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  1. Morrison, Amy C (Ed.)

    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.

     
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    Free, publicly-accessible full text available November 6, 2025
  2. Alam, Mumtaz (Ed.)

    When COVID-19 was first introduced to the United States, state and local governments enacted a variety of policies intended to mitigate the virulence of the epidemic. At the time, the most effective measures to prevent the spread of COVID-19 included stay-at-home orders, closing of nonessential businesses, and mask mandates. Although it was well known that regions with high population density and cold climates were at the highest risk for disease spread, rural counties that are economically reliant on tourism were incentivized to enact fewer precautions against COVID-19. The uncertainty of the COVID-19 pandemic, the multiple policies to reduce transmission, and the changes in outdoor recreation behavior had a significant impact on rural tourism destinations and management of protected spaces. We utilize fine-scale incidence and demographic data to study the relationship between local economic and political concerns, COVID-19 mitigation measures, and the subsequent severity of outbreaks throughout the continental United States. We also present results from an online survey that measured travel behavior, health risk perceptions, knowledge and experience with COVID-19, and evaluation of destination attributes by 407 out-of-state visitors who traveled to Maine from 2020 to 2021. We synthesize this research to present a narrative on how perceptions of COVID-19 risk and public perceptions of rural tourism put certain communities at greater risk of illness throughout 2020. This research could inform future rural destination management and public health policies to help reduce negative socioeconomic, health and environmental impacts of pandemic-derived changes in travel and outdoor recreation behavior.

     
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    Free, publicly-accessible full text available April 9, 2025
  3. Althouse, Benjamin Muir (Ed.)
    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. 
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  4. null (Ed.)
  5. Abstract

    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.

     
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