Disease epidemic outbreaks on human metapopulation networks are often driven by a small number of superspreader nodes, which are primarily responsible for spreading the disease throughout the network. Superspreader nodes typically are characterized either by their locations within the network, by their degree of connectivity and centrality, or by their habitat suitability for the disease, described by their reproduction number (R). Here we introduce a model that considers simultaneously the effects of network properties and R on superspreaders, as opposed to previous research which considered each factor separately. This type of model is applicable to diseases for which habitat suitability varies by climate or land cover, and for direct transmitted diseases for which population density and mitigation practices influences R. We present analytical models that quantify the superspreader capacity of a population node by two measures: probability-dependent superspreader capacity, the expected number of neighboring nodes to which the node in consideration will randomly spread the disease per epidemic generation, and time-dependent superspreader capacity, the rate at which the node spreads the disease to each of its neighbors. We validate our analytical models with a Monte Carlo analysis of repeated stochastic Susceptible-Infected-Recovered (SIR) simulations on randomly generated human population networks, and we use a random forest statistical model to relate superspreader risk to connectivity, R, centrality, clustering, and diffusion. We demonstrate that either degree of connectivity or R above a certain threshold are sufficient conditions for a node to have a moderate superspreader risk factor, but both are necessary for a node to have a high-risk factor. The statistical model presented in this article can be used to predict the location of superspreader events in future epidemics, and to predict the effectiveness of mitigation strategies that seek to reduce the value of R, alter host movements, or both.

Taylor blast wave (TBW) theory and geometrical shock dynamics (GSD) theory describe a radially expanding shock wave front through an inert material, typically an ideal gas, in the strong blast wave limit and weak acoustic limit respectively. We simulate a radially expanding blast shock in air using a hydrodynamic simulation code and numerically describe the intermediate region between these two limits. We test our description of the intermediate shock phase through a two-dimensional simulation of the Bryson and Gross experiment. We then apply the principles of GSD to materials that follow the Mie–Gruneisen equation of state, such as plastics and metals, and derive an equation that accurately relates the acceleration, velocity and curvature of the shock through these materials. Along with detonation shock dynamics (DSD), which describes detonation shock propagation through high explosive fluids, we develop a hybrid DSD/GSD model for the simulation of heterogeneous explosives. This model enables computationally efficient simulation of the shock front in high explosive/inert mixtures consisting of simple or complex geometric configurations. We simulate an infinite two-dimensional slab consisting of one half explosive, PBXN-9, and one half aluminium and model the boundary angle conditions using shock polar analysis. We also simulate a series of high explosive unit cells embedded with aluminium spherical particles, and we compare the propagation of the detonation shock front with a direct numerical simulation performed with the ALE3D code.

The Damariscotta River in midcoast Maine is a weakly stratified estuary characterized by several constrictions and channel bends that affect tidal asymmetry and material transport. Microstructure and current velocity measurements were collected at a cross‐channel transect in the northern reach of the estuary during spring and neap tidal conditions to study the effects of a channel bend immediately upstream of a constricted sill on intratidal dynamics. During the flood phase, a counterclockwise gyre is formed upstream of the headland, which enhances landward‐directed flow in the channel and is countered by seaward‐directed flow over the shallow shoal. Semidiurnal and quarter‐diurnal patterns of lateral advection and stress divergence emerge in the surface layer because of these secondary flows. Lateral advection effects dominate the dynamics in neap tide, and although they are stronger in spring tide, they are partially obscured by bottom friction forces that are proportional to the along‐channel velocity squared. A novel harmonic decomposition technique is introduced to determine the relative importance of advection and stress divergence on quarter‐diurnal velocity generation, and their implications to neap/spring variability in estuarine water quality are discussed.

This paper discusses the mathematical formulation of Detonation Shock Dynamics (DSD) regarding a detonation shock wave passing over a series of inert spherical particles embedded in a high-explosive material. DSD provides an efficient method for studying detonation front propagation in such materials without the necessity of simulating the combustion equations for the entire system. We derive a series of partial differential equations in a cylindrical coordinate system and a moving shock-attached coordinate system which describes the propagation of detonation about a single particle, where the detonation obeys a linear shock normal velocity-curvature (D_n–κ) DSD relation. We solve these equations numerically and observe the short-term and long-term behaviour of the detonation shock wave as it passes over the particles. We discuss the shape of the perturbed shock wave and demonstrate the periodic and convergent behaviour obtained when detonation passes over a regular, periodic array of inert spherical particles.

The Zika virus epidemic of 2015–16, which caused over 1 million confirmed or suspected human cases in the Caribbean and Latin America, was driven by a combination of movement of infected humans and availability of suitable habitat for mosquito species that are key disease vectors. Both human mobility and mosquito vector abundances vary seasonally, and the goal of our research was to analyze the interacting effects of disease vector densities and human movement across metapopulations on disease transmission intensity and the probability of super-spreader events. Our research uses the novel approach of combining geographical modeling of mosquito presence with network modeling of human mobility to offer a comprehensive simulation environment for Zika virus epidemics that considers a substantial number of spatial and temporal factors compared to the literature. Specifically, we tested the hypotheses that 1) regions with the highest probability of mosquito presence will have more super-spreader events during dry months, when mosquitoes are predicted to be more abundant, 2) regions reliant on tourism industries will have more super-spreader events during wet months, when they are more likely to contribute to network-level pathogen spread due to increased travel. We used the case study of Colombia, a country with a population of about 50 million people, with an annual calendar that can be partitioned into overlapping cycles of wet and dry seasons and peak tourism and off tourism seasons that drive distinct cyclical patterns of mosquito abundance and human movement. Our results show that whether the first infected human was introduced to the network during the wet versus dry season and during the tourism versus off tourism season profoundly affects the severity and trajectory of the epidemic. For example, Zika virus was first detected in Colombia in October of 2015. Had it originated in January, a dry season month with high rates of tourism, it likely could have infected up to 60% more individuals and up to 40% more super-spreader events may have occurred. In addition, popular tourism destinations such as Barranquilla and Cartagena have the highest risk of super-spreader events during the winter, whereas densely populated areas such as Medellín and Bogotá are at higher risk of sustained transmission during dry months in the summer. Our research demonstrates that public health planning and response to vector-borne disease outbreaks requires a thorough understanding of how vector and host patterns vary due to seasonality in environmental conditions and human mobility dynamics. This research also has strong implications for tourism policy and the potential response strategies in case of an emergent epidemic. The Zika virus pandemic of 2015–16, which caused over 1 million human cases in the Caribbean and Latin America, was driven by a combination of movement of infected humans and presence of mosquito species, such as Aedes aegypti, that carry the disease. Both human mobility and mosquito vector densities vary seasonally, and the goal of our research was to analyze the interacting effects of disease vector presence and human movement across metapopulations on disease transmission intensity and the probability of super-spreader events. Specifically, we tested the hypotheses that 1) regions with the highest probability of mosquito presence will have more super-spreader events during dry months, when mosquitoes are predicted to be more abundant, 2) regions reliant on tourism industries will have more super-spreader events during wet months, as they are more likely to contribute to network-level pathogen spread due to increased travel. We used the case study of Colombia, a nation with a population of about 50 million people, whose annual calendar can be partitioned into overlapping cycles of wet and dry seasons and tourism and off-tourism seasons that drive distinct cyclical patterns of mosquito occurrence and human movement. Our approach combined spatial modeling of mosquito presence with network modeling of human mobility. We used a Maximum Entropy model to estimate the spatially and temporally varying habitat suitability for Aedes aegypti, the primary mosquito vector of Zika virus in Colombia, in terms of environmental factors such as temperature, precipitation, and vegetation coverage and human factors indicating land use and development, socioeconomic status, and prevalence of standing water that provides key habitat for juvenile mosquitoes. We also used airline travel data to develop metapopulation patch network models of the 1,123 municipalities of Colombia for each month of 2015 and 2016. We combined our vector habitat suitability and human mobility network models to simulate the Zika virus epidemic if it had begun in each month of 2015. Our results show that whether the first human case was introduced to the network during the wet vs. dry season and during the tourism vs. off-tourism season profoundly affects the severity and trajectory of the epidemic. For example, Zika virus was first detected in Colombia in October of 2015. Had it originated in January, a dry season month with high rates of tourism, it likely could have infected up to 30% more individuals and up to 40% more super-spreader events may have occurred. In addition, popular vacation spots such as Barranquilla and Cartageña have the highest risk of super-spreader events during the winter, whereas densely populated areas such as Medellín and Bogotá are at higher risk of sustained transmission during dry months in the summer. Our research demonstrates that public health planning and response to vector-borne disease outbreaks requires a thorough understanding of how vector and host patterns vary due to seasonality in environmental conditions and human mobility dynamics.