Risk perceptions and behaviors concerning rural tourism and economic-political drivers of COVID-19 policy in 2020.

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.

Epidemic spread on patch networks with community structure.

Predicting and preparing for the trajectory of disease epidemics relies on a knowledge of environmental and socioeconomic factors that affect transmission rates on local and global spatial scales. This article discusses the simulation of epidemic outbreaks on human metapopulation networks with community structure, such as cities within national boundaries, for which infection rates vary both within and between communities. We demonstrate mathematically, through next-generation matrices, that the structures of these communities, setting aside all other considerations such as disease virulence and human decision-making, have a profound effect on the reproduction rate of the disease throughout the network. In high modularity networks, with high levels of separation between neighboring communities, disease epidemics tend to spread rapidly in high-risk communities and very slowly in others, whereas in low modularity networks, the epidemic spreads throughout the entire network as a steady pace, with little regard for variations in infection rate. The correlation between network modularity and effective reproduction number is stronger in population with high rates of human movement. This implies that the community structure, human diffusion rate, and disease reproduction number are all intertwined, and the relationships between them can be affected by mitigation strategies such as restricting movement between and within high-risk communities. We then test through numerical simulation the effectiveness of movement restriction and vaccination strategies in reducing the peak prevalence and spread area of outbreaks. Our results show that the effectiveness of these strategies depends on the structure of the network and the properties of the disease. For example, vaccination strategies are most effective in networks with high rates of diffusion, whereas movement restriction strategies are most effective in networks with high modularity and high infection rates. Finally, we offer guidance to epidemic modelers as to the ideal spatial resolution to balance accuracy and data collection costs.

Patterns and Ecological Mechanisms of Tick-Borne Disease Exposure Risk in Acadia National Park, Mount Desert Island, Maine, United States.

National parks are unique and significant vector-borne pathogen transmission settings, engaging over 300 million people in outdoor recreation per year. In this study, we integrated vector surveys and ecological habitat feature data in spatial models to characterize tick-borne disease exposure risk in Acadia National Park (ANP), Maine. To determine the broad-scale patterns of blacklegged tick Ixodes scapularis Say (Acari: Ixodidae) densities in ANP, we conducted host-seeking tick collections at 114 sites across the park over two years. Using these tick survey data and geospatial landscape feature data (i.e., land cover, elevation, forest patch size, and aspect) we developed a random forest model of nymphal tick density. We found that host-seeking tick density varies significantly across the park and is particularly high in areas characterized by deciduous forest cover and relatively low elevation. To explore potential fine-scale ecological drivers of tick density spatial patterns, we quantified microclimate conditions, host activity, and vegetation characteristics at a subset of 19 sites. We identified significant differences in microclimate conditions but not host activity or vegetation metrics across broad-scale landscape feature classes. Mean temperature and mean humidity were correlated to nymphal densities and therefore may provide a mechanistic link between landscape features and blacklegged tick densities. Finally, we detected multiple tick-borne pathogens in both ticks and small mammals sampled in ANP, including Borrelia burgdorferi, Babesia microti, and Anaplasma phagocytophilum. Our findings demonstrate the value of using ecological metrics to estimate vector-borne disease exposure risk and provide insight into habitat characteristics that may drive tick-borne disease exposure risk.

Connectivity, reproduction number, and mobility interact to determine communities' epidemiological superspreader potential in a metapopulation network.

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.

Production of Culex pipiens in Stormwater and Combined Sewer Catch Basins.

Man-made stormwater and sewage infrastructure, particularly roadside catch basins, provides widespread habitats for immature mosquitoes in urban and suburban environments. Historically, throughout much of the USA, stormwater, sewage, and industrial wastewater were conducted together through "combined" sewer systems, discharging a combination of stormwater and wastewater into streams. Within recent decades, many cities have replaced these combined sewers with "stormwater only" systems that separate stormwater from wastewater. The objective of this research was to evaluate the implications of this infrastructure conversion for production of Culex pipiens, a primary vector for West Nile virus. On a weekly basis over 14 wk, 20 catch basins (10 combined sewer and 10 stormwater only) were sampled for mosquito larvae and emerging adults using the dipping collection method and floating emergence traps. Abundance of larval Cx. pipiens was higher in combined sewer compared with stormwater-only catch basins, while to the contrary, abundance of adult Cx. pipiens was lower in combined sewer compared with stormwater-only catch basins. This study is the first to reveal that habitat attractiveness and quality for Cx. pipiens may vary between combined sewer and stormwater-only catch basins, and our results contribute to a growing body of research to inform vector management and urban planning efforts as municipalities consider the environmental and public health implications of conversion from combined sewage management to separation of stormwater and wastewater.