Increasing incidences of emerging and re-emerging diseases that are mostly zoonotic (e.g. severe acute respiratory syndrome, avian influenza H5N1, pandemic influenza) has led to the need for a multidisciplinary approach to tackling these threats to public and animal health. Accordingly, a global movement of 'One-Health/One-Medicine' has been launched to foster collaborative efforts amongst animal and human health officials and researchers to address these problems. Historical evidence points to the fact that pandemics caused by influenza A viruses remain a major zoonotic threat to mankind. Recently, a range of mathematical and computer simulation modelling methods and tools have increasingly been applied to improve our understanding of disease transmission dynamics, contingency planning and to support policy decisions on disease outbreak management. This review provides an overview of methods, approaches and software used for modelling the spread of zoonotic influenza viruses in animals and humans, particularly those related to the animal-human interface. Modelling parameters used in these studies are summarized to provide references for future work. This review highlights the limited application of modelling research to influenza in animals and at the animal-human interface, in marked contrast to the large volume of its research in human populations. Although swine are widely recognized as a potential host for generating novel influenza viruses, and that some of these viruses, including pandemic influenza A/H1N1 2009, have been shown to be readily transmissible between humans and swine, only one study was found related to the modelling of influenza spread at the swine-human interface. Significant gaps in the knowledge of frequency of novel viral strains evolution in pigs, farm-level natural history of influenza infection, incidences of influenza transmission between farms and between swine and humans are clearly evident. Therefore, there is a need to direct additional research to the study of influenza transmission dynamics in animals and at the animal-human interface.

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A review of simulation modelling approaches used for the spread of zoonotic influenza viruses in animal and human populations

Public health surveillance is undergoing a revolution driven by advances in the field of information technology. Many countries have experienced vast improvements in the collection, ingestion, analysis, visualization, and dissemination of public health data. Resource-limited countries have lagged behind due to challenges in information technology infrastructure, public health resources, and the costs of proprietary software. The Suite for Automated Global Electronic bioSurveillance (SAGES) is a collection of modular, flexible, freely-available software tools for electronic disease surveillance in resource-limited settings. One or more SAGES tools may be used in concert with existing surveillance applications or the SAGES tools may be used en masse for an end-to-end biosurveillance capability. This flexibility allows for the development of an inexpensive, customized, and sustainable disease surveillance system. The ability to rapidly assess anomalous disease activity may lead to more efficient use of limited resources and better compliance with World Health Organization International Health Regulations.

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SAGES: A Suite of Freely-Available Software Tools for Electronic Disease Surveillance in Resource-Limited Settings

Sea ice profoundly influences ocean circulation, the polar environment, biology, climate, and commercial activities. The rapidly changing sea ice environment and increased human activities in polar regions drive the demand for sea ice monitoring. Spaceborne synthetic aperture radar (SAR) has been widely adopted for sea ice sensing due to its all-weather, high spatial resolution, and day-and-night imaging capabilities. Previous reviews have addressed sea ice sensing based on various applications and sensors. However, no meta-analysis has been performed to specifically explore spaceborne polarimetric SAR-data-based sea ice sensing. Therefore, this study aims to provide a meta-analysis of spaceborne polarimetric SAR-data-based sea ice sensing by investigating 182 articles published in the last decade. Sea ice sensing applications for retrieving four key geophysical parameters (sea ice types, concentration, thickness, and motion) as well as SAR scattering characteristics analysis for sea ice are included. The review database was created with 15 fields including quantitative and qualitative perspectives, such as SAR frequency, polarization mode, methodology, evaluation metrics, etc. This meta-analysis aims to provide comparisons among different techniques and identify current challenges to determine effective methods for sea ice sensing. Overall, a snapshot of spaceborne polarimetric SAR-data-based sea ice sensing is presented through the meta-analysis, which could benefit researchers for future studies to advance this field.

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A Meta-Analysis of Sea Ice Monitoring Using Spaceborne Polarimetric SAR: Advances in the Last Decade

The last decade has witnessed the nine lowest Arctic September sea ice extents in the observational record. It also forms the most recent third of the long-term trend in that record, which reached -13.4% decade-1 in 2015. While hemispheric analyses paint a compelling picture of sea ice loss across the Arctic, the situation with multiyear ice in the Beaufort Sea is particularly dire. This study was undertaken in light of substantial changes that have occurred in the extent of summer multiyear sea ice in the Arctic inferred from the passive microwave record. To better elucidate these changes at a sub-regional scale, we use data from the Canadian Ice Service archive, the most direct observations of sea ice stage-of-development available. We also build upon the only previous sea ice climatological analysis for Canada's western Arctic region by sea ice stage-of-development that ended in 2004. The annual evolution of sea ice by stage of development in Canada's western Arctic changed dramatically between 1983 and 2014. The rate of these changes and their spatial prevalence were most prominent in the last decade. In summer, total sea ice loss occurred via reductions in old and first-year sea ice over increasingly large areas and over more months per year. Resultant delay of thermodynamic freeze up has increased the annual open water duration in the study region. The winter sea ice cover was increasingly composed of first-year sea ice at the expense of old ice. Breakup timing has not significantly changed in the region.

Replacement of Multiyear sea ice and Changes in the Open Water Season Duration in the Beaufort Sea Since 2004

Epidemiological modeling for infectious disease is important for disease management and its routine implementation needs to be facilitated through better description of models in an operational context. A standardized model characterization process that allows selection or making manual comparisons of available models and their results is currently lacking. A key need is a universal framework to facilitate model description and understanding of its features. Los Alamos National Laboratory (LANL) has developed a comprehensive framework that can be used to characterize an infectious disease model in an operational context. The framework was developed through a consensus among a panel of subject matter experts. In this paper, we describe the framework, its application to model characterization, and the development of the Biosurveillance Analytics Resource Directory (BARD; http://brd.bsvgateway.org/brd/), to facilitate the rapid selection of operational models for specific infectious/communicable diseases. We offer this framework and associated database to stakeholders of the infectious disease modeling field as a tool for standardizing model description and facilitating the use of epidemiological models.

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The Biosurveillance Analytics Resource Directory (BARD): Facilitating the Use of Epidemiological Models for Infectious Disease Surveillance

An algorithm for ice/water detection of ice less than 30 cm in thickness (thin ice) using dual-polarized synthetic aperture radar (SAR) and passive microwave data has been developed based on insights from previous work. The frequency chosen from those available was in the C-band. For the radiometer layer ice concentrations, land contaminated pixels were removed. For the SAR layers, a novel approach to their use in ice detection was developed using the coefficient of variation (COV), defined as the ratio of the standard deviation of the normalized radar cross section, $\sigma _{0}$ , to the mean $\sigma _{0}$ . The COV reduced the complicated signatures of breaking waves and thin ice to bright ice pixels on a dark water background. The co-pol and cross-pol COV layers and the radiometer layer were merged according to physics-based rules, except in regions adjacent to land or the ice edge where only the SAR layers were valid. The detection algorithm was applied to 233 Sentinel 1a and 1b dual-polarization images of the freeze-up from 2015, 2016, 2018, and 2019 in the Beaufort and Chukchi Seas. The synergistic ice retrievals showed improved agreement with manual operational analyses in the marginal ice zone over other automated operational products.

Active/Passive Multiple Polarization Sea Ice Detection During Initial Freeze-Up

The Pandemic Influenza Policy Model (PIPM) is a collaborative computer modeling effort between the U.S. Department of Defense (DoD) and the Johns Hopkins University Applied Physics Laboratory. Many helpful computer simulations exist for examining the propagation of pandemic influenza in civilian populations. We believe the mission-oriented nature and structured social composition of military installations may result in pandemic influenza intervention strategies that differ from those recommended for civilian populations. Intervention strategies may differ between military bases because of differences in mission, location, or composition of the population at risk. The PIPM is a web-accessible, user-configurable, installation-specific disease model allowing military planners to evaluate various intervention strategies. Innovations in the PIPM include expanding on the mathematics of prior stochastic models, using military-specific social network epidemiology, utilization of DoD personnel databases to...

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The Pandemic Influenza Policy Model: A Planning Tool for Military Public Health Officials

Combustive sound source arrays consisting of submersible combustion chambers filled with a hydrogen/oxygen mixture are employed to assess their effectiveness for seismic exploration applications. The combustive mixture is ignited via spark and radiates acoustic pulses capable of undersea deep sub-bottom sediment penetration. Since electrolytic cells may be employed to generate the hydrogen/oxygen mixture from surrounding seawater, this source is an attractive alternative to airgun arrays for Arctic seismic exploration from under-ice platforms. Combustive sound source array configurations were tested in a central Virginia basin with hydrophones deployed in a line at another nearby basin. Seismic reflections are compared against nearby geologic cross sections of the central Virginia seismic zone. Funding provided via UAF sub-award under NOAA Grant NA09NOS4000262 and ONR.

Jerrold E. Dietz

Papers

Active/Passive Multiple Polarization Sea Ice Detection During Initial Freeze-Up

The Pandemic Influenza Policy Model: A Planning Tool for Military Public Health Officials

Initial assessment of combustive sound source arrays as airgun alternatives for Arctic under-ice seismic exploration