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Generalization of pollination systems is widely accepted by ecologists in the studies of plant–pollinator interaction networks at the community level, but the degree of generalization of pollination networks remains largely unknown at the individual pollinator level. Using potential legitimate pollinators that were constantly visiting flowers in two alpine meadow communities, we analyzed the differences in the pollination network structure between the pollinator individual level and species level. The results showed that compared to the pollinator species‐based networks, the linkage density, interaction diversity, interaction evenness, the average plant linkage level, and interaction diversity increased, but connectance, degree of nestedness, the average of pollinator linkage level, and interaction diversity decreased in the pollinator individual‐based networks, indicating that pollinator individuals had a narrower food niche than their counterpart species. Pollination networks at the pollinator individual level were more specialized at the network level (H′2) and the plant species node level (d′) than at the pollinator species‐level networks, reducing the chance of underestimating levels of specialization in pollination systems. The results emphasize that research into pollinator individual‐based pollination networks will improve our understanding of the pollination networks at the pollinator species level and the coevolution of flowering plants and pollinators., Our finding revealed that the pollinator individual‐level pollination networks were specialized at network and plant node level. The pollinator individual‐based pollination networks reduce the chance of underestimating levels of specialization and improve our understanding of coevolution of flowering plants and pollinators in pollination systems.

AimTo evaluate the utility of opportunistic data from citizen science programmes for forecasting species distributions against forecasts with a model of individual-based population dynamics.LocationSweden.MethodsWe evaluated whether alternative methods for building habitat suitability models (HSMs) based on opportunistic data from citizen science programmes produced forecasts that were consistent with forecasts from two benchmark models: (1) a HSM based on data from systematic monitoring and (2) an individual-based model for spatially explicit population dynamics based on empirical demographic and movement data. We forecasted population numbers and habitat suitability for three realistic, future forest landscapes for a forest bird, the Siberian jay (Perisoreus infaustus). We ranked simulated forest landscapes with respect to their benefits to Siberian jays for each modelling method and compared the agreement of the rankings among methods.ResultsForecasts based on our two benchmark models were consistent with each other and with expectations based on the species’ ecology. Forecasts from logistic regression models based on opportunistic data were consistent with the benchmark models if species detections were combined with high-quality inferred absences derived via retrospective interviews with experienced “super-reporters.” In contrast, forecasts with three other widely used methods were inconsistent with the benchmark models, sometimes with misleading rankings of future scenarios.Main conclusionsOur critical evaluation of alternative HSMs against a spatially explicit IBM demonstrates that information on species absences critically improves forecasts of species distributions using opportunistic data from citizen science programmes. Moreover, high-quality information on species absences can be retrospectively inferred from surveys of the consistency of reporting of individual species and the identification skills of participating reporters. We recommend that citizen science projects incorporate procedures to evaluate reporting behaviour. Inferred absences may be especially useful for improving forecasts for species and regions poorly covered by systematic monitoring schemes. published

This paper proposes a new index of multidimensional poverty, called the Global Correlation Sensitive Poverty Index (G-CSPI), which has three interesting features. First, it encompasses three dimensions: decent work, education and access to drinking water and sanitation, which largely overlap with the list of ideal dimensions obtained by expanding the Constitutional Approach, although it does not include direct health measures. Second, it uses a distribution-sensitive measure that can also be decomposed into the three poverty components: incidence, intensity and inequality. Finally, the G-CSPI is an individual-based, rather than household-based index, although restricted to individuals 15–65 years of age. It is thus able to detect intra-household differences in poverty among members within that age-range. To have a full picture of multidimensional poverty at the country level, it should then be complemented by specific poverty measures for children and the elderly. Being centered on individuals and sensitive to inequality, the G-CSPI is coherent with the overarching principle of the 2030 Agenda “leaving no one behind”. Using recent estimates of the G-CSPI for 104 countries, the empirical analysis reveals that the index is highly robust to different specifications, and that, as expected, fragile countries experience the largest levels of poverty.

“Shedder status” or “shedder type” are commonly used terms that categorise an individual based on their ability to deposit “touch” DNA via direct contact with a surface. However, it is not yet clear how best to categorise an individual into a shedder class, or how to allocate a shedder score on a sliding scale. This study considers categorisation of participants into discrete shedder categories based on DNA quantity and profile quality data, the maintenance of their shedder status over an extended period, and explores whether different methods of deposition or collection directly from hands or other body areas are interchangeable and/or more appropriate means of determining an individual’s shedder status. The shedder categorisation of participants was possible from their handprints and remained unchanged over three years. Washing hands had limited impact and shedder categorisation was not readily possible from samples collected directly from hands, other body areas or gloves after wearing gloves for a set duration. Use of consecutive deposits may assist in establishing a participant’s shedder status. As shedder categorisation may be of relevance during activity level assessments further efforts towards the ability to do so are necessary.

1. The biotic and abiotic context of individual plants within animal-pollinated plant populations can influence pollinator foraging behaviour. Pollinator movements regulate pollen flow among plant individuals, and ultimately determine individual plant reproductive success. Yet the underlying drivers of this context dependency of interactions at the population level and their functional consequences for individuals remain poorly known. 2. Here we used a well-characterised population of Halimium halimifolium (Cistaceae), a Mediterranean shrub species, in combination with exponential random graph models (ERGMs) to evaluate how the intrapopulation variation in plant attributes configures individual-based plant–pollinator networks and determines their reproductive outcomes. Specifically, we assessed (a) how the intrinsic (i.e. phenotype and phenology) and extrinsic (i.e. microsite) plant attributes influenced the emerging configuration of the bipartite plant–pollinator network and the unipartite plant–plant network derived from pollinator sharing, and (b) how these plant attributes combined with the network topological position of individual plants affect their female fitness, measured as the total seed weight per plant. 3. We found that both intrinsic and extrinsic plant attributes contributed substantially to explain the configuration of both the bipartite and the unipartite pollination network. Besides the effects of plant attributes, the functional group to which pollinator species belonged was also important to determine the variance in plant– pollinator interaction odds, while the probability of plants to share more pollinator species was additionally influenced by the spatial distance between those plants. Furthermore, our results showed that these influences of plant attributes on network structure can be translated into functional outcomes at the plant individual level, with direct consequences for intrapopulation fitness variation. 4. Synthesis. This study builds towards a better understanding of the multiple drivers underlying the context dependency of plant–pollinator interactions and how they mediate the reproductive outputs of individual plants within a population. The application of our analytical framework allows a conceptual shift from descriptive topredictive research on the evolutionary and ecological processes that give rise to complex ecological networks at the population level. Consejo Superior de Investigaciones Científicas de España. JAEINT18_EX_0080 Ministerio de Ciencia e Innovación de España. CGL2017-082847P, CGL2017-92436EXP y FPU19_02552

Individual‐based morphological brain networks built from T1‐weighted magnetic resonance imaging (MRI) reflect synchronous maturation intensities between anatomical regions at the individual level. Autism spectrum disorder (ASD) is a socio‐cognitive and neurodevelopmental disorder with high neuroanatomical heterogeneity, but the specific patterns of morphological networks in ASD remain largely unexplored at the individual level. In this study, individual‐based morphological networks were constructed by using high‐resolution structural MRI data from 40 young children with ASD (age range: 2–8 years) and 38 age‐, gender‐, and handedness‐matched typically developing children (TDC). Measurements were recorded as threefold. Results showed that compared with TDC, young children with ASD exhibited lower values of small‐worldness (i.e., σ) of individual‐level morphological brain networks, increased morphological connectivity in cortico‐striatum‐thalamic‐cortical (CSTC) circuitry, and decreased morphological connectivity in the cortico‐cortical network. In addition, morphological connectivity abnormalities can predict the severity of social communication deficits in young children with ASD, thus confirming an associational impact at the behavioral level. These findings suggest that the morphological brain network in the autistic developmental brain is inefficient in segregating and distributing information. The results also highlight the crucial role of abnormal morphological connectivity patterns in the socio‐cognitive deficits of ASD and support the possible use of the aberrant developmental patterns of morphological brain networks in revealing new clinically‐relevant biomarkers for ASD., This research shed light on the crucial role of abnormal morphological connectivity patterns in the socio‐cognitive deficits of young children with autism spectrum disorder. We found that the morphological brain network showed poor efficiency of segregated and distributed information processing, and further exhibited the increased morphological connectivity in cortico‐striatum‐thalamic‐cortical circuitry and the decreased morphological connectivity in the cortico‐cortical network, which are associated with social deficits in the autistic developmental brain.

Salmon lice Lepeophtheirus salmonis infestation of sea trout Salmo trutta results in both additional marine mortality and behavioral changes which may contribute to sea trout population decline. For effective management of activities that increase exposure to salmon lice, such as salmon aquaculture, it is necessary to have a full understanding of how salmon lice may affect sea trout populations. An individual-based model (IBTRUTTA) was therefore developed to investigate the potential effects of salmon lice infestation on sea trout population abundance and dynamics based on data from the River Halselva and Altafjord system in northern Norway. This model allowed investigation of the effect of lice-induced mortality and also the compensatory salmonid behavioral mechanisms of premature return to freshwater, either persistent for overwintering or transitory after which sea trout could go back to sea. It was found that, in the absence of compensatory mechanisms, even low rates of lice infestation could lead to marked declines in sea trout abundance. Compensatory behavioral mechanisms had the potential to reduce these de - clines, but persistent premature return resulted in reduced body mass of returning adults. The shape of the stock-recruitment relationship was also shown to strongly affect how lice-induced mortality impacted the population. Individual-based model · Sea trout · Salmon lice · Lice-induced mortality · Premature return

In the 4th industrial revolution, big data and artificial intelligence are becoming more and more important. This is because the value can be four by applying artificial intelligence techniques to data generated and accumulated in real-time. Various industries utilize them to provide a variety of services and products to customers and enhance their competitiveness. The KNN algorithm is one of such analysis methods, which predicts the class of an unlabeled instance by using the classes of nearby neighbors. It is used a lot because it is simpler and easier to understand than other methods. In this study, we proposed a GBW-KNN algorithm that finds KNN after assigning weights to each individual based on the KNN graph. In addition, a statistical test was conducted to see if there was a significant difference in the performance difference between the KNN and GBW-KNN methods. As a result of the experiment, it was confirmed that the performance of GBW-KNN was excellent overall, and the difference in performance between the two methods was significant.

Droughts driven by global change are triggering worldwide forest dieback, a phenomenon that is predicted to worsen. We combined genome-wide single nucleotide polymorphisms (SNPs) and dendrochronological approaches to assess genetically-based individual tree vulnerability to past extreme droughts that caused massive mortality of coihue (Nothofagus dombeyi (Mirb.) Blume) forests in northern Patagonia, Argentina. We collected fresh leaves and wood cores from pairs of trees, one with a healthy crown (HC) and another with a partially affected crown (PA), at four sites impacted by droughts in 1998, 2008, and 2014. We used dendrochronological techniques to estimate parameters in terms of growth trends due to drought and genomic analysis to assess the relationship of genomic variation with water stress. While 5155 neutral loci did not differentiate PA from HC trees, a set of 33 adaptive SNPs did, 8 of which were related to water stress. Association analysis between genomic variants and dendrophenotypic traits yielded 6 SNPs that were associated with a growth measure as resilience to cope with drought. Our preliminary results indicate that susceptibility to drought in N. dombeyi could be determined at the genomic level. The combination of these approaches provides a framework for the identification and analysis of candidate genes for stress response in non-model species.

The research of Dimitris Kleftogiannis was funded by the KAUST Base Research Funds to professor Panos Kalnis. We thank Nikolaos Zarokanelos for his useful comments. We express our appreciation to the two reviewers for the careful reading of the manuscript and their thoughtful comments.

An individual-based model of stochastic branching is proposed and studied, in which point particles drift in $$\bar{\mathbb {R}}_{+}:=[0,+\infty )$$ R ¯ + : = [ 0 , + ∞ ) toward the origin (edge) with unit speed, where each of them splits into two particles that instantly appear in $$\bar{\mathbb {R}}_{+}$$ R ¯ + at random positions. During their drift, the particles are subject to a random disappearance (death). The model is intended to capture the main features of the proliferation of tumor cells, in which trait $$x\in \bar{\mathbb {R}}_{+}$$ x ∈ R ¯ + of a given cell is time to its division and the death is caused by therapeutic factors. The main result of the paper is proving the existence of an honest evolution of this kind and finding a condition that involves the death rate and cell cycle distribution parameters, under which the mean size of the population remains bounded in time.

Background: Dental anthropology is an important aspect of bioarchaeology and forensic anthropology. In forensic anthropological studies, identifying an unknown individual from a mangled and decomposing body is a challenging task. Cranial remains are the only critical clues available for personal identification. Age, sex, and stature are significant parameters in establishing the identity of an unrecognized individual. Stature is the natural height of the individual in an upright position. Stature can be estimated from various parts of the body such as long bones, short bones, and skull. Few studies conducted in specific population have established a correlation for predicting stature from cephalometric and odontometric parameters. Thus, the present study was conducted to determine stature and gender based on cephalometric and odontometric parameters in the population of Tamil Nadu. Aims: The aim of this study was to investigate the possibility of predicting the stature and gender of an individual based on odontometric measurements, facial height, and bizygomatic width. Materials and methods: A total of seventy individuals in the age group of 18–24 years were selected for the study. The facial height, bizygomatic width and length ,mesiodistal and labiolingual diameter of clinical crown of maxillary central,lateral and canine teeth of both the right and left quadrants were measured. The values were analyzed statistically. Through this analysis, regression equations for estimation of stature from odontometric and facial index were derived. Results: Pearson correlation test was carried out to establish a correlation between the stature and the measured parameters. Regression analysis was carried out to establish stature from the odontometric and facial index of the individual. The regression equations were used to get estimated stature in the same sample of volunteers from which regression equations were obtained. Tabulated results showed a probable correlation between various parameters. Conclusion: In our study, a positive correlation between cephalometric measurements, gender, crown length of permanent left central incisor, and combined mesiodistal diameter of permanent maxillary anteriors with stature was found. This can be used as a supplementary method for estimating the stature of unknown human bodies.

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused global transmission, and been spread all over the world. For those regions that are currently free of infected cases, it is an urgent issue to prevent and control the local outbreak of COVID-19 when there are sporadic cases. To evaluate the effects of non-pharmaceutical interventions against local transmission of COVID-19, and to forecast the epidemic dynamics after local outbreak of diseases under different control measures, we developed an individual-based model (IBM) to simulate the transmission dynamics of COVID-19 from a microscopic perspective of individual-to-individual contacts to heterogenous among individuals. Based on the model, we simulated the effects of different levels of non-pharmaceutical interventions in controlling disease transmission after the appearance of sporadic cases. Simulations shown that isolation of infected cases and quarantine of close contacts alone would not eliminate the local transmission of COVID-19, and there is a risk of a second wave epidemics. Quarantine the second-layer close contacts can obviously reduce the size of outbreak. Moreover, to effectively eliminate the daily new infections in a short time, it is necessary to reduce the individual-to-individual contacts. IBM provides a numerical representation for the local transmission of infectious diseases, and extends the compartmental models to include individual heterogeneity and the close contacts network. Our study suggests that combinations of self-isolation, quarantine of close contacts, and social distancing would be necessary to block the local transmission of COVID-19.

El presente artículo busca profundizar la reflexión entre la intervención social y las carácterísticas de las masculinidades no hegemónicas que resurgen en el contexto del neoliberalismo postdictadura. Este vínculo se evidencia como una necesidad urgente para acercar la intervención social a las subjetividades abyectas que se desarrollan en contextos de profundas desigualdades sociales, característica que se evidencia en américa latina y especialmente en Chile, con el surgimiento de dictaduras cívico-militares en la segunda mitad del siglo XX y la transformación de la sociedad a través de la implantación del modelo neoliberal como proyecto homogeneizador que instala al individuo por sobre la comunidad, atomizando al sujeto de intervención. Las masculinidades no hegemónicas en este contexto, se pueden entender como resultado de la relación entre sociedad e individuo marginal y/o marginado en función de su orientación sexual, requiriendo su caracterización que privilegie miradas desde la complejidad

Even after decades of research, the migration of songbirds still holds numerous secrets. Distinct stopover and routing behavior of diurnally and nocturnally migrating songbirds has been stated in the 1960s, but empirical confirmation is yet lacking widely. We studied the behavior of individual diurnally migrating dunnocks and nocturnally migrating blackcaps by means of large‐scale automated radio‐telemetry. Birds were radio‐tagged during their stopover at the German North Sea coast. Our data indicate longer initial stopover duration in the diurnally migrating dunnocks, opposing the hypothesis of nocturnal migrants needing more time to recover due to their longer migratory flights. Nonetheless, dunnocks stopped over more often along their tracks as when compared to the nocturnally migrating blackcaps. Behavior en route did not differ as clearly between species challenging the general view of contrasting routings of diurnal and nocturnal migrants with regard to landscape and open water. Our results imply additional factors of relevance other than differences in species or daily migration timing per se. We discuss and highlight the need of detailed and individual based data to better understand stopover and routing behavior of songbirds in the environmental context., We use a large‐scale radio‐telemetry approach to directly compare the behaviour of dunnocks and blackcaps, a daytime and a nighttime migrant, on migration at the German North Sea coast. The diurnally travelling dunnocks stayed longer at stopover sites, while en route behaviour did not differ as clearly between species, challenging the general view of contrasting routings of diurnal and nocturnal migrants with regard to landscape and open water. Our results imply that detailed and individual‐based data are important to understand migratory strategies and properly judge risks and challenges individual species face on migration.

Introduction: Estimation of stature is of paramount importance in verifying the identity of an individual from the bodily remains, often after any kind of mass disaster, explosion or intentional cuts and dismantling we encounter very few bodily parts. The objective of the present study was to estimate stature from the measurement of finger length based on the principle of correlation. Methods: The present study consisted of 300 individuals (150 male and 150 female) of age group 18 to 26 years during the period of August 2017 to August 2018. Stature and finger length of individuals were measured by using stadiometer and digital caliper respectively. Data were analyzed by using statistical software SPSS-22. Results: A positive and highly significant (p

We introduce an individual-based model with dynamical equations for susceptible-infected-susceptible (SIS) epidemics on clustered networks. Linking the mean-field and quenched mean-field models, a general method for deriving a cluster approximation for three-node loops in complex networks is proposed. The underlying epidemic threshold condition is derived by using the quasi-static approximation. Our method thus extends the pair quenched mean-field (pQMF) approach for SIS disease spreading in unclustered networks to the scenario of epidemic outbreaks in clustered systems with abundant transitive relationships.We found that clustering can significantly alter the epidemic threshold, depending nontrivially on topological details of the underlying population structure. The validity of our method is verified through the existence of bounded solutions to the clustered pQMF model equations, and is further attested via stochastic simulations on homogeneous small-world artificial networks and growing scale-free synthetic networks with tunable clustering, as well as on real-world complex networked systems. Our method has vital implications for the future policy development and implementation of intervention measures in highly clustered networks, especially in the early stages of an epidemic in which clustering can decisively alter the growth of a contagious outbreak.

This conceptual analysis examines the implementation of a state-funded college and career readiness pilot program at a small, rural all-Black school in Illinois. Drawing from ethnographic data that dates from 2010, I offer a retrospective analysis on individual based intervention programming for college and career readiness. Concerned with how young Black high school students are constructed as problems in regards to increasing readiness, I trace policy failure through the theoretical lenses of deficit ideology (Valencia in The evolution of deficit thinking: educational thought and practice, Routledge, New York, 1997) and dysconscious racism (King in J Negro Educ 60(2):133–146, 1991). This analysis illuminates how racism ultimately undermines a-contextual efforts to increase readiness for college and career.

To compare the spread patterns between two types of plant viruses, non-persistent virus (NPV) and persistent virus (PV), we developed a spatially-explicit individual-based model. Our probability-based model is driven by the actions of insect vectors that are affected by interactions with host plants and plant viruses, considering both biological and behavioral components of their relationship. As a model system, we used potato virus y and potato leafroll virus, respectively for NPV and PV, potato for host plant, and Myzus persicae for the insect vector; empirical results from previous studies were acquired and adjusted to be used as our parameter values. Our simulation results showed that initial infection of PV in the field resulted in over 1.3 times greater number of insect vectors while causing approximately 7 times greater number of virus-infected plants compared to NPV by the end of simulation. Furthermore, spatial analysis showed that PV-infected plants showed greater aggregation in the field, forming larger patches compared to NPV-infected plants. Our results demonstrated the importance of host plant and insect vector manipulation by plant viruses as well as biological properties such as infectious period in the insect on the difference in overall spread pattern.

We are living in a cyber-physical-social environment with a variety of lifestyles and values. Living support has become important in such a diverse society. Owing to the ability to collect a large amount of personal data or life logs in the cyber-physical-social environment, it is now possible for us to provide living support based on personal data analysis. Moreover, analyzing such data can facilitate a deep understanding of an individual. In this study, we focus on the provision of cyber-enabled well-being oriented daily living support for an individual based on personal data analysis. Three categories of personal data are identified from an individual's daily life data. In this paper, we discuss the basic concept, model, and framework for well-being oriented personal data analysis in order to offer suggestions and advices to improve the living quality of an individual. Finally, we report a feasibility study with an application scenario by using personal and environmental data.

We investigate competition between separate periodical cicada populations each possessing different life‐cycle lengths. We build an individual‐based model to simulate the cicada life cycle and allow random migrations to occur between patches inhabited by the different populations. We show that if hybridization between different cycle lengths produces offspring that have an intermediate life‐cycle length, then predation acts disproportionately to select against the hybrid offspring. This happens because they emerge in low densities without the safety‐in‐numbers provided by either parent population. Thus, prime‐numbered life cycles that can better avoid hybridization are favored. However, we find that this advantage of prime‐numbered cycles occurs only if there is some mechanism that can occasionally synchronize emergence between local populations in sufficiently many patches., We investigate competition between separate periodical cicada populations each possessing different life‐cycle lengths. We show that prime‐numbered life cycles are selected for when hybridization produces offspring with an intermediate life‐cycle length compared to their parents.

Individual-based relative deprivation (IRD) refers to anger and resentment associated with upward interpersonal comparison. Four studies investigated whether and when IRD can be a result of receiving help. In all the studies, we found an interaction between type of help (i.e., autonomy-oriented vs. dependency-oriented help) and status discrepancy such that participants experienced a higher level of IRD after receiving dependency-oriented help from a higher status helper than in other conditions. This interaction emerged uniquely on IRD, but not on general affect, self-esteem, or evaluations of helper. Moreover, Study 3 indicated that the perception of a diminished likelihood of changing mediated the impact of dependency-oriented, high-status help on IRD. Study 4 showed that IRD stemming from such help was positively associated with prioritizing self-interest over others'. Implications for research on help and IRD are discussed.

This work proposes a version of the Individual-Based Model (IBM) that converges, on average, to the result of the SIR (Susceptible-Infected-Recovered) model, and studies the effect of this IBM in two types of networks: random and scale-free. A numerical computational case study is considered, using large scale networks implemented by an efficient framework. Statistical tests are performed to show the similarities and differences between the network models and the deterministic model taken as a baseline. Simulation results verify that different network topologies alter the behavior of the epidemic propagation in the following aspects: temporal evolution, basal reproducibility and the number of infected in the final.

Anticipatory effects mediated by epigenetic changes occur when parents modify the phenotype of their offspring by making epigenetic changes in their gametes guided by information from an environmental cue. To investigate when do anticipatory effects mediated by epigenetic changes evolve in a fluctuating environment, I use an individual based simulation model with explicit genetic architecture. The model allows for the population to respond to environmental changes by evolving within generation plasticity, bet-hedging, or track the environment with genetic adaptation, in addition to the evolution of anticipatory effects. The results show that anticipatory effects evolve when the environmental cue provides reliable information about the environment and the environmental fluctuations span from a few to tens of generations, provided that fitness costs of anticipatory effects are rather low. Moreover, evolution of anticipatory effects is quite robust to different genetic architectures when reliability of the environmental cue is high. Anticipatory effects always give smaller fitness benefits than within generation plasticity, suggesting a possible reason for generally small observed anticipatory effects in empirical studies. The model suggests that empirical studies could focus on taxa that live in environments that have high environmental autocorrelations or environmental cues that reliably predict the environment to test model predictions.

We implemented multilocus selection in a spatially-explicit, individual-based framework that enables multivariate environmental gradients to drive selection in many loci as a new module for the landscape genetics programs, CDPOP and CDMetaPOP. Our module simulates multilocus selection using a linear additive model, providing a flexible platform to evaluate a wide range of genotype-environment associations. Importantly, the module allows simulation of selection in any number of loci under the influence of any number of environmental variables. We validated the module with individual-based selection simulations under Wright-Fisher assumptions. We then evaluated results for simulations under a simple landscape selection model. Next, we simulated individual-based multilocus selection across a complex selection landscape with three loci linked to three different environmental variables. Finally, we demonstrated how the program can be used to simulate multilocus selection under varying selection strengths across different levels of gene flow in a landscape genetics framework. This new module provides a valuable addition to the study of landscape genetics, allowing for explicit evaluation of the contributions and interactions between gene flow and selection-driven processes across complex, multivariate environmental and landscape conditions.

Children have a high burden of influenza and play a central role in spreading influenza. Routinely vaccinating children against influenza may, thus, not only reduce their disease burden, but also that of the general population, including the elderly who frequently suffer severe complications. Using the published individual-based tool 4Flu, we simulated how pediatric vaccination would change infection incidence in Germany. Transmission of four influenza strains was simulated in 100,000 individuals with German demography and contact structure. After initialization with the recorded trivalent influenza vaccination coverage for 20 years (1997–2016), all vaccinations were switched to quadrivalent influenza vaccine (QIV). Scenarios where vaccination coverage of children (0.5-17-year-old) was increased from the current value (4.3%) to a maximum of 10-60% were compared to baseline with unchanged coverage, averaging results of 1,000 pairs of simulations over a 20-year evaluation period (2017–2036). Pediatric vaccination coverage of 10-60% annually prevented 218–1,732 (6.3–50.5%) infections in children, 204–1,961 (2.9–28.2%) in young adults and 95–868 (3.1–28.9%) in the elderly in a population of 100,000 inhabitants; overall, 34.1% of infections in the total population (3.7 million infections per year in Germany) can be prevented if 60% of all children are vaccinated annually. 4.4–4.6 vaccinations were needed to prevent one infection among children; 1.7–1.8 were needed to prevent one in the population. Enhanced pediatric vaccination prevents many infections in children and even more in young adults and the elderly.

International communication and global cooperation have greatly accelerated the worldwide spread of dengue fever, increasing the impact of imported cases on dengue outbreaks in non-naturall...

Funding Information: The research conducted by D.M.H. and N.H. in this study was funded by the Fonds Wetenschappelijk Onder-zoek—Vlaanderen (Research Foundation—Flanders; FWO, http://www.fwo.be/en/) (Grant Agreements G0E8416N and G0B2317N). The research done by J.D.S. and A.M.V. in this study has been supported in part by Grants G.0692.14 and G0B2317N, funded by the FWO, Belgium. P.J.K.L. was supported By a PhD Grant of the FWO (1S31916N), and is currently funded via a post-doctoral fellowship (1242021N) of the FWO. WD was supported by a postdoctoral fellowship from FWO (12L5816N). Research done by V.M. in this study has been completed as part of the ELTE Thematic Excellence Programme 2020 supported by the National Research, Development and Innovation Office (TKP2020-IKA-05). The authors gratefully acknowledge support from the FWO Scientific Research Community on Network Statistics for Sexually Transmitted Diseases Epidemiology. The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation—Flanders (FWO) and the Flemish Government—department EWI. Publisher Copyright: © 2021, The Author(s). Model comparisons have been widely used to guide intervention strategies to control infectious diseases. Agreement between different models is crucial for providing robust evidence for policy-makers because differences in model properties can influence their predictions. In this study, we compared models implemented by two individual-based model simulators for HIV epidemiology in a heterosexual population with Herpes simplex virus type-2 (HSV-2). For each model simulator, we constructed four models, starting from a simplified basic model and stepwise including more model complexity. For the resulting eight models, the predictions of the impact of behavioural interventions on the HIV epidemic in Yaoundé-Cameroon were compared. The results show that differences in model assumptions and model complexity can influence the size of the predicted impact of the intervention, as well as the predicted qualitative behaviour of the HIV epidemic after the intervention. These differences in predictions of an intervention were also observed for two models that agreed in their predictions of the HIV epidemic in the absence of that intervention. Without additional data, it is impossible to determine which of these two models is the most reliable. These findings highlight the importance of making more data available for the calibration and validation of epidemiological models. publishersversion published

The biodiversity of tundra areas in northern high latitudes is threatened by invasion of forests under global warming. However, poorly understood nonlinear responses of the treeline ecotone mean the timing and extent of tundra losses are unclear but policymakers need such information to optimize conservation efforts. Our individual based model LAVESI, developed for the Siberian tundra-taiga ecotone, can help improve our understanding. Consequently, we simulated treeline migration trajectories until the end of the millennium, causing a loss of tundra area when advancing north. Our simulations reveal that the treeline follows climate warming with a severe, century-long time lag, which is overcompensated by infilling of stands in the long run even when temperatures cool again. Our simulations reveal that only under ambitious mitigation strategies (RCP 2.6) will ~30% of original tundra areas remain in the north but separated into two disjunct refugia.

As climate change threatens species' persistence, predicting the potential for species to adapt to rapidly changing environments is imperative for the development of effective conservation strategies. Eco-evolutionary individual-based models (IBMs) can be useful tools for achieving this objective. We performed a literature review to identify studies that apply these tools in marine systems. Our survey suggested that this is an emerging area of research fuelled in part by developments in modelling frameworks that allow simulation of increasingly complex ecological, genetic and demographic processes. The studies we identified illustrate the promise of this approach and advance our understanding of the capacity for adaptation to outpace climate change. These studies also identify limitations of current models and opportunities for further development. We discuss three main topics that emerged across studies: (i) effects of genetic architecture and non-genetic responses on adaptive potential; (ii) capacity for gene flow to facilitate rapid adaptation; and (iii) impacts of multiple stressors on persistence. Finally, we demonstrate the approach using simple simulations and provide a framework for users to explore eco-evolutionary IBMs as tools for understanding adaptation in changing seas.

Beginning in early 2020, coronavirus disease (COVID-19) has rapidly spread all over the world. As of now there have been over 102.52 million confirmed cases along with 2.21 million deaths worldwide. Our objective is to create an algorithm that will predict the severity of a COVID-19 case for an individual based on demographic data such as race, age, gender, and location. Using international, national and local datasets, we collected the demographic data and organized them into their respective categories, namely age, race, gender, and location of origin. We then inputted this data into an algorithm that works around the principle of probability. Our algorithm uses such trends to develop a risk assessment and create a model. While compiling that data we noted common trends within the three demographics. Specifically, around the age thirty, cases were higher compared to other age ranges. The data collected and trends noted can be used to prioritize and prepare for patients that may be in critical danger, providing a chance for hospitals and vaccine distribution centers to preemptively address higher risk cases early.

Some ecological studies have shown that areas with higher use of cannabis may have lower opioid use and fewer opioid-related problems. Newer studies are questioning this finding. Few individually based studies have been performed. Using data from the Norwegian Prescription Database, this study investigated the individual level effect of prescribed cannabis extract (Sativex®) in prescription opioid users on their opioid use in the following year. Looking at all those filling a prescription for Sativex®, opioid use was only marginally lowered in the follow-up period. Some Sativex® users, however, filled more prescriptions for Sativex® and were able to reduce their opioid use substantially. Further studies are needed to elucidate more details on these patients, so as to know who can benefit from such cannabis-based extracts in reducing their opioid use.

Mathematical models are powerful tools in HIV epidemiology, producing quantitative projections of key indicators such as HIV incidence and prevalence. In order to improve the accuracy of predictions, such models need to incorporate a number of behavioural and biological heterogeneities, especially those related to the sexual network within which HIV transmission occurs. An individual-based model, which explicitly models sexual partnerships, is thus often the most natural type of model to choose. In this paper we present PopART-IBM, a computationally efficient individual-based model capable of simulating 50 years of an HIV epidemic in a large, high-prevalence community in under a minute. We show how the model calibrates within a Bayesian inference framework to detailed age- and sex-stratified data from multiple sources on HIV prevalence, awareness of HIV status, ART status, and viral suppression for an HPTN 071 (PopART) study community in Zambia, and present future projections of HIV prevalence and incidence for this community in the absence of trial intervention., Author summary In this paper we present PopART-IBM, an individual-based model used to simulate HIV transmission in communities in high prevalence settings. We show that PopART-IBM can simulate transmission over a span of decades in a large community in less than a minute. This computational efficiency allows us to calibrate the model within an inference framework, and we show an illustrative example of calibration using an adaptive population Monte Carlo Approximate Bayesian Computation algorithm for a community in Zambia that was part of the HPTN-071 (PopART) trial. We compare the detailed model output to real-world data collected during the trial from this community. Finally, we project how the HIV epidemic would have changed over time in this community if no intervention from the trial had occurred.

Contemporary evolution that occurs across ecologically relevant time scales, such as a few generations or decades, can not only change phenotypes but also feed back to demographic parameters and the dynamics of populations. This chapter presents a method to make phenotypic traits evolve in mechanistic individual-based simulations. The method is broadly applicable, as demonstrated through its applications to boreal forest adaptation to global warming, eco-evolutionary dynamics driven by fishing-induced selection in Atlantic cod, and the evolution of age at maturity in Atlantic salmon. The main message of this chapter is that there may be little reason to exclude phenotypic evolution in analyses of population dynamics, as these can be modified by evolutionary changes in life histories. Future challenges will be to integrate rapidly accumulating genomic knowledge and an ecosystem perspective to improve population projections and to better understand the drivers of population dynamics.

BackgroundMalawi is rapidly closing the gap in achieving the UNAIDS 95-95-95 targets, with 90% of people living with HIV in Malawi aware of their status. As we approach epidemic control, interventions to improve coverage will become more costly. There is therefore an urgent need to identify innovative and low-cost strategies to maintain and increase testing coverage without diverting resources from other HIV services.MethodsA data-driven individual-based model was parameterized with data from a community-representative survey (sociodemographic, health service utilization, HIV testing history) of men and youth in Malawi (data collected 08/2019). 79 different strategies for the implementation of HIV self-testing (HIVST) and provider-initiated-testing-and-counselling at the outpatient department (OPD) were evaluated. Outcomes included percent of men/youth tested for HIV in a 12-month period, cost-effectiveness, and human resource requirements. Testing yield was assumed to be constant across the scenarios.FindingsFacility-based HIVST offered year-round resulted in the greatest increase in proportion of men and youth tested in the OPD (from 45% to 72%-83%), was considered cost-saving for HIVST kit priced at $1.00, and generally reduced required personnel as compared to the status quo. At higher HIVST kit prices, and more relaxed eligibility criteria, all scenarios that considered year-round HIVST in the OPD remained on the cost-effectiveness frontier.InterpretationFacility-based HIVST is a cost-effective strategy to increase the proportion of men/youth tested for HIV and decreases the human resource requirements for HIV testing in the OPD-providing additional health care worker time for other priority health care activities.FundingFCDO; USAID

We introduce J une , an open-source framework for the detailed simulation of epidemics on the basis of social interactions in a virtual population constructed from geographically granular census data, reflecting age, sex, ethnicity and socio-economic indicators. Interactions between individuals are modelled in groups of various sizes and properties, such as households, schools and workplaces, and other social activities using social mixing matrices. J une provides a suite of flexible parametrizations that describe infectious diseases, how they are transmitted and affect contaminated individuals. In this paper, we apply J une to the specific case of modelling the spread of COVID-19 in England. We discuss the quality of initial model outputs which reproduce reported hospital admission and mortality statistics at national and regional levels as well as by age strata.

Engineering culture perpetuates norms that are unwelcoming to minoritized identities, particularlywomen and racialized folks. A theory useful for understanding this is “hidden curriculum” whichdescribes the assumptions and beliefs that are unintentionally and implicitly taught in engineeringeducation. This paper outlines an initial conceptual model for using IBM (individual-based modeling) to better understand the hidden curriculum of engineering. We provide an overview of the driving question behind the model design, the agents and their attributes, the rules andprocesses which change these attributes, and the scale of the model. This overview of the model building process provides insight into the model design for simulating and better understanding the perpetuation of the hidden curriculum within engineering education.

Every individual is unique in their own way and they learn and understand things at their own pace and in their own style more efficiently. Currently learning systems have a major shortcoming which is that they do not consider an individual learner’s style and goals. Companies spent thousands of dollars on training programs without desired results. And thus there is a dire need of a good personalized learning system. Existing solutions are a tad bit inefficient as ignore a lot of parameters since they do not take advantage of the entire design. Thus to overcome the above issues we propose a learner-centric e-learning system using adaptive learning in this paper. This system is designed as such that it will be most suitable to the learner and flexible according to their style as they learn and grow. The important part of the design is the neural network model created for every individual based on their interaction and performance and accordingly recommends most suitable learning content. Also the system adjusts to user’s pace and uses gamification in activities to improve experience. The aim of this paper is the introduction of a learning system which increases user engagement and maximizes their learning abilities.