Individual-based Simulation Model Research Articles

The impact of community-wide, mass drug administration on aggregation of soil-transmitted helminth infection in human host populations

BackgroundSoil-transmitted helminths (STH) are intestinal parasites estimated to infect over 1.5 billion people. Current treatment programmes are aimed at morbidity control through school-based deworming programmes (targeting school-aged children, SAC) and treating women of reproductive age (WRA), as these two groups are believed to record the highest morbidity. More recently, however, the potential for interrupting transmission by treating entire communities has been receiving greater emphasis and the feasibility of such programmes are now under investigation in randomised clinical trials through the Bill & Melinda Gates Foundation funded DeWorm3 studies. Helminth parasites are known to be highly aggregated within human populations, with a small minority of individuals harbouring most worms. Empirical evidence from the TUMIKIA project in Kenya suggests that aggregation may increase significantly after anthelminthic treatment.MethodsA stochastic, age-structured, individual-based simulation model of parasite transmission is employed to better understand the factors that might induce this pattern. A simple probabilistic model based on compounded negative binomial distributions caused by age-dependencies in both treatment coverage and exposure to infection is also employed to further this understanding.ResultsBoth approaches confirm helminth aggregation is likely to increase post-mass drug administration as measured by a decrease in the value of the negative binomial aggregation parameter, k. Simple analytical models of distribution compounding describe the observed patterns well.ConclusionsThe helminth aggregation that was observed in the field was replicated with our stochastic individual-based model. Further work is required to generalise the probabilistic model to take account of the respective sensitivities of different diagnostics on the presence or absence of infection.

Vector bionomics and vectorial capacity as emergent properties of mosquito behaviors and ecology.

Mosquitoes are important vectors for pathogens that infect humans and other vertebrate animals. Some aspects of adult mosquito behavior and mosquito ecology play an important role in determining the capacity of vector populations to transmit pathogens. Here, we re-examine factors affecting the transmission of pathogens by mosquitoes using a new approach. Unlike most previous models, this framework considers the behavioral states and state transitions of adult mosquitoes through a sequence of activity bouts. We developed a new framework for individual-based simulation models called MBITES (Mosquito Bout-based and Individual-based Transmission Ecology Simulator). In MBITES, it is possible to build models that simulate the behavior and ecology of adult mosquitoes in exquisite detail on complex resource landscapes generated by spatial point processes. We also developed an ordinary differential equation model which is the Kolmogorov forward equations for models developed in MBITES under a specific set of simplifying assumptions. While mosquito infection and pathogen development are one possible part of a mosquito's state, that is not our main focus. Using extensive simulation using some models developed in MBITES, we show that vectorial capacity can be understood as an emergent property of simple behavioral algorithms interacting with complex resource landscapes, and that relative density or sparsity of resources and the need to search can have profound consequences for mosquito populations' capacity to transmit pathogens.

Sperm storage reduces the strength of the mate-finding Allee effect.

Mate searching is a key component of sexual reproduction that can have important implications for population viability, especially for the mate‐finding Allee effect. Interannual sperm storage by females may be an adaptation that potentially attenuates mate limitation, but the demographic consequences of this functional trait have not been studied. Our goal is to assess the effect of female sperm storage durability on the strength of the mate‐finding Allee effect and the viability of populations subject to low population density and habitat alteration. We used an individual‐based simulation model that incorporates realistic representations of the demographic and spatial processes of our model species, the spur‐thighed tortoise (Testudo graeca). This allowed for a detailed assessment of reproductive rates, population growth rates, and extinction probabilities. We also studied the relationship between the number of reproductive males and the reproductive rates for scenarios combining different levels of sperm storage durability, initial population density, and landscape alteration. Our results showed that simulated populations parameterized with the field‐observed demographic rates collapsed for short sperm storage durability, but were viable for a durability of one year or longer. In contrast, the simulated populations with a low initial density were only viable in human‐altered landscapes for sperm storage durability of 4 years. We find that sperm storage is an effective mechanism that can reduce the strength of the mate‐finding Allee effect and contribute to the persistence of low‐density populations. Our study highlights the key role of sperm storage in the dynamics of species with limited movement ability to facilitate reproduction in patchy landscapes or during population expansion. This study represents the first quantification of the effect of sperm storage durability on population dynamics in different landscapes and population scenarios.

The potential impact of the Affordable Care Act and Medicaid expansion on reducing colorectal cancer screening disparities in African American males.

Few investigations have explored the potential impact of the Affordable Care Act on health disparity outcomes in states that chose to forgo Medicaid expansion. Filling this evidence gap is pressing as Congress grapples with controversial healthcare legislation that could phase out Medicaid expansion. Colorectal cancer (CRC) is a commonly diagnosed, preventable cancer in the US that disproportionately burdens African American men and has substantial potential to be impacted by improved healthcare insurance coverage. Our objective was to estimate the impact of the Affordable Care Act (increasing insurance through health exchanges alone or with Medicaid expansion) on colorectal cancer outcomes and economic costs among African American and White males in North Carolina (NC), a state that did not expand Medicaid. We used an individual-based simulation model to estimate the impact of ACA (increasing insurance through health exchanges alone or with Medicaid expansion) on three CRC outcomes (screening, stage-specific incidence, and deaths) and economic costs among African American and White males in NC who were age-eligible for screening (between ages 50 and 75) during the study period, years of 2013–2023. Health exchanges and Medicaid expansion improved simulated CRC outcomes overall, though the impact was more substantial among AAs. Relative to health exchanges alone, Medicaid expansion would prevent between 7.1 to 25.5 CRC cases and 4.1 to 16.4 per 100,000 CRC cases among AA and White males, respectively. Our findings suggest policies that expanding affordable, quality healthcare coverage could have a demonstrable, cost-saving impact while reducing cancer disparities.

Agent-Based Simulation Research on Urban Land Development of San Diego

This paper introduces an individual-based simulation model to study the land development process in San Diego by using multi-agent simulation (MAS) software, where the urban expansion activities are realized through the construction behaviors of three major types of developer agents (residential/industrial/commercial). Except compiling codes for the interaction activities between different kinds of agents and their surrounding environment, this model also includes the analysis function of urban renewal into the simulation process. And in order to find a suitable land policy for urban growth management in San Diego, this paper conducts a prediction research about future land development in three different types of administration scenarios, and evaluates their simulation layouts from two separate perspectives: visual observation and geometrical calculation. After that, following the suggestions of comprehensive plan in 2050 and smart growth principles, this paper proposes a series of measures for the improvement of current land management, and verifies their effectiveness through the comparison research between optimized scenario and other three administration policies mentioned above. Also, for the purpose of increasing simulation layout’s consistency with the real world, several possible ways are put forward to optimize the modeling procedures at the end of this paper.

Investigating the drivers of the spatio-temporal patterns of genetic differences between Plasmodium falciparum malaria infections in Kilifi County, Kenya

Knowledge of how malaria infections spread locally is important both for the design of targeted interventions aiming to interrupt malaria transmission and the design of trials to assess the interventions. A previous analysis of 1602 genotyped Plasmodium falciparum parasites in Kilifi, Kenya collected over 12 years found an interaction between time and geographic distance: the mean number of single nucleotide polymorphism (SNP) differences was lower for pairs of infections which were both a shorter time interval and shorter geographic distance apart. We determine whether the empiric pattern could be reproduced by a simple model, and what mean geographic distances between parent and offspring infections and hypotheses about genotype-specific immunity or a limit on the number of infections would be consistent with the data. We developed an individual-based stochastic simulation model of households, people and infections. We parameterized the model for the total number of infections, and population and household density observed in Kilifi. The acquisition of new infections, mutation, recombination, geographic location and clearance were included. We fit the model to the observed numbers of SNP differences between pairs of parasite genotypes. The patterns observed in the empiric data could be reproduced. Although we cannot rule out genotype-specific immunity or a limit on the number of infections per individual, they are not necessary to account for the observed patterns. The mean geographic distance between parent and offspring malaria infections for the base model was 0.4 km (95% CI 0.24, 1.20), for a distribution with 58% of distances shorter than the mean. Very short mean distances did not fit well, but mixtures of distributions were also consistent with the data. For a pathogen which undergoes meiosis in a setting with moderate transmission and a low coverage of infections, analytic methods are limited but an individual-based model can be used with genotyping data to estimate parameter values and investigate hypotheses about underlying processes.

Mass campaigns combining antimalarial drugs and anti-infective vaccines as seasonal interventions for malaria control, elimination and prevention of resurgence: a modelling study

BackgroundThe only licensed malaria vaccine, RTS,S/AS01, has been developed for morbidity-control in young children. The potential impact on transmission of deploying such anti-infective vaccines to wider age ranges, possibly with co-administration of antimalarial treatment, is unknown. Combinations of existing malaria interventions is becoming increasingly important as evidence mounts that progress on reducing malaria incidence is stalling and threatened by resistance.MethodsMalaria transmission and intervention dynamics were simulated using OpenMalaria, an individual-based simulation model of malaria transmission, by considering a seasonal transmission setting and by varying epidemiological and setting parameters such as transmission intensity, case management, intervention types and intervention coverages. Chemopreventive drugs and anti-infective vaccine efficacy profiles were based on previous studies in which model parameters were fitted to clinical trial data. These intervention properties were used to evaluate the potential of seasonal mass applications of preventative anti-infective malaria vaccines, alone or in combination with chemoprevention, to reduce malaria transmission, prevent resurgence, and/or reach transmission interruption.ResultsDeploying a vaccine to all ages on its own is a less effective intervention strategy compared to chemoprevention alone. However, vaccines combined with drugs are likely to achieve dramatic prevalence reductions and in few settings, transmission interruption. The combined mass intervention will result in lower prevalence following the intervention compared to chemoprevention alone and will increase chances of interruption of transmission resulting from a synergistic effect between both interventions. The combination of vaccine and drug increases the time before transmission resurges after mass interventions cease compared to mass treatment alone. Deploying vaccines and drugs together requires fewer rounds of mass intervention and fewer years of intervention to achieve the same public health impact as chemoprevention alone.ConclusionsThrough simulations we identified a previously unidentified value of deploying vaccines with drugs, namely the greatest benefit will be in preventing and delaying transmission resurgence for longer periods than with other human targeted interventions. This is suggesting a potential role for deploying vaccines alongside drugs in transmission foci as part of surveillance-response strategies.

Cost-Effectiveness Analysis for Influenza Vaccination Coverage and Timing in Tropical and Subtropical Climate Settings: A Modeling Study

BackgroundThe lack of seasonality in influenza epidemics in the tropics makes the application of well-established temperate zone national vaccination plans challenging. ObjectivesWe developed an individual-based simulation model to study optimal vaccination scheduling and assess cost-effectiveness of these vaccination schedules in scenarios of no influenza seasonality and the seasonality regimes of Singapore, Taipei, and Tokyo. MethodsThe simulation models heterogeneities in human contact networks, levels of protective antibodies following infection, the effectiveness of the influenza vaccine, and seasonality. Using a no intervention baseline, we consider 3 alternative vaccination strategies: (1) annual vaccination for a percentage of the elderly, (2) biannual vaccination for a percentage of the elderly, and (3) annual vaccination for all elderly and a fraction of the remaining population. We considered 5 vaccination uptake rates for each strategy and modeled the estimated costs, quality-adjusted life years, and incremental cost-effectiveness ratios (ICERs), indicating the cost-effectiveness of each scenario. ResultsIn Singapore, annual vaccination for a proportion of elderly is largely cost-effective. However, with fixed uptake rates, partial biannual vaccination for the elderly yields a higher ICER than partial annual vaccination for the elderly, resulting in a cost-ineffective ICER. The most optimal strategy is the total vaccination of all the elderly and a proportion of individuals from other age groups, which results in a cost-saving ICER. This finding is consistent across different seasonality regimes. ConclusionsTropical countries like Singapore can have comparably cost-effective vaccination strategies as found in countries with winter epidemics. The vaccination of all the elderly and a proportion of other age groups is the most cost-effective strategy, supporting the need for an extensive national influenza vaccination program.

Scaling up the effects of inbreeding depression from individuals to metapopulations.

Inbreeding is common in nature, and many laboratory studies have documented that inbreeding depression can reduce the fitness of individuals. Demonstrating the consequences of inbreeding depression on the growth and persistence of populations is more challenging because populations are often regulated by density- or frequency-dependent selection and influenced by demographic and environmental stochasticity. A few empirical studies have shown that inbreeding depression can increase extinction risk of local populations. The importance of inbreeding depression at the metapopulation level has been conjectured based on population-level studies but has not been evaluated. We quantified the impact of inbreeding depression affecting the fitness of individuals on metapopulation persistence in heterogeneous habitat networks of different sizes and habitat configuration in a context of natural butterfly metapopulations. We developed a spatial individual-based simulation model of metapopulations with explicit genetics. We used Approximate Bayesian Computation to fit the model to extensive demographic, genetic and life-history data available for the well-studied Glanville fritillary butterfly (Melitaea cinxia) metapopulations in the Åland islands in SW Finland. We compared 18 semi-independent habitat networks differing in size and fragmentation. The results show that inbreeding is more frequent in small habitat networks, and consequently, inbreeding depression elevates extinction risks in small metapopulations. Metapopulation persistence and neutral genetic diversity maintained in the metapopulations increase with the total habitat amount in and mean patch size of habitat networks. Dispersal and mating behaviour interact with landscape structure to determine how likely it is to encounter kin while looking for mates. Inbreeding depression can decrease the viability of small metapopulations even when they are strongly influenced by stochastic extinction-colonization dynamics and density-dependent selection. The findings from this study support that genetic factors, in addition to demographic factors, can contribute to extinctions of small local populations and also of metapopulations.

School dismissal as a pandemic influenza response: When, where and for how long?

We used individual-based computer simulation models at community, regional and national levels to evaluate the likely impact of coordinated pre-emptive school dismissal policies during an influenza pandemic. Such policies involve three key decisions: when, over what geographical scale, and how long to keep schools closed. Our evaluation includes uncertainty and sensitivity analyses, as well as model output uncertainties arising from variability in serial intervals and presumed modifications of social contacts during school dismissal periods. During the period before vaccines become widely available, school dismissals are particularly effective in delaying the epidemic peak, typically by 4–6 days for each additional week of dismissal. Assuming the surveillance is able to correctly and promptly diagnose at least 5–10% of symptomatic individuals within the jurisdiction, dismissals at the city or county level yield the greatest reduction in disease incidence for a given dismissal duration for all but the most severe pandemic scenarios considered here. Broader (multi-county) dismissals should be considered for the most severe and fast-spreading (1918-like) pandemics, in which multi-month closures may be necessary to delay the epidemic peak sufficiently to allow for vaccines to be implemented.

Simulation-based evaluation of reserve network performance for Centrophorus zeehaani (Centrophoridae): a protected deep-sea gulper shark

Abstract Resource use and conservation objectives can conflict where protected species and commercially fished species occur together. We tested the potential for a previously overfished deep-sea shark (Centrophorus zeehaani) with very low biological productivity to recover in a network of three reserves. An individual-based simulation model was developed and applied using reproduction and movement data. Without any reserves or reductions in fishing mortality the population will never recover and will fail in 27.9 ± 6.1 years. With three reserves, recovery from 8% of initial numbers to a target of 20% would take 63.1 ± 3.1 years with the network in place. Length of the female cycle, natural mortality, and density dependent female dispersal are uncertain, but could delay recovery time by an additional 16.5, 98.3, or 61.9 years, respectively. Effectiveness of the reserve network was particularly sensitive to location and patchiness of the population. Doubling the size of a reserve where C. zeehaani are abundant would reduce recovery time by 12.5 years; halving it would increase recovery time by 12.0 years. Future re-opening of orange roughy fishing in waters deeper than 750 m would delay recovery of C. zeehaani by 45.9 years. The methods developed here can be used to explore co-management options for target species and protected species in fisheries.

Cost-effectiveness and Budgetary Impact of Hepatitis C Virus Testing, Treatment, and Linkage to Care in US Prisons.

Hepatitis C virus (HCV) testing and treatment uptake in prisons remains low. We aimed to estimate clinical outcomes, cost-effectiveness (CE), and budgetary impact (BI) of HCV testing and treatment in United States (US) prisons or linkage to care at release. We used individual-based simulation modeling with healthcare and Department of Corrections (DOC) perspectives for CE and BI analyses, respectively. We simulated a US prison cohort at entry using published data and Washington State DOC individual-level data. We considered permutations of testing (risk factor based, routine at entry or at release, no testing), treatment (if liver fibrosis stage ≥F3, for all HCV infected or no treatment), and linkage to care (at release or no linkage). Outcomes included quality-adjusted life-years (QALY); cases identified, treated, and cured; cirrhosis cases avoided; incremental cost-effectiveness ratios; DOC costs (2016 US dollars); and BI (healthcare cost/prison entrant) to generalize to other states. Compared to "no testing, no treatment, and no linkage to care," the "test all, treat all, and linkage to care at release" model increased the lifetime sustained virologic response by 23%, reduced cirrhosis cases by 54% at a DOC annual additional cost of $1440 per prison entrant, and would be cost-effective. At current drug prices, targeted testing and liver fibrosis-based treatment provided worse outcomes at higher cost or worse outcomes at higher cost per QALY gained. In sensitivity analysis, fibrosis-based treatment restrictions were cost-effective at previous higher drug costs. Although costly, widespread testing and treatment in prisons is considered to be of good value at current drug prices.

A genetic approach for simulating persistence of reintroduced tree species populations in restored forests

Tree populations in regions undergoing restoration are generally made up of few individuals, isolated from neighboring populations, and are found within a matrix of inhospitable human-modified landscapes. Resulting negative genetic consequences such as inbreeding depression and genetic drift require mitigation strategies to maintain sufficient genetic diversity in restoration areas. Such strategies often involve seed sampling from many source trees with different provenances. However, the efficacy of these approaches has not been validated. We present an individual-based spatial simulation model to evaluate the effects of: 1) differing levels of initial genetic diversity; and 2) different area sizes on short (tens of years) and mid-term (hundreds of years) restored population viability. We demonstrate this approach and the use of our model with case study of Centrolobium tomentosum, a tropical tree species widely used in restoration projects in the Atlantic Forest of Brazil. Our model and analysis framework can be applied in studies of tree species with different characteristics, from tropical and temperate forests, to assess population persistence in restoration sites as a function of genetic diversity and population size. This knowledge can support planning of both restoration projects and management actions, increasing the probability of restoration success while also reducing associated costs.

Intersexual Resource Competition and the Evolution of Sex-Biased Dispersal

Resource competition is a major driver of dispersal: an emigrating individual leaves more resources to its kin. Existing models of sex-biased dispersal rarely consider intersexual competition for resources. Instead, male reproductive success is often solely assumed to depend on female availability, implying a tacit assumption that male presence never depletes resources, such as food, that are of interest to female kin. In reality, both male and female offspring typically consume resources on their natal site before departing to consume resources elsewhere, and sexually dimorphic body sizes imply that the resource needs can differ. The goal of our study is to investigate how intersexual competition for resources can affect the evolution of sex-specific dispersal, via competition between kin of the same sex or different sexes and the subsequent success elsewhere. Our individual-based simulation model allows not only the dispersal probability but also its timing to evolve. We consider dispersal timing because later dispersal yields a longer period of kin competition than dispersal that occurs soon after independence. We also highlight the role of sex-specific income/capital breeding strategies, which is understudied in both empirical and theoretical literature of sex-specific dispersal. We show that sex biases in dispersal probability and timing are sensitive to the presence of intersexual competition, sexual differences in capital vs. income breeding strategies, and sexual dimorphism in the quantity of resources consumed. Males may evolve to disperse earlier if they also consume more food, as a result of selection to reduce intersexual kin competition. Alternatively, males may evolve to disperse less as well as later than females, if male fitness depends more on resource accumulation (e.g., building a large body to succeed in mating competition under polygyny) whereas female fitness depends more on reliable income (e.g., in species that require extended periods of maternal foraging), even if both sexes are equally competitive in consuming resources. Although the more dispersive sex is often the earlier departing sex, we also find cases where the only clear dimorphism is found in dispersal timing. We thus encourage more studies on the timing aspect of sex-biased dispersal.

A spatially-explicit, individual-based demogenetic simulation framework for evaluating hybridization dynamics

Spatially-explicit individual-based simulation models provide a valuable tool for exploring complex ecological and evolutionary processes that are not easily empirically measured. Here, we present modifications of a spatially-explicit individual-based simulation model (CDMetaPOP) to accommodate a two-species system and simulations involving interspecific hybridization. We first describe how a hybrid (H) index is used to distinguish individuals of interspecific descent from those of either parental species. User-defined thresholds provide flexibility in the degree of admixture tolerated for classifying ‘pure’ individuals. We then detail relationships further informed by the H index, including individual growth, temperature-based fitness and selection, and mate preference behavior. Empirically derived species- and system-specific information can be incorporated into these relationships, for example, to produce differential growth among hybrids and parental species. Lastly, we demonstrate an application of this simulation framework by exploring the relative effects of temperature-based selection, mate preference behavior, and hybrid fitness on the rate and spatial extent of sympatric hybridization between two native riverine fish species, bull trout (Salvelinus confluentus) and Dolly Varden (Salvelinus malma), in the upper Skagit River system (United States and Canada). Results from this demonstration provide guidance for future empirical studies of bull trout, a federally threatened species. Understanding factors that contribute to the initiation and maintenance of hybridization, as well as the ecological and evolutionary consequences of this phenomenon, is of increasing importance given shifting species ranges due to large-scale landscape modification and a changing global climate. Our framework can be used to study a wide range of hybridization dynamics in any terrestrial or aquatic system, including comparisons of distinct environmental conditions or potential management responses.

Challenges in modelling the proportion of undiagnosed HIV infections in Sweden.

BackgroundSweden has a low HIV prevalence. However, among new HIV diagnoses in 2016, the proportion of late presenters and migrants was high (59% and 81%, respectively). This poses challenges in estimating the proportion of undiagnosed persons living with HIV (PLHIV).AimTo estimate the proportion of undiagnosed PLHIV in Sweden comparing two models with different demands on data availability and modelling expertise.MethodsAn individual-based stochastic simulation model of HIV positive populations (SSOPHIE) and the incidence method of the European Centre for Disease Prevention and Control (ECDC) HIV Modelling Tool were applied to clinical, surveillance and migration data from Sweden 1980–2016.ResultsSSOPHIE estimated that the proportion of undiagnosed PLHIV in 2013 was 26% (n = 2,100; 90% plausibility range (PR): 900–5,000) for all PLHIV, 17% (n = 600; 90% PR: 100–2,000) for men who have sex with men (MSM), 35% in male (n = 300; 90% PR: 200–700) and 34% in female (n = 400; 90% PR: 200–800) migrants from sub-Saharan Africa (SSA). The estimates for the ECDC model in 2013 were 21% (n = 2,013; 95% confidence interval (CI): 1,831–2,189) for all PLHIV, 15% (n = 369; 95% CI: 299–434) for MSM and 21% (n = 530; 95% CI: 436–632) for migrants from SSA.ConclusionsThe proportion of undiagnosed PLHIV in Sweden is uncertain. SSOPHIE estimates had wide PR. The ECDC model estimates were unreliable because migration was not accounted for. Better migration data and estimation methods are required to obtain reliable estimates of proportions of undiagnosed PLHIV in similar settings.

Unimodal diversity-productivity relationship emerged under stressful environment through sampling effect

Researches on the context dependence of biodiversity and ecosystem functioning (BEF) reveal the variation of diversity-productivity relationship (DPR) under stressful environment. The “habitat sampling effect” (HSE) is proposed as the dominant species interaction mechanism at stressful environment, whereas its potential role in driving the DPR has never been testified before. We constructed an individual-based simulation model to explore the variation of DPR along environmental stress gradient, and evaluated the contribution of HSE in explaining this variation. Our results indicated that DPR changed from positive to negative along environmental stress gradients. An unimodal DPR curve emerged at stressful environment, which was caused by the counterbalance of two opposite impacts of HSE on community productivity. At low richness level, the positive impact of HSE on community productivity dominated through the promotion of community size, which resulted in the positive DPR. Whereas with the increase of richness, the negative impact of HSE dominated instead through the reducing of individual productivity, which caused the decreasing part of unimodal curve. Our results highlight the complex characteristic of BEF relationship at stressful environment, and emphasize the necessity of biodiversity in maintaining community's functioning at stressful environment which are often sparse in species richness.

Population structure leads to male-biased population sex ratios under environmental sex determination.

Spatial structure has been shown to favor female-biased sex allocation, but current theory fails to explain male biases seen in many taxa, particularly those with environmental sex determination (ESD). We present a theory and accompanying individual-based simulation model that demonstrates how population structure leads to male-biased population sex ratios under ESD. Our simulations agree with earlier work showing that the high productivity of female-producing habitats creates a net influx of sex-determining alleles into male-producing habitats, causing larger sex ratio biases, and lower productivity in male-producing environments (Harts etal. 2014). In contrast to previous findings, we show that male-biasing habitats disproportionately impact the global sex ratio, resulting in stable male-biased population sex ratios under ESD. The failure to detect a male bias in earlier work can be attributed to small subpopulation sizes leading to local mate competition, a condition unlikely to be met in most ESD systems. Simulations revealed that consistent male biases are expected over a wide range of population structures, environmental conditions, and genetic architectures of sex determination, with male excesses as large as 30 percent under some conditions. Given the ubiquity of genetic structure in natural populations, we predict that modest, enduring male biased allocation should be common in ESD species, a pattern consistent with reviews of ESD sex ratios.

Implementing spatially explicit wind-driven seed and pollen dispersal in the individual-based larch simulation model: LAVESI-WIND 1.0

Abstract. It is of major interest to estimate the feedback of arctic ecosystems to the global warming we expect in upcoming decades. The speed of this response is driven by the potential of species to migrate, tracking their climate optimum. For this, sessile plants have to produce and disperse seeds to newly available habitats, and pollination of ovules is needed for the seeds to be viable. These two processes are also the vectors that pass genetic information through a population. A restricted exchange among subpopulations might lead to a maladapted population due to diversity losses. Hence, a realistic implementation of these dispersal processes into a simulation model would allow an assessment of the importance of diversity for the migration of plant species in various environments worldwide. To date, dynamic global vegetation models have been optimized for a global application and overestimate the migration of biome shifts in currently warming temperatures. We hypothesize that this is caused by neglecting important fine-scale processes, which are necessary to estimate realistic vegetation trajectories. Recently, we built and parameterized a simulation model LAVESI for larches that dominate the latitudinal treelines in the northernmost areas of Siberia. In this study, we updated the vegetation model by including seed and pollen dispersal driven by wind speed and direction. The seed dispersal is modelled as a ballistic flight, and for the pollination of ovules of seeds produced, we implemented a wind-determined and distance-dependent probability distribution function using a von Mises distribution to select the pollen donor. A local sensitivity analysis of both processes supported the robustness of the model's results to the parameterization, although it highlighted the importance of recruitment and seed dispersal traits for migration rates. This individual-based and spatially explicit implementation of both dispersal processes makes it easily feasible to inherit plant traits and genetic information to assess the impact of migration processes on the genetics. Finally, we suggest how the final model can be applied to substantially help in unveiling the important drivers of migration dynamics and, with this, guide the improvement of recent global vegetation models.

Defining stopping criteria for ending randomized clinical trials that investigate the interruption of transmission of soil-transmitted helminths employing mass drug administration.

The current World Health Organization strategy to address soil-transmitted helminth (STH) infections in children is based on morbidity control through routine deworming of school and pre-school aged children. However, given that transmission continues to occur as a result of persistent reservoirs of infection in untreated individuals (including adults) and in the environment, in many settings such a strategy will need to be continued for very extended periods of time, or until social, economic and environmental conditions result in interruption of transmission. As a result, there is currently much discussion surrounding the possibility of accelerating the interruption of transmission using alternative strategies of mass drug administration (MDA). However, the feasibility of achieving transmission interruption using MDA remains uncertain due to challenges in sustaining high MDA coverage levels across entire communities. The DeWorm3 trial, designed to test the feasibility of interrupting STH transmission, is currently ongoing. In DeWorm3, three years of high treatment coverage—indicated by mathematical models as necessary for breaking transmission—will be followed by two years of surveillance. Given the fast reinfection (bounce-back) rates of STH, a two year no treatment period is regarded as adequate to assess whether bounce-back or transmission interruption have occurred in a given location. In this study, we investigate if criteria to determine whether transmission interruption is unlikely can be defined at earlier timepoints. A stochastic, individual-based simulation model is employed to simulate core aspects of the DeWorm3 community-based cluster-randomized trial. This trial compares a control arm (annual treatment of children alone with MDA) with an intervention arm (community-wide biannual treatment with MDA). Simulations were run for each scenario for both Ascaris lumbricoides and hookworm (Necator americanus). A range of threshold prevalences measured at six months after the last round of MDA and the impact of MDA coverage levels were evaluated to see if the likelihood of bounce-back or elimination could reliably be assessed at that point, rather than after two years of subsequent surveillance. The analyses suggest that all clusters should be assessed for transmission interruption after two years of surveillance, unless transmission interruption can be effectively ruled out through evidence of low treatment coverage. Models suggest a tight range of homogenous prevalence estimates following high coverage MDA across clusters which do not allow for discrimination between bounce back or transmission interruption within 24 months following cessation of MDA.