Biosurveillance of emerging biothreats using scalable genotype clustering

A systematic review of aberration detection algorithms used in public health surveillance

The algorithms used for detecting anomalies have evolved substantially over the last decade to take advantage of advances in informatics and to accommodate changes in surveillance data. We identified 145 studies since 2007 that evaluated statistical methods used to detect aberrations in public health surveillance data. For each study, we classified the analytic methods and reviewed the evaluation metrics. We also summarized the practical usage of the detection algorithms in public health surveillance systems worldwide. Traditional methods (e.g., control charts, linear regressions) were the focus of most evaluation studies and continue to be used commonly in practice. There was, however, an increase in the number of studies using forecasting methods and studies applying machine learning methods, hidden Markov models, and Bayesian framework to multivariate datasets. Evaluation studies demonstrated improved accuracy with more sophisticated methods, but these methods do not appear to be used widely in public health practice.

Global Spread of Hemorrhagic Fever Viruses: Predicting Pandemics

As successive epidemics have swept the world, the scientific community has quickly learned from them about the emergence and transmission of communicable diseases. Epidemics usually occur when health systems are unprepared. During an unexpected epidemic, health authorities engage in damage control, fear drives action, and the desire to understand the threat is greatest. As humanity recovers, policy-makers seek scientific expertise to improve their "preparedness" to face future events.Global spread of disease is exemplified by the spread of yellow fever from Africa to the Americas, by the spread of dengue fever through transcontinental migration of mosquitos, by the relentless influenza virus pandemics, and, most recently, by the unexpected emergence of Ebola virus, spread by motorbike and long haul carriers. Other pathogens that are remarkable for their epidemic expansions include the arenavirus hemorrhagic fevers and hantavirus diseases carried by rodents over great geographic distances and the arthropod-borne viruses (West Nile, chikungunya and Zika) enabled by ecology and vector adaptations. Did we learn from the past epidemics? Are we prepared for the worst?The ultimate goal is to develop a resilient global health infrastructure. Besides acquiring treatments, vaccines, and other preventive medicine, bio-surveillance is critical to preventing disease emergence and to counteracting its spread. So far, only the western hemisphere has a large and established monitoring system; however, diseases continue to emerge sporadically, in particular in Southeast Asia and South America, illuminating the imperfections of our surveillance. Epidemics destabilize fragile governments, ravage the most vulnerable populations, and threaten the global community.Pandemic risk calculations employ new technologies like computerized maintenance of geographical and historical datasets, Geographic Information Systems (GIS), Next Generation sequencing, and Metagenomics to trace the molecular changes in pathogens during their emergence, and mathematical models to assess risk. Predictions help to pinpoint the hot spots of emergence, the populations at risk, and the pathogens under genetic evolution. Preparedness anticipates the risks, the needs of the population, the capacities of infrastructure, the sources of emergency funding, and finally, the international partnerships needed to manage a disaster before it occurs. At present, the world is in an intermediate phase of trying to reduce health disparities despite exponential population growth, political conflicts, migration, global trade, urbanization, and major environmental changes due to global warming. For the sake of humanity, we must focus on developing the necessary capacities for health surveillance, epidemic preparedness, and pandemic response.

Pathogen genome bioinformatics

Recent advances in DNA sequencing technology have made the whole-genome sequencing of pathogens in a clinically relevant turn-around time both technically and economically feasible. The DNA sequencing of pathogens with epidemic potential offers new and exciting opportunities for high-resolution public health surveillance. This chapter outlines major methods and bioinformatics tools for pathogen genome characterization, the identification of infectious disease clusters, as well as for genomics-guided biosurveillance. Existing challenges are also considered.

Biosurveillance enterprise for operational awareness, a genomic-based approach for tracking pathogen virulence

To protect our civilians and warfighters against both known and unknown pathogens, biodefense stakeholders must be able to foresee possible technological trends that could affect their threat risk assessment. However, significant flaws in how we prioritize our countermeasure-needs continue to limit their development. As recombinant biotechnology becomes increasingly simplified and inexpensive, small groups, and even individuals, can now achieve the design, synthesis, and production of pathogenic organisms for offensive purposes. Under these daunting circumstances, a reliable biosurveillance approach that supports a diversity of users could better provide early warnings about the emergence of new pathogens (both natural and manmade), reverse engineer pathogens carrying traits to avoid available countermeasures, and suggest the most appropriate detection, prophylactic, and therapeutic solutions. While impressive in data mining capabilities, real-time content analysis of social media data misses much of the complexity in the factual reality. Quality issues within freeform user-provided hashtags and biased referencing can significantly undermine our confidence in the information obtained to make critical decisions about the natural vs. intentional emergence of a pathogen. At the same time, errors in pathogen genomic records, the narrow scope of most databases, and the lack of standards and interoperability across different detection and diagnostic devices, continue to restrict the multidimensional biothreat assessment. The fragmentation of our biosurveillance efforts into different approaches has stultified attempts to implement any new foundational enterprise that is more reliable, more realistic and that avoids the scenario of the warning that comes too late. This discussion focus on the development of genomic-based decentralized medical intelligence and laboratory system to track emerging and novel microbial health threats in both military and civilian settings and the use of virulence factors for risk assessment. Examples of the use of motif fingerprints for pathogen discrimination are provided.

Improving resolution of public health surveillance for human Salmonella enterica serovar Typhimurium infection: 3 years of prospective multiple-locus variable-number tandem-repeat analysis (MLVA)

Background

Prospective typing of Salmonella enterica serovar Typhimurium (STM) by multiple-locus variable-number tandem-repeat analysis (MLVA) can assist in identifying clusters of STM cases that might otherwise have gone unrecognised, as well as sources of sporadic and outbreak cases. This paper describes the dynamics of human STM infection in a prospective study of STM MLVA typing for public health surveillance.

Methods

During a three-year period between August 2007 and September 2010 all confirmed STM isolates were fingerprinted using MLVA as part of the New South Wales (NSW) state public health surveillance program.

Results

A total of 4,920 STM isolates were typed and a subset of 4,377 human isolates was included in the analysis. The STM spectrum was dominated by a small number of phage types, including DT170 (44.6% of all isolates), DT135 (13.9%), DT9 (10.8%), DT44 (4.5%) and DT126 (4.5%). There was a difference in the discriminatory power of MLVA types within endemic phage types: Simpson's index of diversity ranged from 0.109 and 0.113 for DTs 9 and 135 to 0.172 and 0.269 for DTs 170 and 44, respectively. 66 distinct STM clusters were observed ranging in size from 5 to 180 cases and in duration from 4 weeks to 25 weeks. 43 clusters had novel MLVA types and 23 represented recurrences of previously recorded MLVA types. The diversity of the STM population remained relatively constant over time. The gradual increase in the number of STM cases during the study was not related to significant changes in the number of clusters or their size. 667 different MLVA types or patterns were observed.

Conclusions

Prospective MLVA typing of STM allows the detection of community outbreaks and demonstrates the sustained level of STM diversity that accompanies the increasing incidence of human STM infections. The monitoring of novel and persistent MLVA types offers a new benchmark for STM surveillance.

A part of this study was presented at the MEEGID × (Molecular Epidemiology and Evolutionary Genetics of Infectious Diseases) Conference, 3-5 November 2010, Amsterdam, The Netherlands

Global animal disease surveillance

Development and implementation of global animal disease surveillance has been limited by the lack of information systems that enable near real-time data capturing, sharing, analysis, and related decision- and policy-making. The objective of this paper is to describe requirements for global animal disease surveillance, including design and functionality of tools and methods for visualization and analysis of animal disease data. The paper also explores the potential application of techniques for spatial and spatio-temporal analysis on global animal disease surveillance, including for example, landscape genetics, social network analysis, and Bayesian modeling. Finally, highly pathogenic avian influenza data from Denmark and Sweden are used to illustrate the potential application of a novel system (Disease BioPortal) for data sharing, visualization, and analysis for regional and global surveillance efforts.

Populations, Patients, Germs and Genes: Ethics Of Genomics and Informatics in Communicable Disease Control

This chapter will explore the ways in which genomics (and the informatics tools needed to analyze and interpret them) could, potentially, transform our understanding of infectious disease epidemiology, improve disease management and prevention and save lives. The integration of microbiological, clinical and environmental data into personalized clinical decision support and risk assessment tools will improve both the care of patients with infectious diseases and public health protection. Integrated electronic health information systems will be faster, safer and more cost-effective than the current inefficient miscellany of individual medical records and disease surveillance systems. Technical, commercial and economic barriers to system implementation are unlikely to be insurmountable, but the largely uncharted ethical hazards are potentially more challenging. Prospective evaluation of clinical and public health impact and cost-effectiveness of new diagnostic and surveillance methods and information systems, designed to protect privacy and security of health data without placing unnecessary restrictions on access to essential public health data, will be needed. The interests, priorities and concerns of all stakeholders, including patients and potential patients, health professionals and administrators, public health officials and politicians, must be openly debated and addressed. Otherwise, unnecessary delays or a failure to implement better systems will allow unnecessary suffering and preventable mortality to continue.

Informatics for Infectious Disease Research and Control

The goal of infectious disease informatics is to optimize the clinical and public health management of infectious diseases through improvements in the development and use of antimicrobials, the design of more effective vaccines, the identification of biomarkers for life-threatening infections, a better understanding of host-pathogen interactions, and biosurveillance and clinical decision support. Infectious disease informatics can lead to more targeted and effective approaches for the prevention, diagnosis and treatment of infections through a comprehensive review of the genetic repertoire and metabolic profiles of a pathogen. The developments in informatics have been critical in boosting the translational science and in supporting both reductionist and integrative research paradigms.