Predicting the Risk of Lyme Disease: Habitat Suitability for Ixodes scapularis in the North Central United States

The distribution and abundance of Ixodes scapularis were studied in Wisconsin, northern Illinois, and portions of the Upper Peninsula of Michigan by inspecting small mammals for ticks and by collecting questing ticks at 138 locations in state parks and natural areas. Environmental data were gathered at a local level (i.e., micro and meso levels), and a geographic information system (GIS) was used with several digitized coverages of environmental data to create a habitat profile for each site and a grid map for Wisconsin and Illinois. Results showed that the presence and abundance of I. scapularis varied, even when the host population was adequate. Tick presence was positively associated with deciduous, dry to mesic forests and alfisol-type soils of sandy or loam-sand textures overlying sedimentary rock. Tick absence was associated with grasslands, conifer forests, wet to wet/mesic forests, acidic soils of low fertility and a clay soil texture, and Precambrian bedrock. We performed a discriminant analysis to determine environmental differences between positive and negative tick sites and a regression equation to examine the probability of I. scapularis presence per grid. Both analyses indicated that soil order and land cover were the dominant contributors to tick presence. We then constructed a risk map indicating suitable habitats within areas where I. scapularis is already established. The risk map also shows areas of high probability the tick will become established if introduced. Thus, this risk analysis has both explanatory power and predictive capability

Suitability of maize crop residue fermented by Pleurotus ostreatus as feed for edible crickets: growth performance, micronutrient content, and iron bioavailability

Small-scale farming of edible insects could help combat public health challenges such as protein energy malnutrition and anemia, but reliable low-cost feeds for insects are needed. In resource-limited contexts, where grains such as maize are prohibitively costly for use as insect feed, the feasibility of insect farming may depend on finding alternatives. Here, we explore the potential to modify plentiful maize crop residue with edible mushroom mycelium to generate a low-cost feed adjunct for the farmed two-spotted cricket, Gryllus bimaculatus. Mushroom farming, like insect agriculture, is versatile; it can yield nutritious food while increasing system circularity by utilizing lignocellulosic residues from row crops as inputs. Pleurotus ostreatus, is an edible basidiomycete capable of being cultivated on corn stover (Zea mays). Mushroom harvest results in abundant “spent” substrate, which we investigated as a candidate feed ingredient. We created six cricket feeds containing fermented Pleurotus substrate plus an unfermented control, measuring cricket mass, mortality, and maturation weekly to evaluate cricket growth performance impacts of both fungal fermentation duration and mushroom formation. Pasteurized corn stover was inoculated with P. ostreatus mycelium and fermented for 0, 2, 3, 4, or 8 weeks. Some 4 and 8-week substrates were induced to produce mushrooms through manipulations of temperature, humidity, and light conditions. Dried fermented stover (40%) was added to a 1:1 corn/soy grain mix and fed to crickets ad libitum for 44 days. The unfermented control group showed higher survivorship compared to several fermented diets. Control group mass yield was higher for 2 out of 6 fermented diets. Little variation in cricket iron content was observed via ICP-spectrometry across feeds, averaging 2.46 mg/100 g. To determine bioavailability, we conducted in vitro Caco-2 human colon epithelial cell absorption assays, showing that iron in crickets fed fruiting-induced substrates was more bioavailable than in unfruited groups. Despite more bioavailable iron in crickets reared on post-fruiting substrates, we conclude that Pleurotus-fermented stover is an unsuitable feed ingredient for G. bimaculatus due to high mortality, variability in growth responses within treatments, and low mass yield

Partially observable latent class analysis (POLCA): An application to serial participation in mosquito control in Madison, Wisconsin

Serial nonparticipation in nonmarket valuation using choice data is a frequently observed pattern of behavior in which an individual always appears to choose the status quo or ‘no program’ alternative. In choice models serial nonparticipation may be viewed as belonging to a class of deterministic choice patterns, other examples of which include serial participation and lexicographic preferences. While common in the context of environmental goods unfamiliar to respondents, logit-based choice models are ill-equipped for identifying such preferences, because predicted choice probabilities cannot take a value of zero or one. We extend latent class analysis (LCA) of preference heterogeneity to address this issue, for each class specifying a subset of alternatives that are avoided with certainty. We are then able to partially observe class membership, knowing with certainty that an individual does not belong to a class if she selects any alternatives excluded by that class. We apply our model to a discrete choice experiment on mosquito control programs to reduce West Nile virus risk and nuisance disamenities in Madison, Wisconsin. We find that partially observable latent class analysis (POLCA) obtains the same goodness of fit as LCA with fewer parameters. Adjusting for the need to re-specify the reference alternative when the status quo is excluded, our relative valuation measures are significantly different than those obtained from LCA. We argue that our model is useful for detecting and addressing alternative-specific nonidentification in a given dataset, thus reducing the risk of invalid inference from discrete choice data

Partially observable latent class analysis (POLCA): An application to serial participation in mosquito control in Madison, WI

Serial nonparticipation in nonmarket valuation using choice data is a frequently observed pattern of behavior in which an individual always appears to choose the status quo or ‘no program’ alternative. In choice models serial nonparticipation may be viewed as belonging to a class of deterministic choice patterns, other examples of which include serial participation and lexicographic preferences. While common in the context of environmental goods unfamiliar to respondents, logit-based choice models are ill-equipped for identifying such preferences, because predicted choice probabilities cannot take a value of zero or one. We extend latent class analysis (LCA) of preference heterogeneity to address this issue, for each class specifying a subset of alternatives that are avoided with certainty. We are then able to partially observe class membership, knowing with certainty that an individual does not belong to a class if she selects any alternatives excluded by that class. We apply our model to a discrete choice experiment on mosquito control programs to reduce West Nile virus risk and nuisance disamenities in Madison, Wisconsin. We find that partially observable latent class analysis (POLCA) obtains the same goodness of fit as LCA with fewer parameters. Adjusting for the need to re-specify the reference alternative when the status quo is excluded, our relative valuation measures are significantly different than those obtained from LCA. We argue that our model is useful for detecting and addressing alternative-specific nonidentification in a given dataset, thus reducing the risk of invalid inference from discrete choice data

Partially observable latent class analysis (POLCA): An application to serial participation in mosquito control in Madison, Wisconsin

Serial nonparticipation in nonmarket valuation using choice data is a frequently observed pattern of behavior in which an individual always appears to choose the status quo or ‘no program’ alternative. In choice models serial nonparticipation may be viewed as belonging to a class of deterministic choice patterns, other examples of which include serial participation and lexicographic preferences. While common in the context of environmental goods unfamiliar to respondents, logit-based choice models are ill-equipped for identifying such preferences, because predicted choice probabilities cannot take a value of zero or one. We extend latent class analysis (LCA) of preference heterogeneity to address this issue, for each class specifying a subset of alternatives that are avoided with certainty. We are then able to partially observe class membership, knowing with certainty that an individual does not belong to a class if she selects any alternatives excluded by that class. We apply our model to a discrete choice experiment on mosquito control programs to reduce West Nile virus risk and nuisance disamenities in Madison, Wisconsin. We find that partially observable latent class analysis (POLCA) obtains the same goodness of fit as LCA with fewer parameters. Adjusting for the need to re-specify the reference alternative when the status quo is excluded, our relative valuation measures are significantly different than those obtained from LCA. We argue that our model is useful for detecting and addressing alternative-specific nonidentification in a given dataset, thus reducing the risk of invalid inference from discrete choice data

Impact of Consumption of Bananas on Attraction of Anopheles stephensi to Humans

Humans vary in attractiveness to mosquitoes, a phenomenon that is largely attributed to differences in physical cues such as heat and volatile odors emanating from breath and skin. Diet can change human odors but whether specific dietary components alter host attractiveness is largely unexplored. We identified bananas as a target for study following a survey of the internet for advice on avoiding mosquito bites. Human attractiveness to Anopheles stephensi Liston was measured using a glass vial bioassay where mosquito contacts were measured before and 1–3 h after ingestion of bananas or grapes. Consumption of grapes had no effect on the number of contacts but banana ingestion resulted in a significant increase in the overall number of contacts in spite of individual variation that included some subjects who showed no effect or decreases in contacts. Further tests with a single volunteer showed that the effect was repeatable and consistent across 15 trials. The magnitude of the increase was not affected by the number of bananas eaten. Increased contact counts after banana ingestion were also observed when A. gambiae Giles was tested. These results support the hypothesis that diet plays an important role in mediating host attractiveness to anopheline mosquitoes

Do-It-Yourself Tick Control: Granular Gamma-Cyhalothrin Reduces Ixodes scapularis (Acari: Ixodidae) Nymphs in Residential Backyards

Lyme disease is the most common vector-borne disease in the United States with hotspots in the Northeast and Midwest. Integrated vector control for mosquito-borne disease prevention is often organized at the community level, but tick control is primarily coordinated at the household and individual level. Management of the blacklegged tick, Ixodes scapularis (Say), the vector of the causative agent of Lyme disease in the Midwest and eastern United States in peridomestic environments may be critical as many tick encounters are reported to occur in the yard. Therefore, we assessed the effectiveness of a widely available and low-cost pesticide that targets common lawn pests and is labeled for use against ticks. In June 2019, we evaluated a granular form of gamma-cyhalothrin in a placebo-controlled residential backyard study (n = 90) in two communities in Wisconsin. The product applied by the research team reduced nymphal blacklegged ticks in plots established in the lawn part of the ecotone by 97% one week after application at both communities and by 89–97% three to four weeks postapplication. The proportion of homes with at least one nymphal tick postapplication was significantly lower at acaricide-treated homes and ranged from 4.2 to 29.2% compared with placebo homes where at least one nymphal tick was found at 50–81.5% of homes. These results support the efficacy of a low-cost do-it-yourself strategy for homeowners seeking to reduce blacklegged ticks in the yard

Field Evaluations of Three Botanically Inspired Repellents Against the Blacklegged Tick, Ixodes scapularis (Acari: Ixodidae)

Three compounds derived from botanicals sources, ethyl perillyl carbonate, geranyl isovalerate, and citronellyl cyclobutane carboxylate, were tested for repellent activity against Ixodes scapularis Say in a semi-field trial. Tick drags were treated with the compounds or with N, N-diethyl-m-toluamide (DEET) at high (0.25mg/cm2) or low (0.15mg/cm2) concentrations. Negative controls included untreated drags and drags treated with acetone, the carrier for all repellents. Freshly treated drags (within 20 minutes) were used to collect I. scapularis ticks at a county park in Wisconsin. To assess effectiveness, we measured tick encounter rates, detachment rate, and time to detachment. None of the repellent treatments resulted in significantly fewer encounters compared to both control treatments. However, the percentage of ticks that detached within 3 min was significantly higher on drags treated with repellents compared to controls. DEET was the most effective, repelling 69.7 - 87% of ticks by 3 min, but the effectiveness of the three test compounds was still high, ranging from 42% to 87% of ticks detaching by 3 min. For time to detachment, there were no significant differences between DEET and the three test compounds. We conclude that these botanically-derived repellents were effective against I. scapularis in a semi-field trial and could be viable alternatives to DEET.This is a manuscript of an article published as Xia Lee, Colin Wong, Joel Coats, Susan Paskewitz, Field Evaluations of Three Botanically Inspired Repellents Against the Blacklegged Tick, Ixodes scapularis (Acari: Ixodidae), Journal of Medical Entomology, Volume 59, Issue 5, September 2022, Pages 1694–1699, https://doi.org/10.1093/jme/tjac111. Posted with permission

A generalized latent class logit model of discontinuous preferences in repeated discrete choice data: an application to mosquito control in Madison, Wisconsin

Serial nonparticipation in nonmarket valuation using choice data is a pattern of behavior in which an individual always appears to choose the status quo or ‘no program’ alternative. From a choice modelling perspective serial nonparticipation may be viewed as belonging to a class of ‘discontinuous preferences,’ which also includes other behavioral patterns, such as serial participation (never choosing the status quo), as well as lexicographic preferences (e.g. always choosing the alternative with the greatest health benefit). Discontinuous preferences are likely to be especially relevant in the context of environmental goods, due to the lack of familiarity that individuals have with valuing these goods in markets. In the case of discrete choice data, logit-based choice models are ill-equipped for identifying such preferences, because conditional logit choice probabilities cannot take a value of zero or one for any finite parameter estimates. Here we extend latent class choice models to account for discontinuous preferences. Our methodological innovation is to specify for each latent class a subset of alternatives that are avoided with certainty. This results in class membership being partially observable, since we then know with certainty that an individual does not belong to a class if she selects any alternatives avoided by that class. We apply our model to data from a discrete choice experiment on mosquito control programs to reduce West Nile virus risk and nuisance disamenities in Madison, Wisconsin. We find that our ‘generalized latent class model’ (GLCM) outperforms standard latent class models in terms of information criteria metrics, and provides significantly different estimates for willingness-to-pay. We also argue that GLCMs are useful for identifying some alternatives for which valuation estimates may not be identified in a given dataset, thus reducing the risk of invalid inference from discrete choice data