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Environmental dimensions, such as temperature, precipitation, humidity, and vegetation type, influence the activity, survival, and geographic distribution of tick species. Ticks are vectors of various pathogens that cause disease in humans, andIxodes scapularisandAmblyomma americanumare among the tick species that transmit pathogens to humans across the central and eastern United States. Although their potential geographic distributions have been assessed broadlyviaecological niche modeling, no comprehensive study has compared ecological niche signals between ticks and tick-borne pathogens. We took advantage of National Ecological Observatory Network (NEON) data for these two tick species and associated bacteria pathogens across North America. We used two novel statistical tests that consider sampling and absence data explicitly to perform these explorations: a univariate analysis based on randomization and resampling, and a permutational multivariate analysis of variance. Based on univariate analyses, inAmblyomma americanum, three pathogens(Borrelia lonestari,Ehrlichia chaffeensis, andE. ewingii) were tested; pathogens showed nonrandom distribution in at least one environmental dimension. Based on the PERMANOVA test, the null hypothesis that the environmental position and variation of pathogen-positive samples are equivalent to those ofA. americanumcould not be rejected for any of the pathogens, except for the pathogenE. ewingiiin maximum and minimum vapor pressure and minimum temperature. ForIxodes scapularis,six pathogens (A. phagocytophilum,Babesia microti,Borrelia burgdorferisensu lato,B. mayonii,B. miyamotoi, andEhrlichia muris-like) were tested; onlyB. miyamotoiwas not distinct from null expectations in all environmental dimensions, based on univariate tests. In the PERMANOVA analyses, the pathogens departed from null expectations forB. microtiandB. burgdorferisensu lato, with smaller niches inB. microti, and larger niches inB. burgdorferisensu lato, than the vector. More generally, this study shows the value of large-scale data resources with consistent sampling methods, and known absences of key pathogens in particular samples, for answering public health questions, such as the relationship of presence and absence of pathogens in their hosts respect to environmental conditions. 

ABSTRACT Species are distributed in predictable ways in geographic spaces. The three principal factors that determine geographic distributions of species are biotic interactions (B), abiotic conditions (A), and dispersal ability or mobility (M). A species is expected to be present in areas that are accessible to it and that contain suitable sets of abiotic and biotic conditions for it to persist. A species' probability of presence can be quantified as a combination of responses toB,A, andMviaecological niche modeling (ENM; also frequently referred to as species distribution modeling or SDM). This analytical approach has been used broadly in ecology and biogeography, as well as in conservation planning and decision‐making, but commonly in the context of ‘natural’ settings. However, it is increasingly recognized that human impacts, including changes in climate, land cover, and ecosystem function, greatly influence species' geographic ranges. In this light, historical distinctions between natural and anthropogenic factors have become blurred, and a coupled human–natural landscape is recognized as the new norm. Therefore,B,A, andM(BAM) factors need to be reconsidered to understand and quantify species' distributions in a world with a pervasive signature of human impacts. Here, we present a framework, termed human‐influenced BAM (Hi‐BAM, for distributional ecology that (i) conceptualizes human impacts in the form of six drivers, and (ii) synthesizes previous studies to show how each driver modifies the natural BAM and species' distributions. Given the importance and prevalence of human impacts on species distributions globally, we also discuss implications of this framework for ENM/SDM methods, and explore strategies by which to incorporate increasing human impacts in the methodology. Human impacts are redefining biogeographic patterns; as such, future studies should incorporate signals of human impacts integrally in modeling and forecasting species' distributions. 

The states of Kansas and Oklahoma, in the central Great Plains, lie at the western periphery of the geographic distributions of several tick species. As the focus of most research on ticks and tick-borne diseases has been on Lyme disease which commonly occurs in areas to the north and east, the ticks of this region have seen little research attention. Here, we report on the phenology and activity patterns shown by tick species observed at 10 sites across the two states and explore factors associated with abundance of all and life specific individuals of the dominant species. Ticks were collected in 2020–2022 using dragging, flagging and carbon-dioxide trapping techniques, designed to detect questing ticks. The dominant species wasA.americanum(24098, 97%) followed byDermacentor variabilis(370, 2%),D.albipictus(271, 1%),Ixodes scapularis(91, <1%)and A.maculatum(38, <1%).Amblyomma americanum,A.maculatum and D.variabiliswere active in Spring and Summer, whileD.albipictus and I.scapulariswere active in Fall and Winter. Factors associated with numbers of individuals ofA.americanumincluded day of year, habitat, and latitude. Similar associations were observed when abundance was examined by life-stage. Overall, the picture is one of broadly distributed tick species that shows seasonal limitations in the timing of their questing activity. 

COVID-19 is a respiratory disease caused by a recently discovered, novel coronavirus, SARS-COV-2. The disease has led to over 81 million confirmed cases of COVID-19, with close to two million deaths. In the current social climate, the risk of COVID-19 infection is driven by individual and public perception of risk and sentiments. A number of factors influences public perception, including an individual’s belief system, prior knowledge about a disease and information about a disease. In this article, we develop a model for COVID-19 using a system of ordinary differential equations following the natural history of the infection. The model uniquely incorporates social behavioral aspects such as quarantine and quarantine violation. The model is further driven by people’s sentiments (positive and negative) which accounts for the influence of disinformation. People’s sentiments were obtained by parsing through and analyzing COVID-19 related tweets from Twitter, a social media platform across six countries. Our results show that our model incorporating public sentiments is able to capture the trend in the trajectory of the epidemic curve of the reported cases. Furthermore, our results show that positive public sentiments reduce disease burden in the community. Our results also show that quarantine violation and early discharge of the infected population amplifies the disease burden on the community. Hence, it is important to account for public sentiment and individual social behavior in epidemic models developed to study diseases like COVID-19. 

Ecological niches are increasingly appreciated as a long-term stable constraint on the geographic and temporal distributions of species, including species involved in disease transmission cycles (pathogens, vectors, hosts). Although considerable research effort has used correlative methodologies for characterizing niches, sampling effort (and the biases that this effort may or may not carry with it) considerations have generally not been incorporated explicitly into ecological niche modeling. In some cases, however, the sampling effort can be characterized explicitly, such as when hosts are tested for pathogens, as well as comparable situations such as when traps are deployed to capture particular species, etc. Here, we present simple methods for testing the hypothesis that non-randomness in occurrence or detection exists with respect to environmental dimensions (= a detectable signal of ecological niche); i.e., whether a pathogen occurs nonrandomly with respect to environment, given the occurrence and sampling of its host. We have implemented a set of R functions that presents an overall test for nonrandom occurrence with respect to a set of environmental dimensions, and, a posteriori, a set of exploratory tests that identify in which dimension(s) and in which direction or form the nonrandom occurrence is manifested. Our tools correctly detected signals of niche in most of our example cases. Although such signal may not be detectable in cases in which the niche of interest is broader than the universe sampled, such a possibility was correctly discarded in our analyses, preventing further interpretations. This kind of testing can constitute an initial step in a process that would conclude with development of a more typical ecological niche model. The particular advantage of the analyses proposed is that they consider the biases involved in sampling, testing, and reporting, in the context of nonrandom occurrence with respect to environment before proceeding to inferential and predictive steps. 

Amblyomma maculatum (Gulf Coast tick), and Dermacentor andersoni (Rocky Mountain wood tick) are two North American ticks that transmit spotted fevers associated Rickettsia . Amblyomma maculatum transmits Rickettsia parkeri and Francisella tularensis , while D. andersoni transmits R. rickettsii , Anaplasma marginale , Coltivirus (Colorado tick fever virus), and F. tularensis . Increases in temperature causes mild winters and more extreme dry periods during summers, which will affect tick populations in unknown ways. Here, we used ecological niche modeling (ENM) to assess the potential geographic distributions of these two medically important vector species in North America under current condition and then transfer those models to the future under different future climate scenarios with special interest in highlighting new potential expansion areas. Current model predictions for A. maculatum showed suitable areas across the southern and Midwest United States, and east coast, western and southern Mexico. For D. andersoni , our models showed broad suitable areas across northwestern United States. New potential for range expansions was anticipated for both tick species northward in response to climate change, extending across the Midwest and New England for A. maculatum , and still farther north into Canada for D. andersoni . 

Abstract Background The COVID-19 pandemic has caused more than 25 million cases and 800 thousand deaths worldwide to date. In early days of the pandemic, neither vaccines nor therapeutic drugs were available for this novel coronavirus. All measures to prevent the spread of COVID-19 are thus based on reducing contact between infected and susceptible individuals. Most of these measures such as quarantine and self-isolation require voluntary compliance by the population. However, humans may act in their (perceived) self-interest only. Methods We construct a mathematical model of COVID-19 transmission with quarantine and hospitalization coupled with a dynamic game model of adaptive human behavior. Susceptible and infected individuals adopt various behavioral strategies based on perceived prevalence and burden of the disease and sensitivity to isolation measures, and they evolve their strategies using a social learning algorithm (imitation dynamics). Results This results in complex interplay between the epidemiological model, which affects success of different strategies, and the game-theoretic behavioral model, which in turn affects the spread of the disease. We found that the second wave of the pandemic, which has been observed in the US, can be attributed to rational behavior of susceptible individuals, and that multiple waves of the pandemic are possible if the rate of social learning of infected individuals is sufficiently high. Conclusions To reduce the burden of the disease on the society, it is necessary to incentivize such altruistic behavior by infected individuals as voluntary self-isolation.