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Birth-death processes are applied in the modelling of many biological populations, such as tumour cells and viruses. Various studies have established that birth-death processes, which occurwhen the population size is zero, are not in-line with reality in many situations. Therefore, in this study, the birth-death processes with immigration were investigated. We considered two immigration policies. First, immigration is allowed if and only if the population size is zero. Second, immigration at a constant rate is allowed irrespective of the population size. Birth and death rates were chosen such that the mean population size is a Gompertz function when the immigration rate is zero. The transient population size probability was obtained for both cases. Several tumour growth datasets were fitted using the mean population size of the above models and standard birth-death model without immigration. The two models with immigration provided entirely different probabilities of the population size being zero at an arbitrary epoch when compared with the model without immigration. Moreover, all three models provided a similar fit to the data. For each of the datasets studied, the models that allowed immigration produced less variance than the non-immigration model.

In this work, we study a linear birth–death process starting from random initial conditions. First, we consider these initial conditions as a random number of particles following different standard probabilistic distributions – Negative-Binomial and its closest Geometric, Poisson or Pólya–Aeppli distributions. It is proved analytically and numerically that in these cases the random number of particles alive at any positive time follows the same probability law like the initial condition, but with different parameters depending on time. The random initial conditions cannot change the critical parameter of branching mechanism, but they impact the extinction probability. Finally, the numerical model is extended to an application for studying branching processes with more complex initial conditions. This is demonstrated with a linear birth–death process initialised with Pólya urn sampling scheme. The obtained preliminary results for particle distribution show close relation to Pólya–Aeppli distribution.

We consider continuous-time branching random walks on multidimensional lattices with birth and death of particles at a finite number of lattice points. Such processes are used in numerous applications, in particular, in statistical physics, population dynamics, and chemical kinetics. In the last decade, for various models of branching random walks, a series of limit theorems about the behavior of the process for large times has been obtained. However, it is almost impossible to analyze analytically branching random walks on finite time intervals; so in this paper we present an algorithm for simulating branching random walks and examples of its numerical realization.

The United Nations' Sustainable Development Goal 3.2 aims to reduce under-5 child mortality to 25 deaths per 1,000 live births by 2030. Child mortality tends to be concentrated in developing regions where much of the information needed to assess achievement of this goal comes from surveys and censuses. In both, women are asked about their birth histories, but with varying degrees of detail. Full birth history (FBH) data contain the reported dates of births and deaths of every surveyed mother's children. In contrast, summary birth history (SBH) data contain only the total number of children born and total number of children who died for each mother. Specialized methods are needed to accommodate this type of data into analyses of child mortality trends. We develop a data augmentation scheme within a Bayesian framework where for SBH data, birth and death dates are introduced as auxiliary variables. Since we specify a full probability model for the data, many of the well-known biases that exist in this data can be accommodated, along with space-time smoothing on the underlying mortality rates. We illustrate our approach in a simulation, showing that uncertainty is reduced when incorporating SBH data over simply analyzing all available FBH data. We also apply our approach to data in the Central region of Malawi. We compare with the well-known Brass method., 50 pages, 18 figures

We discuss a continuous-time Markov branching model in which each individual can trigger an alarm according to a Poisson process. The model is stopped when a given number of alarms is triggered or when there are no more individuals present. Our goal is to determine the distribution of the state of the population at this stopping time. In addition, the state distribution at any fixed time is also obtained. The model is then modified to take into account the possible influence of death cases. All distributions are derived using probability-generating functions, and the approach followed is based on the construction of families of martingales.

We apply physical interpretations to construct algorithms for the key matrix G of discrete-time quasi-birth-and-death (dtQBD) processes which records the probability of the process reaching level (...

We consider a continuous-time branching random walk on ℤ d with birth and death of particles at a periodic set of points (sources of branching). Spectral properties of the evolution operator of the mean number of particles are studied. We derive a representation of the mean value of particle number in a form of asymptotic series.

A flexible class of multivariate distributions for continuous lifetimes is proposed. The distribution is defined in terms of the age-at-death of m siblings. The expression for the joint density is derived using classical results from mathematical demography. The parameters of the distribution are the age-specific birth and death rates, in addition to a vector of relative death times for the m siblings. For the case of constant birth and death rates we are able to derive an explicit expression for the bivariate sibling density, which is proven to be MTP2, and hence has positive dependence. Further, we show that a special case of the sibling distribution belongs to the Block-Basu class of multivariate distribution. In the general case, with age-dependent birth and death rates, evaluation of the density involves numerical integration, but is still feasible.

A spatial open-population capture-recapture model is described that extends both the non-spatial open-population model of Schwarz and Arnason and the spatially explicit closed-population model of Borchers and Efford. The superpopulation of animals available for detection at some time during a study is conceived as a two-dimensional Poisson point process. Individual probabilities of birth and death follow the conventional open-population model. Movement between sampling times may be modeled with a dispersal kernel using a recursive Markovian algorithm. Observations arise from distance-dependent sampling at an array of detectors. As in the closed-population spatial model, the observed data likelihood relies on integration over the unknown animal locations; maximization of this likelihood yields estimates of the birth, death, movement, and detection parameters. The models were fitted to data from a live-trapping study of brushtail possums (Trichosurus vulpecula) in New Zealand. Simulations confirmed that spatial modeling can greatly reduce the bias of capture-recapture survival estimates and that there is a degree of robustness to misspecification of the dispersal kernel. An R package is available that includes various extensions.

We examine necessary and sufficient conditions for recurrence and positive recurrence of a class of irreducible, level-dependent quasi-birth-and-death (LDQBD) processes with a block tridiagonal structure that exhibits asymptotic convergence in the rows as the level tends to infinity. These conditions are obtained by exploiting a multi-dimensional Lyapunov drift approach, along with the theory of generalized Markov group inverses. Additionally, we highlight analogies to well-known average drift results for level-independent quasi-birth-and-death (QBD) processes.

This work presents a construction of stationary Markov models with negative-binomial marginal distributions. A simple closed form expression for the corresponding transition probabilities is given, linking the proposal to well-known classes of birth and death processes and thus revealing interesting characterizations. The advantage of having such closed form expressions is tested on simulated and real data., Forthcoming in "Statistics & Probability Letters"

International audience; We study the quasi-stationary behavior of multidimensional processes absorbed when one of the coordinates vanishes. Our results cover competitive or weakly cooperative Lotka-Volterra birth and death processes and Feller diffusions with competitive Lotka-Volterra interaction. To this aim, we develop original non-linear Lyapunov criteria involving two Lyapunov functions, which apply to general Markov processes.

The Sustainable Development Goals call for a total reduction of preventable child mortality before 2030. Further, the goals state the desirability to have subnational mortality estimates. Estimates at this level are required for health interventions at the subnational level. In a low and middle income countries context, the data on mortality typically consist of household surveys, which are carried out with a stratified, cluster design, and census microsamples. Most household surveys collect full birth history (FBH) data on birth and death dates of a mother's children, but censuses collect summary birth history (SBH) data which consist only of the number of children born and the number that died. In previous work, direct (survey-weighted) estimates with associated variances were derived from FBH data and smoothed in space and time. Unfortunately, the FBH data from household surveys are usually not sufficiently abundant to obtain yearly estimates at the Admin-2 level (at which interventions are often made). In this paper we describe four extensions to previous work: (i) combining SBH data with FBH data, (ii) modeling on a yearly scale, to combine data on a yearly scale with data at coarser time scales, (iii) adjusting direct estimates in Admin-2 areas where we do not observe any deaths due to small sample sizes, (iv) acknowledge differences in data sources by modeling potential bias arising from the various data sources. The methods are illustrated using household survey and census data from Kenya and Malawi, to produce mortality estimates from 1980 to the time of the most recent survey, and predictions to 2020.

Motivated by the analysis of spectrometric data, a Gaussian graphical model for learning the dependence structure among frequency bands of the infrared absorbance spectrum is introduced. The spectra are modeled as continuous functional data through a B-spline basis expansion and a Gaussian graphical model is assumed as a prior specification for the smoothing coefficients to induce sparsity in their precision matrix. Bayesian inference is carried out to simultaneously smooth the curves and to estimate the conditional independence structure between portions of the functional domain. The proposed model is applied to the analysis of infrared absorbance spectra of strawberry purees. Università Cattolica del Sacro Cuore, Italy

Motivated by the goal of understanding the evolution of populations undergoing selection, we consider branching Brownian motion in which particles independently move according to one-dimensional Brownian motion with drift, each particle may either split into two or die, and the difference between the birth and death rates is a linear function of the position of the particle. We show that, under certain assumptions, after a sufficiently long time, the empirical distribution of the positions of the particles is approximately Gaussian. This provides mathematically rigorous justification for results in the biology literature indicating that the distribution of the fitness levels of individuals in a population over time evolves like a Gaussian traveling wave., 83 pages

International audience; We prove under mild conditions that the Fleming-Viot process selects the minimal quasi-stationary distribution for Markov processes with soft killing on non-compact state spaces. Our results are applied to multi-dimensional birth and death processes, continuous time Galton-Watson processes and diffusion processes with soft killing.

We introduce and study the dynamics of an \emph{immortal} critical branching process. In the classic, critical branching process, particles give birth to a single offspring or die at the same rates. Even though the average population is constant in time, the ultimate fate of the population is extinction. We augment this branching process with immortality by positing that either: (a) a single particle cannot die, or (b) there exists an immortal stem cell that gives birth to ordinary cells that can subsequently undergo critical branching. We discuss the new dynamical aspects of this immortal branching process., Comment: 12 pages. For the special edition of Physica A in memory of Dietrich Stauffer. Version 2 contains some additional material and a few other changes in response to referee comments

Many spatiotemporal data record the time of birth and death of individuals, along with their spatial trajectories during their lifetime, whether through continuous-time observations or discrete-time observations. Natural applications include epidemiology, individual-based modelling in ecology, spatiotemporal dynamics observed in bioimaging and computer vision. The aim of this article is to estimate in this context the birth and death intensity functions that depend in full generality on the current spatial configuration of all alive individuals. While the temporal evolution of the population size is a simple birth–death process, observing the lifetime and trajectories of all individuals calls for a new paradigm. To formalise this framework, we introduce spatial birth–death–move processes, where the birth and death dynamics depends on the current spatial configuration of the population and where individuals can move during their lifetime according to a continuous Markov process with possible interactions. We consider non-parametric kernel estimators of their birth and death intensity functions. The setting is original because each observation in time belongs to a non-vectorial, infinite dimensional space and the dependence between observations is barely tractable. We prove the consistency of the estimators in the presence of continuous-time and discrete-time observations, under fairly simple conditions. We moreover discuss how we can take advantage in practice of structural assumptions made on the intensity functions and we explain how data-driven bandwidth selection can be conducted, despite the unknown (and sometimes undefined) second order moments of the estimators. We finally apply our statistical method to the analysis of the spatiotemporal dynamics of proteins involved in exocytosis in cells, providing new insights on this complex mechanism.

Consider a spectrally positive Stable$(1\!+\!\alpha )$ process whose jumps we interpret as lifetimes of individuals. We mark the jumps by continuous excursions assigning “sizes” varying during the lifetime. As for Crump–Mode–Jagers processes (with “characteristics”), we consider for each level the collection of individuals alive. We arrange their “sizes” at the crossing height from left to right to form an interval partition. We study the continuity and Markov properties of the interval-partition-valued process indexed by level. From the perspective of the Stable$(1\!+\!\alpha )$ process, this yields new theorems of Ray–Knight-type. From the perspective of branching processes, this yields new, self-similar models with dense sets of birth and death times of (mostly short-lived) individuals. This paper feeds into projects resolving conjectures by Feng and Sun (2010) on the existence of certain measure-valued diffusions with Poisson–Dirichlet stationary laws, and by Aldous (1999) on the existence of a continuum-tree-valued diffusion.

In this paper, an SIR model with birth and death on complex networks is analyzed, where infected individuals are divided into m groups according to their infection and contact between human is treated as a scale-free social network. We obtain the basic reproduction number R 0 as well as the effects of various immunization schemes. The results indicate that the disease-free equilibrium is locally and globally asymptotically stable in some conditions, otherwise disease-free equilibrium is unstable and exists an unique endemic equilibrium that is globally asymptotically stable. Our theoretical results are confirmed by numerical simulations and a promising way for infectious diseases control is suggested.

We analyze a spatial age-structured model with density regulation, age specific dispersal, stochasticity in vital rates and proportional harvesting. We include two age classes, juveniles and adults, where juveniles are subject to logistic density dependence. There are environmental stochastic effects with arbitrary spatial scales on all birth and death rates, and individuals of both age classes are subject to density independent dispersal with given rates and specified distributions of dispersal distances. We show how to simulate the joint density fields of the age classes and derive results for the spatial scales of all spatial autocovariance functions for densities. A general result is that the squared scale has an additive term equal to the squared scale of the environmental noise, corresponding to the Moran effect, as well as additive terms proportional to the dispersal rate and variance of dispersal distance for the age classes and approximately inversely proportional to the strength of density regulation. We show that the optimal harvesting strategy in the deterministic case is to harvest only juveniles when their relative value (e.g. financial) is large, and otherwise only adults. With increasing environmental stochasticity there is an interval of increasing length of values of juveniles relative to adults where both age classes should be harvested. Harvesting generally tends to increase all spatial scales of the autocovariances of densities.

We derive the variance constant of continuous-time level dependent quasi-birth-and-death processes by investigating the expected integral functionals of the first return times. As an application, w...

We consider an ecological system in which two species interact with two niches. To this end we introduce a stochastic model with four states. Our analysis is founded in three approaches: Monte Carlo simulations of the model on a square lattice, mean-field approximation, and birth and death master equation. From this last approach we obtain a description in terms of Langevin equations which show in an explicit way the role of noise in population biology. We focus mainly on the description of time oscillations of the species population and the alternating dominance between them. The model treated here may provide insights on these properties.

Cancer cells obtain mutations which rely on the production of diffusible growth factors to confer a fitness benefit. These mutations can be considered cooperative, and studied as public goods games within the framework of evolutionary game theory. The population structure, benefit function and update rule all influence the evolutionary success of cooperators. We model the evolution of cooperation in epithelial cells using the Voronoi tessellation model. Unlike traditional evolutionary graph theory, this allows us to implement global updating, for which birth and death events are spatially decoupled. We compare, for a sigmoid benefit function, the conditions for cooperation to be favoured and/or beneficial for well mixed and structured populations. We find that when population structure is combined with global updating, cooperation is more successful than if there were local updating or the population were well-mixed. Interestingly, the qualitative behaviour for the well-mixed population and the Voronoi tessellation model is remarkably similar, but the latter case requires significantly lower incentives to ensure cooperation., 40 Pages, 15 Figures. Accepted version

This paper examines the influence of environmental perturbations on dynamical regimes of model ecosystems. We study a stochastic lattice model describing the dynamics of a group chasing and escaping between predators and prey. The model includes smart pursuit (predators to prey) and evasion (prey from predators). Both species can affect their movement by visual perception within their finite sighting range. Non-conservative processes that change the number of individuals within the population, such as breeding and physiological dying, are implemented in the model. The model contains five parameters that control the breeding and physiological dying of predators and prey: the birth and two death rates of predators and two parameters characterizing the birth and death of prey. We study the response of our model of group chase and escape to sudden perturbations in values of parameters that characterize the non-conservative processes. Temporal dependencies of the number of predators and prey are compared for various perturbation events with different abrupt changes of probabilities affecting the non-conservative processes.

We derive the full kinetic equations describing the evolution of the probability density distribution for a structured population such as cells distributed according to their ages and sizes. The kinetic equations for such a "sizer-timer" model incorporates both demographic and individual cell growth rate stochasticities. Averages taken over the densities obeying the kinetic equations can be used to generate a second order PDE that incorporates the growth rate stochasticity. On the other hand, marginalizing over the densities yields a modified birth-death process that shows how age and size influence demographic stochasticity. Our kinetic framework is thus a more complete model that subsumes both the deterministic PDE and birth-death master equation representations for structured populations., Comment: 14 pages, 1 figure

A biologically motivated individual-based framework for evolution in network-structured populations is developed that can accommodate eco-evolutionary dynamics. This framework is used to construct a network birth and death model. The evolutionary graph theory model, which considers evolutionary dynamics only, is derived as a special case, highlighting additional assumptions that diverge from real biological processes. This is achieved by introducing a negative ecological feedback loop that suppresses ecological dynamics by forcing births and deaths to be coupled. We also investigate how fitness, a measure of reproductive success used in evolutionary graph theory, is related to the life-history of individuals in terms of their birth and death rates. In simple networks, these ecologically motivated dynamics are used to provide new insight into the spread of adaptive mutations, both with and without clonal interference. For example, the star network, which is known to be an amplifier of selection in evolutionary graph theory, can inhibit the spread of adaptive mutations when individuals can die naturally.

This work is motivated by a desire to quantify relationships between two time series of pulsing hormone concentrations. The locations of pulses are not directly observed and may be considered latent event processes. The latent event processes of pulsing hormones are often associated. It is this joint relationship we model. Current approaches to jointly modeling pulsing hormone data generally assume that a pulse in one hormone is coupled with a pulse in another hormone (one-to-one association). However, pulse coupling is often imperfect. Existing joint models are not flexible enough for imperfect systems. In this article, we develop a more flexible class of pulse association models that incorporate parameters quantifying imperfect pulse associations. We propose a novel use of the Cox process model as a model of how pulse events co-occur in time. We embed the Cox process model into a hormone concentration model. Hormone concentration is the observed data. Spatial birth and death Markov chain Monte Carlo is used for estimation. Simulations show the joint model works well for quantifying both perfect and imperfect associations and offers estimation improvements over single hormone analyses. We apply this model to luteinizing hormone (LH) and follicle stimulating hormone (FSH), two reproductive hormones. Use of our joint model results in an ability to investigate novel hypotheses regarding associations between LH and FSH secretion in obese and non-obese women.

In this paper, we present the extension of the analysis of time-dependent limiting characteristics the class of continuous-time birth and death processes defined on non-negative integers with speci...

We study global stability of endemic equilibrium of an epidemic model with birth and death on complex networks. Under some conditions, the local asymptotic stability of the endemic equilibrium was established by Zhang and Jin (2011) for correlated networks, and the global asymptotic stability was obtained by Chen and Sun (2014) for uncorrelated networks. In this work, we remove those conditions, and prove by constructing a Lyapunov function that the endemic equilibrium is globally asymptotically stable. Numerical simulations are also presented to illustrate the feasibility of the result.

A new SAIRS alcoholism model with birth and death on complex heterogeneous networks is proposed. The total population of our model is partitioned into four compartments: the susceptible individual, the light problem alcoholic, the heavy problem alcoholic and the recovered individual. The spread of alcoholism threshold R 0 is calculated by the next generation matrix method. When R 0 1 , the alcohol free equilibrium is globally asymptotically stable, then the alcoholics will disappear. When R 0 > 1 , the alcoholism equilibrium is global attractivity, then the number of alcoholics will remain stable and alcoholism will become endemic. Furthermore, the modified SAIRS alcoholism model on weighted contact network is introduced. Dynamical behavior of the modified model is also studied. Numerical simulations are also presented to verify and extend theoretical results. Our results show that it is very important to treat alcoholics to control the spread of the alcoholism.

For the discrete time Markov chains on Z + d , we construct their strong stationary duality under the assumption of Mobius monotonicity. We also present an application to the two-dimensional birth and death chain.

The Gompertz nonlinear growth (GNG) model with independently and identically distributed (i.i.d.) errors is often employed for describing growth data. However, the corresponding stochastic differential equation (SDE) variant is more realistic for modeling growth data, as it is capable of taking into account the effect of randomly fluctuating parameters, such as birth and death rates. However, one limitation of this prescription is that the diffusion term is assumed to be time independent. The purpose of this article is to generalize the Gompertz SDE model by taking the diffusion coefficient as timevarying. The resultant model is solved analytically and methodology for estimation of parameters, based on the method of maximum likelihood, is developed. Formulas for optimal predictors and prediction error variances and the linear Gompertz SDE (LGSDE) model and modified Gompertz SDE (MGSDE) model are also derived. Superiority of the proposed MGSDE model is shown over the LGSDE and GNG models for pig growth data.

Homogeneous birth and death processes with a finite number of states are studied. We analyze the slowest and fastest rates of convergence to the limit mode. Estimates of these bounds for some classes of mean-field models are obtained. The asymptotics of the convergence rate for some models of chemical kinetics is studied in the case where the number of system states tends to infinity.

Inhomogeneous birth and death processes with intensities close to periodic are studied. Limit mean and double mean of such processes are analyzed, their estimates are obtained, and the method of their approximate calculation is developed. Also some examples from queuing systems theory are considered.

We study ergodic properties of a class of Markov-modulated general birth-death processes under fast regime switching. The first set of results concerns the ergodic properties of the properly scaled joint Markov process with a parameter that is taken large. Under very weak hypotheses, we show that if the averaged process is exponentially ergodic for large values of the parameter, then the same applies to the original joint Markov process. The second set of results concerns steady-state diffusion approximations, under the assumption that the 'averaged' fluid limit exists. Here, we establish convergence rates for the moments of the approximating diffusion process to those of the Markov modulated birth-death process. This is accomplished by comparing the generator of the approximating diffusion and that of the joint Markov process. We also provide several examples which demonstrate how the theory can be applied., 23 pages

Journal of Theoretical Biology, 488, ISSN:0022-5193, ISSN:1095-8541

Graphical models provide powerful tools to uncover complicated patterns in multivariate data and are commonly used in Bayesian statistics and machine learning. In this paper, we introduce the R package BDgraph which performs Bayesian structure learning for general undirected graphical models (decomposable and non-decomposable) with continuous, discrete, and mixed variables. The package efficiently implements recent improvements in the Bayesian literature, including that of Mohammadi and Wit (2015) and Dobra and Mohammadi (2018). To speed up computations, the computationally intensive tasks have been implemented in C++ and interfaced with R, and the package has parallel computing capabilities. In addition, the package contains several functions for simulation and visualization, as well as several multivariate datasets taken from the literature and used to describe the package capabilities. The paper includes a brief overview of the statistical methods which have been implemented in the package. The main part of the paper explains how to use the package. Furthermore, we illustrate the package's functionality in both real and artificial examples., Comment: Published at https://www.jstatsoft.org/article/view/v089i03 in the Journal of Statistical Software

We consider models of the population or opinion dynamics which result in the non-linear stochastic differential equations (SDEs) exhibiting the spurious long-range memory. In this context, the correspondence between the description of the birth-death processes as the continuous-time Markov chains and the continuous SDEs is of high importance for the alternatives of modeling. We propose and generalize the Bessel-like birth-death process having clear representation by the SDEs. The new process helps to integrate the alternatives of description and to derive the equations for the probability density function (PDF) of the burst and inter-burst duration of the proposed continuous time birth-death processes., 11 pages, 3 figures

A feature of human creativity is the ability to take a subset of existing items (e.g. objects, ideas, or techniques) and combine them in various ways to give rise to new items, which, in turn, fuel further growth. Occasionally, some of these items may also disappear (extinction). We model this process by a simple stochastic birth-death model, with non-linear combinatorial terms in the growth coefficients to capture the propensity of subsets of items to give rise to new items. In its simplest form, this model involves just two parameters (P, α). This process exhibits a characteristic 'hockey-stick' behaviour: a long period of relatively little growth followed by a relatively sudden 'explosive' increase. We provide exact expressions for the mean and variance of this time to explosion and compare the results with simulations. We then generalise our results to allow for more general parameter assignments, and consider possible applications to data involving human productivity and creativity.

A major cause of chemoresistance and recurrence in tumors is the presence of dormant tumor foci that survive chemotherapy and can eventually transition to active growth to regenerate the cancer. In this paper, we propose a Quasi Birth-and-Death (QBD) model for the dynamics of tumor growth and recurrence/remission of the cancer. Starting from a discrete-state master equation that describes the time-dependent transition probabilities between states with different numbers of dormant and active tumor foci, we develop a framework based on a continuum-limit approach to determine the time-dependent probability that an undetectable residual tumor will become large enough to be detectable. We derive an exact formula for the probability of recurrence at large times and show that it displays a phase transition as a function of the ratio of the death rate µA of an active tumor focus to its doubling rate λ. We also derive forward and backward Kolmogorov equations for the transition probability density in the continuum limit and, using a first-passage time formalism, we obtain a drift-diffusion equation for the mean recurrence time and solve it analytically to leading order for a large detectable tumor size N. We show that simulations of the discrete-state model agree with the analytical results, except for O(1/N) corrections. Finally, we describe a scheme to fit the model to recurrence-free survival (Kaplan-Meier) curves from clinical cancer data, using ovarian cancer data as an example. Our model has potential applications in predicting how changing chemotherapy schedules may affect disease recurrence rates, especially in cancer types for which no targeted therapy is available.

The taboo rate is first defined, which satisfies with the Chapman–Kolmogorov equation. Then the differentials of hitting time distribution are expressed by many different taboo rates, which deeply reveal the intrinsic relationship between the transition rate matrix and the hitting time distribution in continuous-time reversible Markov chains. As an example, the explicit expressions of the differentials of the hitting time distribution at a single state are provided for the birth and death chain, hence the transition rate matrix can be identified. Such differentials improve the theory of statistical identification of continuous-time reversible Markov chains.

Highlights • Reconstructed phylogenetic trees contain valuable information on population dynamics. • Considering records of occurrences through time allows us to get more relevant data. • Data is modeled using a birth-death process with specific sampling scheme. • The distribution of the population size conditioned on the data is derived. • This distribution will foster new advances in macroevolution and epidemiology., We consider a homogeneous birth-death process with three different sampling schemes. First, individuals can be sampled through time and included in a reconstructed phylogenetic tree. Second, they can be sampled through time and only recorded as a point ‘occurrence’ along a timeline. Third, extant individuals can be sampled and included in the reconstructed phylogenetic tree with a fixed probability. We further consider that sampled individuals can be removed or not from the process, upon sampling, with fixed probability. We derive the probability distribution of the population size at any time in the past conditional on the joint observation of a reconstructed phylogenetic tree and a record of occurrences not included in the tree. We also provide an algorithm to simulate ancestral population size trajectories given the observation of a reconstructed phylogenetic tree and occurrences. This distribution can be readily used to draw inferences about the ancestral population size in the field of epidemiology and macroevolution. In epidemiology, these results will allow data from epidemiological case count studies to be used in conjunction with molecular sequencing data (yielding reconstructed phylogenetic trees) to coherently estimate prevalence through time. In macroevolution, it will foster the joint examination of the fossil record and extant taxa to reconstruct past biodiversity.

The tumor control probability (TCP) is a metric used to calculate the probability of controlling or eradicating tumors through radiotherapy. Cancer cells vary in their response to radiation, and although many factors are involved, the tumor microenvironment is a crucial one that determines radiation efficacy. The tumor microenvironment plays a significant role in cancer initiation and propagation, as well as in treatment outcome. We have developed stochastic formulations to study the impact of arbitrary microenvironmental fluctuations on TCP and extinction probability (EP), which is defined as the probability of cancer cells removal in the absence of treatment. Since the derivation of analytical solutions are not possible for complicated cases, we employ a modified Gillespie algorithm to analyze TCP and EP, considering the random variations in cellular proliferation and death rates. Our results show that increasing the standard deviation in kinetic rates initially enhances the probability of tumor eradication. However, if the EP does not reach a probability of 1, the increase in the standard deviation subsequently has a negative impact on probability of cancer cells removal, decreasing the EP over time. The greatest effect on EP has been observed when both birth and death rates are being randomly modified and are anticorrelated. In addition, similar results are observed for TCP, where radiotherapy is included, indicating that increasing the standard deviation in kinetic rates at first enhances the probability of tumor eradication. But, it has a negative impact on treatment effectiveness if the TCP does not reach a probability of 1.

In this paper, a fractional SIR model with birth and death rates on heterogeneous complex networks is proposed. Firstly, we obtain a threshold value R 0 based on the existence of endemic equilibrium point E ∗ , which completely determines the dynamics of the model. Secondly, by using Lyapunov function and Kirchhoff’s matrix tree theorem, the globally asymptotical stability of the disease-free equilibrium point E 0 and the endemic equilibrium point E ∗ of the model are investigated. That is, when R 0 1 , the disease-free equilibrium point E 0 is globally asymptotically stable and the disease always dies out; when R 0 > 1 , the disease-free equilibrium point E 0 becomes unstable and in the meantime there exists a unique endemic equilibrium point E ∗ , which is globally asymptotically stable and the disease is uniformly persistent. Finally, the effects of various immunization schemes are studied and compared. Numerical simulations are given to demonstrate the main results.

We developed the indirect method for stochastic logistic growth models involving both birth and death rates in the drift and diffusion coefficients, and not only propose two indirect estimators, but also construct a likelihood ratio-type indirect statistic for testing hypotheses concerning parameters. Simulations show that the proposed two indirect estimators can correct the discretization bias, and the proposed indirect test possesses very good estimated power and size.

A new epidemic spreading model where individuals can be infected repeatedly is proposed in this paper. The infection rate decreases according to the times it has been infected before. This phenomenon may be caused by immunity or heightened alertness of individuals. We introduce a new parameter called decay factor to evaluate the decrease of infection rate. Our model bridges the Susceptible–Infected–Susceptible ( S I S ) model and the Susceptible–Infected–Recovered ( S I R ) model by this parameter. The proposed model has been studied by Monte-Carlo numerical simulation. It is found that initial infection rate has greater impact on peak value comparing with decay factor. The effect of decay factor on final density and threshold of outbreak is dominant but weakens significantly when considering birth and death rates. Besides, simulation results show that the influence of birth and death rates on final density is non-monotonic in some circumstances.

The stochastic versions of the logistic and extended logistic growth models are applied successfully to explain many real-life population dynamics and share a central body of literature in stochastic modeling of ecological systems. To understand the randomness in the population dynamics of the underlying processes completely, it is important to have a clear idea about the quasi-equilibrium distribution and its moments. Bartlett et al. (1960) took a pioneering attempt for estimating the moments of the quasi-equilibrium distribution of the stochastic logistic model. Matis and Kiffe (1996) obtain a set of more accurate and elegant approximations for the mean, variance and skewness of the quasi-equilibrium distribution of the same model using cumulant truncation method. The method is extended for stochastic power law logistic family by the same and several other authors (Nasell, 2003; Singh and Hespanha, 2007). Cumulant truncation and some alternative methods e.g. saddle point approximation, derivative matching approach can be applied if the powers involved in the extended logistic set up are integers, although plenty of evidence is available for non-integer powers in many practical situations (Sibly et al., 2005). In this paper, we develop a set of new approximations for mean, variance and skewness of the quasi-equilibrium distribution under more general family of growth curves, which is applicable for both integer and non-integer powers. The deterministic counterpart of this family of models captures both monotonic and non-monotonic behavior of the per capita growth rate, of which theta-logistic is a special case. The approximations accurately estimate the first three order moments of the quasi-equilibrium distribution. The proposed method is illustrated with simulated data and real data from global population dynamics database.

Overexploitation of commercially beneficial fish is a serious ecological problem around the world. The growth profiles of most of the species are likely to follow density regulated theta-logistic model irrespective of any taxonomy group [Sibly et al., Science, 2005]. Rapid depletion of population size may cause reduced fitness, and the species is exposed to Allee phenomena. Here sustainability is addressed by modelling the herring population as a stochastic process and computing the probability of extinction and expected time to extinction. The models incorporate an Allee effect, crowding effect which reduce birth and death rates at large populations, and two possible choices of harvesting models viz. linear harvesting and nonlinear harvesting. A seminal attempt is made by Saha [Saha et al., Ecol. Model, 2013] for this economically beneficial fish, but ignored the vital phenomena of harvesting. Moreover, in this model, the demographic stochasticity is introduced through the white-noise term, which has certain limitations when harvesting is introduced into the system. White noise is appropriate for such a system where immigration and emigration are allowed, but a harvesting model is rational for a closed system. The demographic stochasticity is introduced by replacing an ordinary differential equation model with a stochastic differential equation model, where the instantaneous variance in the SDE is derived directly from the birth and death rates of a birth-death process. The modelling parameters are fit to data of the herring populations collected from Global Population Dynamics Database (GPDD), and the risk of extinction of each population is computed under different harvesting protocols. A threshold for handling times is computed beneath which the risk of extinction is high. This is proposed as a recommendation to management for sustainable harvesting.

Power-law-distributed species counts or clone counts arise in many biological settings such as multispecies cell populations, population genetics, and ecology. This empirical observation that the number of species $c_{k}$ represented by $k$ individuals scales as negative powers of $k$ is also supported by a series of theoretical birth-death-immigration (BDI) models that consistently predict many low-population species, a few intermediate-population species, and very high-population species. However, we show how a simple global population-dependent regulation in a neutral BDI model destroys the power law distributions. Simulation of the regulated BDI model shows a high probability of observing a high-population species that dominates the total population. Further analysis reveals that the origin of this breakdown is associated with the failure of a mean-field approximation for the expected species abundance distribution. We find an accurate estimate for the expected distribution $\langle c_k \rangle$ by mapping the problem to a lower-dimensional Moran process, allowing us to also straightforwardly calculate the covariances $\langle c_k c_\ell \rangle$. Finally, we exploit the concepts associated with energy landscapes to explain the failure of the mean-field assumption by identifying a phase transition in the quasi-steady-state species counts triggered by a decreasing immigration rate., 22 pages, 8 figures, accepted to J. Phys. A