Determining optimal surveillance networks for an emerging pathogen is difficult since it is not known beforehand what the characteristics of a pathogen will be or where it will emerge. The resources for surveillance of infectious diseases in animals and wildlife are often limited and mathematical modeling can play a supporting role in examining a wide range of scenarios of pathogen spread. We demonstrate how a hierarchy of mathematical and statistical tools can be used in surveillance planning help guide successful surveillance and mitigation policies for a wide range of zoonotic pathogens. The model forecasts can help clarify the complexities of potential scenarios, and optimize biosurveillance programs for rapidly detecting infectious diseases. Using the highly pathogenic zoonotic H5N1 avian influenza 2006-2007 epidemic in Nigeria as an example, we determined the risk for infection for localized areas in an outbreak and designed biosurveillance stations that are effective for different pathogen strains and a range of possible outbreak locations. We created a general multi-scale, multi-host stochastic SEIR epidemiological network model, with both short and long-range movement, to simulate the spread of an infectious disease through Nigerian human, poultry, backyard duck, and wild bird populations. We chose parameter ranges specific to avian influenza (but not to a particular strain) and used a Latin hypercube sample experimental design to investigate epidemic predictions in a thousand simulations. We ranked the risk of local regions by the number of times they became infected in the ensemble of simulations. These spatial statistics were then complied into a potential risk map of infection. Finally, we validated the results with a known outbreak, using spatial analysis of all the simulation runs to show the progression matched closely with the observed location of the farms infected in the 2006-2007 epidemic.

To aid in developing a global biosurveillance program, it is critical to develop a framework to capture and understand the myriad of data streams and evaluate them in context of surveillance goals.  Toward this goal, Los Alamos National Laboratory has developed a new method of evaluating the effectiveness of data stream types through the use of a novel concept called the surveillance window, a technique that integrates operational systems analysis, surveillance system analysis and epidemiological analysis. This study provides a simple, yet elegant methodology for which to ground truth known and emerging data streams for utility in integrated biosurveillance efforts.

Biosurveillance activities focus on acquiring and analyzing epidemiological and biological data to interpret unfolding events and predict outcomes in infectious disease outbreaks. We describe a mathematical modeling framework based on geographically aligned data sources and with appropriate flexibility that partitions the modeling of disease spread into two distinct but coupled levels. A top-level stochastic simulation is defined on a network with nodes representing user-configurable geospatial “patches”. Intra-patch disease spread is treated with differential equations that assume uniform mixing within the patch. We use U.S. county-level aggregated data on animal populations and parameters from the literature to simulate epidemic spread of two strikingly different animal diseases agents: foot-and-mouth disease and highly pathogenic avian influenza. Results demonstrate the capability of this framework to leverage low-fidelity data while producing meaningful output to inform biosurveillance and disease control measures. For example, we show that the possible magnitude of an outbreak is sensitive to the starting location of the outbreak, highlighting the strong geographic dependence of livestock and poultry infectious disease epidemics and the usefulness of effective biosurveillance policy. The ability to compare different diseases and host populations across the geographic landscape is important for decision support applications and for assessing the impact of surveillance, detection, and mitigation protocols.

In recent years, biosurveillance has become the buzzword under which a diverse set of ideas and activities regarding detecting and mitigating biological threats are incorporated depending on context and perspective. Increasingly, biosurveillance practice has become global and interdisciplinary, requiring information and resources across public health, One Health, and biothreat domains. Even within the scope of infectious disease surveillance, multiple systems, data sources, and tools are used with varying and often unknown effectiveness. Evaluating the impact and utility of state-of-the-art biosurveillance is, in part, confounded by the complexity of the systems and the information derived from them. We present a novel approach conceptualizing biosurveillance from the perspective of the fundamental data streams that have been or could be used for biosurveillance and to systematically structure a framework that can be universally applicable for use in evaluating and understanding a wide range of biosurveillance activities. Moreover, the Biosurveillance Data Stream Framework and associated definitions are proposed as a starting point to facilitate the development of a standardized lexicon for biosurveillance and characterization of currently used and newly emerging data streams. Criteria for building the data stream framework were developed from an examination of the literature, analysis of information on operational infectious disease biosurveillance systems, and consultation with experts in the area of biosurveillance. To demonstrate utility, the framework and definitions were used as the basis for a schema of a relational database for biosurveillance resources and in the development and use of a decision support tool for data stream evaluation.

The Biosurveillance Data Stream Framework and associated definitions are proposed as a starting point to facilitate the development of a standardized lexicon for biosurveillance and characterization of currently used and newly emerging data streams. Criteria for building the data stream framework were developed from an examination of the literature, analysis of information on operational infectious disease biosurveillance systems, and consultation with experts in the area of biosurveillance. To demonstrate utility, the framework and definitions were used as the basis for a schema of a relational database for biosurveillance resources and in the development and use of a decision support tool for data stream evaluation.

The National Strategy for Biosurveillancedefines biosurveillance as “the process of gathering, integrating, interpreting, and communicating essential information related to all-hazards threats or disease activity affecting human, animal, or plant health to achieve early detection and warning, contribute to overall situational awareness of the health aspects of an incident, and to enable better decision-making at all levels.” However, the strategy does not specify how “essential information” is to be identified and integrated into the current biosurveillance enterprise, or what the metrics qualify information as being “essential”. Thequestion of data stream identification and selection requires a structured methodology that can systematically evaluate the tradeoffs between the many criteria that need to be taken in account. Multi-Attribute Utility Theory, a type of multi-criteria decision analysis, can provide a well-defined, structured approach that can offer solutions to this problem. While the use of Multi-Attribute Utility Theoryas a practical method to apply formal scientific decision theoretical approaches to complex, multi-criteria problems has been demonstrated in a variety of fields, this method has never been applied to decision support in biosurveillance.We have developed a formalized decision support analytic framework that can facilitate identification of “essential information” for use in biosurveillance systems or processes and we offer this framework to the global BSV community as a tool for optimizing the BSV enterprise. To demonstrate utility, we applied the framework to the problem of evaluating data streams for use in an integrated global infectious disease surveillance system.

approach conceptualizing biosurveillance from the perspective of the fundamental data streams that have been or could be used for biosurveillance and to systematically structure a framework that can be universally applicable for use in evaluating and understanding a wide range of biosurveillance activities. Moreover, the Biosurveillance Data Stream Framework and associated definitions are proposed as a starting point to facilitate the development of a standardized lexicon for biosurveillance and characterization of currently used and newly emerging data streams. Criteria for building the data stream framework were developed from an examination of the literature, analysis of information on operational infectious disease biosurveillance systems, and consultation with experts in the area of biosurveillance. To demonstrate utility, the framework and definitions were used as the basis for a schema of a relational database for biosurveillance resources and in the development and use of a decision support tool for data stream evaluation.

To aid in developing a global biosurveillance program, it is critical to develop a framework to capture and understand the myriad of data streams and evaluate them in context of surveillance goals. Toward this goal, Los Alamos National Laboratory has developed a new method of evaluating the effectiveness of data stream types through the use of a novel concept called the surveillance window, a technique that integrates operational systems analysis, surveillance system analysis and epidemiological analysis. In this presentation application of this methodology to Foot and Mouth Disease, Ebola and Influenza and E.coli related gastrointestinal disease surveillance will be demonstrated.

Multi-Criteria Decision Analysis (MCDA) provides a methodology to approach the problem of biosurveillance system component evaluation. Los Alamos National Laboratory has developed a robust evaluation framework in which we use MCDA to assist in the evaluation of data streams for use in an integrated global biosurveillance system. We identified several potentially useful data streams through this technique and propose that MCDA may be a valuable tool, provided a robust evaluation framework is available.

Living in a closely connected and highly mobile world presents many new mechanisms for rapid disease spread and in recent years, global disease surveillance has become a high priority. In addition, much like the contribution of non-traditional medicine to curing diseases, non-traditional data streams are being considered of value in disease surveillance. Los Alamos National Laboratory is determining the relevance of various data streams through the use of defined metrics and methodologies. An overview of this project will be presented, together with results of data stream evaluation.

In order to assess the utility and effectiveness of different data streams for global disease surveillance, a comprehensive survey of current human, animal, plant, and marine surveillance systems and data streams was undertaken. A relational database has been developed to allow for detailed analyses and searches of data streams and surveillance systems. A need for a biosurveillance one-stop shop has been increasingly called for to help in evaluating what data streams and systems are available and relevant for many different biosurveillance needs and goals. The prototype Biosurveillance Resource Directory is a searchable, dynamic database for biosurveillance system and source information.

For the past decade, the Food and Agriculture Organization of the United Nations has been working toward eradicating rinderpest through vaccination and intense surveillance by 2012. Because of the potential severity of a rinderpest epidemic, it is prudent to prepare for an unexpected outbreak in animal populations. There is no immunity to the disease among the livestock or wildlife in the United States (US). If rinderpest were to emerge in the US, the loss in livestock could be devastating. We predict the potential spread of rinderpest using a two-stage model for the spread of a multi-host infectious disease among agricultural animals in the US. The model incorporates large-scale interactions among US counties and the small-scale dynamics of disease spread within a county. The model epidemic was seeded in 16 locations and there was a strong dependence of the overall epidemic size on the starting location. The epidemics were classified according to overall size into small epidemics of 100 to 300 animals (failed epidemics), epidemics infecting 3 000 to 30 000 animals (medium epidemics), and the large epidemics infecting around one million beef cattle. The size of the rinderpest epidemics were directly related to the origin of the disease and whether or not the disease moved into certain key counties in high-livestock-density areas of the US. The epidemic size also depended upon response time and effectiveness of movement controls.

WATER has been studied more extensively than any other liquid, Set its microscopic properties remain poorly understood. The difficulty in obtaining a rigorous molecular-scale description of water structure is largely a consequence of the extended, dynamic hydrogen-bonded network that exists throughout the liquid(1). Studies of the structure and dynamics of isolated small clusters of water molecules(2-6) provide a means of quantifying the intermolecular forces and hydrogen-bond rearrangements that occur in condensed phases. Experiments(2-7) and theory(8) strongly suggest that the water trimer, tetramer and pentamer have cyclic minimum energy structures. Larger water clusters are expected(8) to have three-dimensional geometries, with the hexamer representing the transition from cyclic to such three-dimensional structures. Here we report investigations by terahertz laser vibration-rotation tunnelling spectroscopy(3) of the structure of the water hexamer. A comparison of our results with quantum Monte Carlo simulations of this Species suggests that the most stable form of (H2O)(6) is indeed a cage-like structure, held together by eight hydrogen bonds (Fig. 1).

Resonance Raman spectroelectrochemistry (RR-SEC) at -20 degrees C has been performed on the pyrazine-bridged dimer of mu-oxo-centered trinuclear ruthenium-acetate "clusters"--[(dmap)(CO)(mu-OAc)6(mu3-O)Ru3(mu-L(b))Ru3(mu3-O)(mu-OAc)6(CO)(dmap)]n (where dmap = 4-(dimethylamino)pyridine and L(b) = pyrazine-h4 and pyrazine-d4)-in three oxidation states: n = 0, -1, and -2. In the one-electron reduced, "mixed-valent" state (overall -1 charge and a single odd electron; formal oxidation states [II, II, III]-[III, III, II] on the metal centers), the Raman excitation at 800 nm is resonant with a cluster-to-cluster intervalence charge-transfer (IVCT) band. Under these conditions, scattering enhancement is observed for all four totally symmetric vibrational modes of the bridging pyrazine ligand (nu8a, nu9a, nu1, and nu6a) in the investigated spectral range (100-2000 cm(-1)), and there is no evidence of activity in non-totally symmetric vibrations. Resonantly enhanced Raman peaks related to peripheral pyridyl (dmap) ligand modes and low-frequency features arising from the trigonal Ru3O cluster core and the cluster[Ru]-[N]ligand vibrations were also observed in the spectra of the intermediate-valence (n = -1) cluster dimer. The vibrational assignments and interpretations proposed in this work were reinforced by observation of characteristic isotopic frequency shifts accompanying deuteration of the bridging pyrazine. The results reveal that the fully symmetric (A(g)) vibrational motions of the organic bridge are coupled to the nominally metal cluster-to-metal cluster fast intramolecular electron transfer (ET) and provide validation of the near-delocalized description according to a predicted three-site/three-state (e.g., metal-bridge-metal) vibronic coupling model, in which the important role of the bridging ligand in mediating electronic communication and delocalization between charge centers is explicitly considered. Further compelling evidence supporting an extended five-state model, which incorporates the peripheral cluster-bound pyridyl ligands, is also presented.

The 1,2-dichloroethane (DCE)/water interface is important because of its suitability as an electrochemical interface between two immiscible electrolyte solutions (ITIES). An issue of particular interest is whether the interfacial region is molecularly sharp or whether the interface is comprised of a diffuse mixed interfacial region. These studies using vibrational sum-frequency spectroscopy as a probe of the structure, orientation, and bonding of interfacial water show that the interface is molecularly disordered with properties similar to a mixed phase interfacial region. It does not have the characteristics of a sharp interface that have been shown to occur at other liquid/liquid interfaces such as CCl4/H2O and alkane/H2O.

The novel charge-transfer ground state found in alpha,alpha'-diimine adducts of ytterbocene (C(5)Me(5))(2)Yb(L) [L = 2,2'-bipyridine (bpy) and 1,10-phenanthroline (phen)] in which an electron is spontaneously transferred from the f(14) metal center into the lowest unoccupied (pi*) molecular orbital (LUMO) of the diimine ligand to give an f(13)-L(*)(-) ground-state electronic configuration has been characterized by cyclic voltammetry, UV-vis-near-IR electronic absorption, and resonance Raman spectroscopies. The voltammetric data demonstrate that the diimine ligand LUMO is stabilized and the metal f orbital is destabilized by approximately 1.0 V each upon complexation for both bpy and phen adducts. The separation between the ligand-based oxidation wave (L(0/-)) and the metal-based reduction wave (Yb(3+/2+)) in the ytterbocene adducts is 0.79 V for both bpy and phen complexes. The UV-vis-near-IR absorption spectroscopic data for both the neutral adducts and the one-electron-oxidized complexes are consistent with those reported recently, but previously unreported bands in the near-IR have been recorded and assigned to ligand (pi*)-to-metal (f orbital) charge-transfer (LMCT) transitions. These optical electronic excited states are the converse of the ground-state charge-transfer process (e.g., f(13)-L(*-) <--> f(14)-L(0)). These new bands occur at approximately 5000 cm(-1) in both adducts, consistent with predictions from electrochemical data, and the spacings of the resolved vibronic bands in these transitions are consistent with the removal of an electron from the ligand pi* orbital. The unusually large intensity observed in the f --> f intraconfiguration transitions for the neutral phenanthroline adduct is discussed in terms of an intensity-borrowing mechanism involving the low-energy LMCT states. Raman vibrational data clearly reveal resonance enhancement for excitation into the low-lying pi* --> pi* ligand-localized excited states, and comparison of the vibrational energies with those reported for alkali-metal-reduced diimine ligands confirms that the ligands in the adducts are reduced radical anions. Differences in the resonance enhancement pattern for the modes in the bipyridine adduct with excitation into different pi* --> pi* levels illustrate the different nodal structures that exist in the various low-lying pi* orbitals.

Vibrational sum-frequency spectroscopy (VSFS) studies of a series of HOD/H2O/D2O mixtures ranging from pure D2O to pure H2O have been performed at the vapor/water interface. The various concentrations allow an iterative fitting procedure to be applied, resulting in a set of resonant peaks which consistently describe the vibrational modes of water molecules present in the interfacial region. The resonant sum-frequency response from the contributing vibrational modes allows more definitive characterization than in previous studies of the bonding interactions between surface water molecules. Comparison of the resonant spectrum of the vapor/H2O interface with the sum-frequency spectrum obtained at the CCl2/H2O interface reveals more similarity between the interfacial hydrogen-bonding environments than previously determined. Recent molecular dynamics simulations of VSF spectra of the vapor/H2O interface are in good agreement with the experimentally obtained spectra, and give insight into the molecular interactions in the interfacial region, as well as an estimate of the interfacial depth probed.

Resonance Raman spectroscopy, performed using spectroelectrochemistry and with excitation in the intervalence bands of three pyrazine-bridged, mixed-valence dimers of trinuclear ruthenium clusters, shows resonant enhancement of symmetric bridging ligand modes. The resonant enhancements and frequency shifts of these bridging ligand modes are observed as a function of varying electronic communication between charge sites, and they show that a three-state vibronic model which explicitly includes the participation of the bridging ligand is needed to explain the spectroscopic behavior of these near-delocalized complexes.