a coordinated system is timely, but also urgent and imperative, as technological progress and market forces will result in widespread (and potentially unhelpfully divergent) deployment of genomic sequencing technology for routine diagnostic testing in clinical laboratories. It is likely that such changes will be easier in countries where systems are not yet entrenched, enabling significant ‘leap-frog’ (i.e. a technological short-cut) potential for developing countries.
A pre-requisite for implementing this novel approach would involve a fundamental shift in the current paradigm which generally separates work efforts according to traditional discipline groupings (e.g. ‘virology, bacteriology, parasitology’ or ‘animal, food, human’). As well, effective collaboration across disciplines (e.g. clinicians, microbiologists, epidemiologists, bioinformaticians, infection control specialists, and others), while respecting legal and ethical rules and regulations will also be necessary.
A system as is envisioned would benefit those tackling individual problems at the frontline (clinicians, veterinarian, etc) as well as other stakeholders (i.e. policy-makers, regulators, industry, etc). By enabling access to this global resource, a professional response to health threats will be within reach of all countries with basic laboratory infrastructure. A more holistic approach for access to laboratory data could potentially result in more efficient resolution of complex problems with consequent cost-savings both in terms of deployment of resources and reduction of health burden in a more timely fashion.
System transparency and openness, along with precise data sharing rules (e.g. potential for downloads of data, consented scope for use of data), are very important aspects of any future system. Issues will arise in outbreak situations where some discretion and data restriction will be prudent, taking due care of the need to follow the WHO International Health Regulations (2005). Decisions on what metadata (e.g. demographic, geographical, sociological) or strain-related data (e.g. phenotypic, genotypic) to include need to be made as well as descriptions of the multiple potential ways of getting such data into the system(s).