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USGS1993

Escherichia coli and fecal-coliform bacteria as indicators of recreational water quality

In 1986, the U.S. Environmental Protection Agency (USEPA) recommended that Escherichia coli (E. coli) be used in place of fecal-coliform bacteria in State recreational water-quality standards as an indicator of fecal contamination. This announcement followed an epidemiological study in which E. coli concentration was shown to be a better predictor of swimming-associated gastrointestinal illness than fecal-coliform concentration. Water-resource managers from Ohio have decided to collect information specific to their waters and decide whether to use E. coli or fecal-coliform bacteria as the basis for State recreational water-quality standards. If one indicator is a better predictor of recreational water quality than the other and if the relation between the two indicators is variable, then the indicator providing the most accurate measure of recreational water quality should be used in water-quality standards. Water-quality studies of the variability of concentrations of E. coli to fecal-coliform bacteria have shown that (1) concentrations of the two indicators are positively correlated, (2) E. coli to fecal-coliform ratios differ considerably from site to site, and (3) the E. coli criteria recommended by USEPA may be more difficult to meet than current (1992) fecal-coliform standards. In this study, a statistical analysis was done on concentrations of E. coli and fecal-coliform bacteria in water samples collected by two government agencies in Ohio-- the U.S. Geological Survey (USGS) and the Ohio River Valley Water Sanitation Commission (ORSANCO). Data were organized initially into five data sets for statistical analysis: (1) Cuyahoga River, (2) Olentangy River, (3) Scioto River, (4) Ohio River at Anderson Ferry, and (5) Ohio River at Cincinnati Water Works and Tanners Creek. The USGS collected the data in sets 1, 2, and 3, whereas ORSANCO collected the data in sets 4 and 5. The relation of E. coli to fecal-coliform concentration was investigated by use of linear-regression analysis and analysis of covariance. Log-transformed E. coli and fecal-coliform concentrations were highly correlated in all data sets (r-values ranged from 0.929 to 0.984). Linear regression analysis on USGS and ORSANCO data sets showed that concentration of E. coli could be predicted from fecal-coliform concentration (coefficients of determination (R2) ranged from 0.863 to 0.970). Results of analysis of covariance (ANCOVA) indicated that the predictive equations among the three USGS data sets and two ORSANCO data sets were not significantly different and that the data could be pooled into two large data sets, one for USGS data and one for ORSANCO data. However, results of ANCOVA indicated that USGS and ORSANCO data could not be pooled into one large data set. Predictions of E. coli concentrations calculated for USGS And ORSANCO regression relations, based on fecal-coliform concentrations set to equal Ohio water-quality standards, further showed the differences in E. coli to fecal-coliform relations among data sets. For USGS data, a predicted geometric mean of 176 col/100 mL (number of colonies per 100 milliliters) was greater than the current geometric-mean E. coli standard for bathing water of 126 col/100mL. In contrast, for ORSANCO data, the predicted geometric mean of 101 col/100 mL was less than the current E. coli standard. The risk of illness associated with predicted E. coli concentrations for USGS and ORSANCO data was evaluated by use of the USEPA regression equation that predicts swimming-related gastroenteritis rates from E. coli concentrations.1 The predicted geometric-mean E. coli concentrations for bathing water of 176 col/100 mL for USGS data and 101 col/100 mL for ORSANCO data would allow 9.4 and 7.1 gastrointestinal illnesses per 1,000 swimmers, respectively. This prediction compares well with the illness rate of 8 individuals per 1,000 swimmers estimated by the USEPA for an E. coli concentration of 126 col/100 mL. Therefore, the

Water-Resources Investigations Report↗
USGS2022

Exploring and mitigating plague for One Health purposes

Purpose of Review In 2020, the Appropriations Committee for the U.S. House of Representatives directed the CDC to develop a national One Health framework to combat zoonotic diseases, including sylvatic plague, which is caused by the flea-borne bacterium Yersinia pestis . This review builds upon that multisectoral objective. We aim to increase awareness of Y. pestis and to highlight examples of plague mitigation for One Health purposes (i.e., to achieve optimal health outcomes for people, animals, plants, and their shared environment). We draw primarily upon examples from the USA, but also discuss research from Madagascar and Uganda where relevant, as Y. pestis has emerged as a zoonotic threat in those foci. Recent Findings Historically, the bulk of plague research has been directed at the disease in humans. This is not surprising, given that Y. pestis is a scourge of human history. Nevertheless, the ecology of Y. pestis is inextricably linked to other mammals and fleas under natural conditions. Accumulating evidence demonstrates Y. pestis is an unrelenting threat to multiple ecosystems, where the bacterium is capable of significantly reducing native species abundance and diversity while altering competitive and trophic relationships, food web connections, and nutrient cycles. In doing so, Y. pestis transforms ecosystems, causing “shifting baselines syndrome” in humans, where there is a gradual shift in the accepted norms for the condition of the natural environment. Eradication of Y. pestis in nature is difficult to impossible, but effective mitigation is achievable; we discuss flea vector control and One Health implications in this context. Summary There is an acute need to rapidly expand research on Y. pestis , across multiple host and flea species and varied ecosystems of the Western US and abroad, for human and environmental health purposes. The fate of many wildlife species hangs in the balance, and the implications for humans are profound in some regions. Collaborative multisectoral research is needed to define the scope of the problem in each epidemiological context and to identify, refine, and implement appropriate and effective mitigation practices.

Current Tropical Medicine Reports↗
USGS2026

From understanding to action: Integrating new and old methodologies to manage marine infectious disease

Marine diseases can have far-reaching effects on population, community and ecosystem health; however, our ability to track, predict and manage these diseases has, historically, been poor. As a result, the fields of disease ecology and epidemiology have developed at a slower pace for marine than terrestrial systems [ 1 ]. New methodologies, including genomic tools for diagnostics [ 2 , 3 ], transcriptomic tools for measuring host and pathogen responses to infection (e.g. [ 4 , 5 ]), regional oceanic modelling systems that estimate environmental conditions influencing pathogen dispersal and disease progression [ 6 ], artificial intelligence methods for quantifying pathology from images (e.g. [ 7 ]) and advanced disease modelling techniques [ 8 , 9 ] are precipitating a rapid increase in our understanding of marine pathosystems. In 2016, these efforts led to the first special issue of Philosophical Transactions of the Royal Society B ( Marine diseases, volume 371, issue 1689) focused entirely on marine disease ecology and evolution, and in 2020, the first book, Marine disease ecology, was devoted to this topic [ 10 ]. This special issue, focused on marine disease management , is being published a decade after the first Philosophical Transactions special issue on marine diseases. The shift to a management focus reflects an urgent need for management strategies to address high-impact diseases and the rapid methodological advances that have resulted. The papers included in this issue demonstrate the value of combining classical approaches (e.g. routine disease surveillance, reductionistic pathogen challenge trials, rapid throughput diagnostics) with cutting-edge technologies (e.g. high-resolution oceanographic models, Bayesian models, replicated transcriptomic studies) to identify drivers of disease, quantify impacts and suggest management strategies.

Philosophical Transactions of the Royal Society, S↗
USGS2011

Applying definitions of “asbestos” to environmental and “low-dose” exposure Levels and health effects, particularly malignant mesothelioma

Although asbestos research has been ongoing for decades, this increased knowledge has not led to consensus in many areas of the field. Two such areas of controversy include the specific definitions of asbestos, and limitations in understanding exposure-response relationships for various asbestos types and exposure levels and disease. This document reviews the current regulatory and mineralogical definitions and how variability in these definitions has led to difficulties in the discussion and comparison of both experimental laboratory and human epidemiological studies for asbestos. This review also examines the issues of exposure measurement in both animal and human studies, and discusses the impact of these issues on determination of cause for asbestos-related diseases. Limitations include the lack of detailed characterization and limited quantification of the fibers in most studies. Associated data gaps and research needs are also enumerated in this review.

Journal of Toxicology and Environmental Health, Pa↗
USGS2010

Persistence of highly pathogenic avian influenza H5N1 virus defined by agro-ecological niche

The highly pathogenic avian influenza (HPAI) H5N1 virus has spread across Eurasia and into Africa. Its persistence in a number of countries continues to disrupt poultry production, impairs smallholder livelihoods, and raises the risk a genotype adapted to human-to-human transmission may emerge. While previous studies identified domestic duck reservoirs as a primary risk factor associated with HPAI H5N1 persistence in poultry in Southeast Asia, little is known of such factors in countries with different agro-ecological conditions, and no study has investigated the impact of such conditions on HPAI H5N1 epidemiology at the global scale. This study explores the patterns of HPAI H5N1 persistence worldwide, and for China, Indonesia, and India includes individual provinces that have reported HPAI H5N1 presence during the 2004–2008 period. Multivariate analysis of a set of 14 agricultural, environmental, climatic, and socio-economic factors demonstrates in quantitative terms that a combination of six variables discriminates the areas with human cases and persistence: agricultural population density, duck density, duck by chicken density, chicken density, the product of agricultural population density and chicken output/input ratio, and purchasing power per capita. The analysis identifies five agro-ecological clusters, or niches, representing varying degrees of disease persistence. The agro-ecological distances of all study areas to the medoid of the niche with the greatest number of human cases are used to map HPAI H5N1 risk globally. The results indicate that few countries remain where HPAI H5N1 would likely persist should it be introduced.

EcoHealth↗
USGS2023

Revealing the extent of sea otter impacts on bivalve prey through multi-trophic monitoring and mechanistic models

Sea otters are apex predators that can exert considerable influence over the nearshore communities they occupy. Since facing near extinction in the early 1900s, sea otters are making a remarkable recovery in Southeast Alaska, particularly in Glacier Bay, the largest protected tidewater glacier fjord in the world. The expansion of sea otters across Glacier Bay offers both a challenge to monitoring and stewardship and an unprecedented opportunity to study the top-down effect of a novel apex predator across a diverse and productive ecosystem. Our goal was to integrate monitoring data across trophic levels, space, and time to quantify and map the predator–prey interaction between sea otters and butter clams Saxidomus gigantea , one of the dominant large bivalves in Glacier Bay and a favoured prey of sea otters. We developed a spatially-referenced mechanistic differential equation model of butter clam dynamics that combined both environmental drivers of local population growth and estimates of otter abundance from aerial survey data. We embedded this model in a Bayesian statistical framework and fit it to clam survey data from 43 intertidal and subtidal sites across Glacier Bay. Prior to substantial sea otter expansion, we found that butter clam density was structured by an environmental gradient driven by distance from glacier (represented by latitude) and a quadratic effect of current speed. Estimates of sea otter attack rate revealed spatial heterogeneity in sea otter impacts and a negative relationship with local shoreline complexity. Sea otter exploitation of productive butter clam habitat substantially reduced the abundance and altered the distribution of butter clams across Glacier Bay, with potential cascading consequences for nearshore community structure and function. Spatial variation in estimated sea otter predation processes further suggests that community context and local environmental conditions mediate the top-down influence of sea otters on a given prey. Overall, our framework provides high-resolution insights about the interaction among components of this food web and could be applied to a variety of other systems involving invasive species, epidemiology or migration.

Journal of Animal Ecology↗
USGS1979

Geochemical surveys in the United States in relation to health.

Geochemical surveys in relation to health may be classified as having one, two or three dimensions. One-dimensional surveys examine relations between concentrations of elements such as Pb in soils and other media and burdens of the same elements in humans, at a given time. The spatial distributions of element concentrations are not investigated. The primary objective of two-dimensional surveys is to map the distributions of element concentrations, commonly according to stratified random sampling designs based on either conceptual landscape units or artificial sampling strata, but systematic sampling intervals have also been used. Political units have defined sample areas that coincide with the units used to accumulate epidemiological data. Element concentrations affected by point sources have also been mapped. Background values, location of natural or technological anomalies and the geographic scale of variation for several elements often are determined. Three-dimensional surveys result when two-dimensional surveys are repeated to detect environmental changes. -Author

Philosophical Transactions of the Royal Society B:↗
USGS2020

A critical review on the potential impacts of neonicotinoid insecticide use: Current knowledge of environmental fate, toxicity, and implications for human health

Neonicotinoid insecticides are widely used in both urban and agricultural settings around the world. Historically, neonicotinoid insecticides have been viewed as ideal replacements for more toxic compounds, like organophosphates, due in part to their perceived limited potential to affect the environment and human health. This critical review investigates the environmental fate and toxicity of neonicotinoids and their metabolites and the potential risks associated with exposure. Neonicotinoids are found to be ubiquitous in the environment, drinking water, and food, with low-level exposure commonly documented below acceptable daily intake standards. Available toxicological data from animal studies indicate possible genotoxicity, cytotoxicity, impaired immune function, and reduced growth and reproductive success at low concentrations, while limited data from ecological or cross-sectional epidemiological studies have identified acute and chronic health effects ranging from acute respiratory, cardiovascular, and neurological symptoms to oxidative genetic damage and birth defects. Due to the heavy use of neonicotinoids and potential for cumulative chronic exposure, these insecticides represent novel risks and necessitate further study to fully understand their risks to humans.

Environmental Science: Processes and Impacts↗
USGS2007

Scale-dependent approaches to modeling spatial epidemiology of chronic wasting disease.

This e-book is the product of a second workshop that was funded and promoted by the United States Geological Survey to enhance cooperation between states for the management of chronic wasting disease (CWD). The first workshop addressed issues surrounding the statistical design and collection of surveillance data for CWD. The second workshop, from which this document arose, followed logically from the first workshop and focused on appropriate methods for analysis, interpretation, and use of CWD surveillance and related epidemiology data. Consequently, the emphasis of this e-book is on modeling approaches to describe and gain insight of the spatial epidemiology of CWD. We designed this e-book for wildlife managers and biologists who are responsible for the surveillance of CWD in their state or agency. We chose spatial methods that are popular or common in the spatial epidemiology literature and evaluated them for their relevance to modeling CWD. Our opinion of the usefulness and relevance of each method was based on the type of field data commonly collected as part of CWD surveillance programs and what we know about CWD biology, ecology, and epidemiology. Specifically, we expected the field data to consist primarily of the infection status of a harvested or culled sample along with its date of collection (not date of infection), location, and demographic status. We evaluated methods in light of the fact that CWD does not appear to spread rapidly through wild populations, relative to more highly contagious viruses, and can be spread directly from animal to animal or indirectly through environmental contamination. We discovered that many of the wellpublished methods were developed for fast-spreading human diseases, such as influenza and measles. While these methods are applicable to fast spreading wildlife diseases, such as foot-and-mouth disease or West Nile virus, many are not likely to work well for CWD. Only limited data exist to evaluate geographic and spatial spread because many locations where we find CWD tend to be locations where samples have just been taken or sample sizes have just become large enough to have a high probability of detecting a low prevalence. Consequently, methods that work well to describe or predict the spread of foot-and-mouth disease throughout England, which occurred within a year, do not work well for describing or predicting CWD spread. We did not exclude methods that we regarded as inappropriate; rather, we included methods that are commonly used for disease epidemiology and then discussed their applicability for modeling the spatial epidemiology of CWD. We hope including inappropriate methods with an explanation of why they are ill-suited for CWD will make it easier to drop them from consideration and explain to others why they were not recommended for spatial modeling of CWD. We organized the three chapters by scale and extent for which each method was developed or best suited. The first chapter covers methods appropriate to multi-jurisdictional or multi-state modeling, which we call “regional” scale. The second chapter covers methods appropriate for within state areas such as wildlife management units or metapopulations, which we call “landscape” scale. The third chapter covers methods appropriate for population or individual-based modeling, which we call “fine” scale. We know this rubric is somewhat artificial because many methods work at multiple scales. We hope, however, that this structure addresses some of the challenges faced by managers that work at local, regional, state, and national scales. Further, the resolution of empirical data often changes with spatial scale, which affects the utility of different modeling approaches. For example, individual-based models work best at modeling spread within populations, while risk analysis is most useful for summarizing data over larger scales such as a region. Because some methods are applicable at several scales, however, we included a graphic at the beginning of each method that indicates the range of scales for which it applies. For example, the graphic to the right indicates that the method is most applicable for regional-scale modeling. There is also a question of resolution as well as scale and extent for each method. CWD surveillance data have been collected over large areas, such as a wildlife management unit or state, but the resolution of the data may be fine scale with GPS locations for many samples. For each method, we described the required resolution of the data and describe the type of data required, as well as what questions the method could answer and how useful the method is, given typical CWD data. For each scale, we presented a focal approach that would be useful for understanding the spatial pattern and epidemiology of CWD, as well as being a useful tool for CWD management. The focal approaches include risk analysis and micromaps for the regional scale, cluster analysis for the landscape scale, and individual based modeling for the fine scale of within population. For each of these methods, we used simulated data and walked through the method step by step to fully illustrate the “how to”, with specifics about what is input and output, as well as what questions the method addresses. We also provided a summary table to, at a glance, describe the scale, questions that can be addressed, and general data required for each method described in this e-book. We hope that this review will be helpful to biologists and managers by increasing the utility of their surveillance data, and ultimately be useful for increasing our understanding of CWD and allowing wildlife biologists and managers to move beyond retroactive fire-fighting to proactive preventative action.

Book↗
USGS2013

Geologic occurrences of erionite in the United States: an emerging national public health concern for respiratory disease

Erionite, a mineral series within the zeolite group, is classified as a Group 1 known respiratory carcinogen. This designation resulted from extremely high incidences of mesothelioma discovered in three small villages from the Cappadocia region of Turkey, where the disease was linked to environmental exposures to fibrous forms of erionite. Natural deposits of erionite, including fibrous forms, have been identified in the past in the western United States. Until recently, these occurrences have generally been overlooked as a potential hazard. In the last several years, concerns have emerged regarding the potential for environmental and occupational exposures to erionite in the United States, such as erionite-bearing gravels in western North Dakota mined and used to surface unpaved roads. As a result, there has been much interest in identifying locations and geologic environments across the United States where erionite occurs naturally. A 1996 U.S. Geological Survey report describing erionite occurrences in the United States has been widely cited as a compilation of all US erionite deposits; however, this compilation only focused on one of several geologic environments in which erionite can form. Also, new occurrences of erionite have been identified in recent years. Using a detailed literature survey, this paper updates and expands the erionite occurrences database, provided in a supplemental file (US_erionite.xls). Epidemiology, public health, and natural hazard studies can incorporate this information on known erionite occurrences and their characteristics. By recognizing that only specific geologic settings and formations are hosts to erionite, this knowledge can be used in developing management plans designed to protect the public.

Arizona;California;Colorado;Idaho;Montana;New Mexi↗
USGS2014

Atmospheric particulate matter in proximity to mountaintop coal mines: Sources and potential environmental and human health impacts

Mountaintop removal mining (MTM) is a widely used approach to surface coal mining in the US Appalachian region whereby large volumes of coal overburden are excavated using explosives, removed, and transferred to nearby drainages below MTM operations. To investigate the air quality impact of MTM, the geochemical characteristics of atmospheric particulate matter (PM) from five surface mining sites in south central West Virginia, USA, and five in-state study control sites having only underground coal mining or no coal mining whatsoever were determined and compared. Epidemiologic studies show increased rates of cancer, respiratory disease, cardiovascular disease, and overall mortality in Appalachian surface mining areas compared to Appalachian non-mining areas. In the present study, 24-h coarse (>2.5 µm) and fine (≤2.5 µm) PM samples were collected from two surface mining sites in June 2011 showed pronounced enrichment in elements having a crustal affinity (Ga, Al, Ge, Rb, La, Ce) contributed by local sources, relative to controls. Follow-up sampling in August 2011 lacked this enrichment, suggesting that PM input from local sources is intermittent. Using passive samplers, dry deposition total PM elemental fluxes calculated for three surface mining sites over multi-day intervals between May and August 2012 were 5.8 ± 1.5 times higher for crustal elements than at controls. Scanning microscopy of 2,249 particles showed that primary aluminosilicate PM was prevalent at surface mining sites compared to secondary PM at controls. Additional testing is needed to establish any link between input of lithogenic PM and disease rates in the study area.

West Virginia↗
USGS2017

Marine infectious disease ecology

To put marine disease impacts in context requires a broad perspective on the roles infectious agents have in the ocean. Parasites infect most marine vertebrate and invertebrate species, and parasites and predators can have comparable biomass density, suggesting they play comparable parts as consumers in marine food webs. Although some parasites might increase with disturbance, most probably decline as food webs unravel. There are several ways to adapt epidemiological theory to the marine environment. In particular, because the ocean represents a three-dimensional moving habitat for hosts and parasites, models should open up the spatial scales at which infective stages and host larvae travel. In addition to open recruitment and dimensionality, marine parasites are subject to fishing, filter feeders, dosedependent infection, environmental forcing, and death-based transmission. Adding such considerations to marine disease models will make it easier to predict which infectious diseases will increase or decrease in a changing ocean.

Annual Review of Ecology, Evolution, and Systemati↗
USGS2008

Results of a Survey of Residential Home Heating Fuel and Stove Type and Use in the Shiprock Area of the Navajo Nation

For many Navajo people, coal provides an affordable and convenient means of home heating. However, coal combustion results in the formation and mobilization of materials that are known risk factors for respiratory and other diseases. The level of respiratory morbidity among the Navajo people is higher than can be explained by usual epidemiological risk factors. The Shiprock area of the Navajo Nation is somewhat unique in that atmospheric thermal inversions trap air pollution low to the ground, especially in winter. There are two large mine mouth coal-fired power plants located in the vicinity, with a third plant in the planning stages. Both of the existing power plants are exempt from regulation under the U.S. Environmental Protection Agency 1990 Amendments to the Clean Air Act due to their age. The purpose of this survey was to assess the fuel and stove type and use, and document other household characteristics that might be related to the exposure of potentially toxic coal combustion products. A total of 137 surveys was conducted in English and Navajo to ascertain and document fuel usage and the type, size and conditions of heating stoves used in both traditional and modern homes. Results have been presented to the community at the Shiprock Chapter in the Navajo language. To increase public awareness, ways to properly use and store coal and to improve stove function and ventilation were also shared.

Open-File Report↗
USGS2021

Sustaining transmission in different host species: The emblematic case of Sarcoptes scabiei

Some pathogens sustain transmission in multiple different host species, but how this epidemiologically important feat is achieved remains enigmatic. Sarcoptes scabiei is among the most host generalist and successful of mammalian parasites. We synthesize pathogen and host traits that mediate sustained transmission and present cases illustrating three transmission mechanisms (direct, indirect, and combined). The pathogen traits that explain the success of S. scabiei include immune response modulation, on-host movement capacity, off-host seeking behaviors, and environmental persistence. Sociality and host density appear to be key for hosts in which direct transmission dominates, whereas in solitary hosts, the use of shared environments is important for indirect transmission. In social den-using species, combined direct and indirect transmission appears likely. Empirical research rarely considers the mechanisms enabling S. scabiei to become endemic in host species—more often focusing on outbreaks. Our review may illuminate parasites’ adaptation strategies to sustain transmission through varied mechanisms across host species.

BioScience↗
USGS2001

. Ecological conceptual models: a framework and case study on ecosystem management for South Florida sustainability

The Everglades and South Florida ecosystems are the focus of national and international attention because of their current degraded and threatened state. Ecological risk assessment, sustainability and ecosystem and adaptive management principles and processes are being used nationally as a decision and policy framework for a variety of types of ecological assessments. The intent of this study is to demonstrate the application of these paradigms and principles at a regional scale. The effects-directed assessment approach used in this study consists of a retrospective, eco-epidemiological phase to determine the causes for the current conditions and a prospective predictive risk-based assessment using scenario analysis to evaluate future options. Embedded in these assessment phases is a process that begins with the identification of goals and societal preferences which are used to develop an integrated suite of risk-based and policy relevant conceptual models. Conceptual models are used to illustrate the linkages among management (societal) actions, environmental stressors, and societal/ecological effects, and provide the basis for developing and testing causal hypotheses. These models, developed for a variety of landscape units and their drivers, stressors, and endpoints, are used to formulate hypotheses to explain the current conditions. They are also used as the basis for structuring management scenarios and analyses to project the temporal and spatial magnitude of risk reduction and system recovery. Within the context of recovery, the conceptual models are used in the initial development of performance criteria for those stressors that are determined to be most important in shaping the landscape, and to guide the use of numerical models used to develop quantitative performance criteria in the scenario analysis. The results will be discussed within an ecosystem and adaptive management framework that provides the foundation for decision making.

Science of the Total Environment↗
USGS2025

A nationwide evaluation of crowd-sourced ambient temperature data

Growing concerns about heat in urban areas paired with the sparsity of weather stations have resulted in individuals drawing on data from citizen science sensor networks to fill in data gaps. The PurpleAir are the most widely-used low-cost air quality sensors in the contiguous United States with 14,777 deployed between 1 Jan 2017 and 20 July 2021. Although the air quality data from PurpleAir sensors have been widely studied, less attention has been paid to reported temperature. We compare temperature metrics reported by PurpleAir sensors with a gridded temperature product, the North American Land Data Assimilation System (NLDAS)-2, which although not a gold-standard measure of temperature, is widely used in epidemiologic research. We evaluate the lag between indoor and outdoor PurpleAir temperatures. We report associations of the difference between outdoor PurpleAir temperatures and NLDAS-2 temperatures, an indicator of degradation, and the duration of sensor operation. Finally, based on the temperature range recorded by the outdoor PurpleAir sensors vis.a.vis NLDAS-2 temperatures, we provide a list of 271 (2.5%) sensors potentially misclassified as outdoor and likely located indoors. We observed that outdoor PurpleAir sensors agreed well with NLDAS-2 (R2 > 0.82). This association broke down under warm conditions (daily average NLDAS ≥ 21.1oC). Landcover and climate zone were significant modifiers of the association between PurpleAir and NLDAS-2 temperature metrics with the poorest PurpleAir- NLDAS-2 agreement in urban areas. Indoor PurpleAir temperatures lagged hourly NLDAS temperatures by two hours across almost all climate zones. The mean difference in hourly PurpleAir and NLDAS-2 temperatures increased by 0.57oC for every operational year, suggesting that careful attention must be paid to degradation. Overall, we find for use in epidemiological studies, that the PurpleAir sensor data is a reliable metric of daytime mean air temperature, but researchers should be aware of its limitations when examining extreme heat, or when aggregating sensor data across multiple years.

Frontiers of Environmental Science and Engineering↗
USGS2021

Transient disease dynamics across ecological scales

Analyses of transient dynamics are critical to understanding infectious disease transmission and persistence. Identifying and predicting transients across scales, from within-host to community-level patterns, plays an important role in combating ongoing epidemics and mitigating the risk of future outbreaks. Moreover, greater emphases on non-asymptotic processes will enable timely evaluations of wildlife and human diseases and lead to improved surveillance efforts, preventive responses, and intervention strategies. Here, we explore the contributions of transient analyses in recent models spanning the fields of epidemiology, movement ecology, and parasitology. In addition to their roles in predicting epidemic patterns and endemic outbreaks, we explore transients in the contexts of pathogen transmission, resistance, and avoidance at various scales of the ecological hierarchy. Examples illustrate how (i) transient movement dynamics at the individual host level can modify opportunities for transmission events over time; (ii) within-host energetic processes often lead to transient dynamics in immunity, pathogen load, and transmission potential; (iii) transient connectivity between discrete populations in response to environmental factors and outbreak dynamics can affect disease spread across spatial networks; and (iv) increasing species richness in a community can provide transient protection to individuals against infection. Ultimately, we suggest that transient analyses offer deeper insights and raise new, interdisciplinary questions for disease research, consequently broadening the applications of dynamical models for outbreak preparedness and management.

Theoretical Ecology↗
USGS2016

Predicting recreational water quality advisories: A comparison of statistical methods

Epidemiological studies indicate that fecal indicator bacteria (FIB) in beach water are associated with illnesses among people having contact with the water. In order to mitigate public health impacts, many beaches are posted with an advisory when the concentration of FIB exceeds a beach action value. The most commonly used method of measuring FIB concentration takes 18–24 h before returning a result. In order to avoid the 24 h lag, it has become common to ”nowcast” the FIB concentration using statistical regressions on environmental surrogate variables. Most commonly, nowcast models are estimated using ordinary least squares regression, but other regression methods from the statistical and machine learning literature are sometimes used. This study compares 14 regression methods across 7 Wisconsin beaches to identify which consistently produces the most accurate predictions. A random forest model is identified as the most accurate, followed by multiple regression fit using the adaptive LASSO.

Environmental Modelling and Software↗