Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior

Individual responsive behavior to an influenza pandemic has significant impacts on the spread dynamics of this epidemic. Current influenza modeling efforts considering responsive behavior either oversimplify the process and may underestimate pandemic impacts, or make other problematic assumptions and are therefore constrained in utility. This study develops an agent-based model for pandemic simulation, and incorporates individual responsive behavior in the model based on public risk communication literature. The resultant model captures the stochastic nature of epidemic spread process, and constructs a realistic picture of individual reaction process and responsive behavior to pandemic situations. The model is then applied to simulate the spread dynamics of 2009 H1N1 influenza in a medium-size community in Arizona. Simulation results illustrate and compare the spread timeline and scale of this pandemic influenza, without and with the presence of pubic risk communication and individual responsive behavior. Sensitivity analysis sheds some lights on the influence of different communication strategies on pandemic impacts. Those findings contribute to effective pandemic planning and containment, particularly at the beginning of an outbreak.     

Modeling dynamics of an influenza pandemic with heterogeneous coping behaviors: case study of a 2009 H1N1 outbreak in Arizona

This paper aims to improve the accuracy of standard compartment models in modeling the dynamics of an influenza pandemic. Standard compartment models, which are commonly used in influenza simulations, make unrealistic assumptions about human behavioral responses during a pandemic outbreak. Existing simulation models with public avoidance also make a rigid assumption regarding the human behavioral response to influenza. This paper incorporates realistic assumptions regarding individuals’ avoidance behaviors in a standard compartment model. Both the standard and modified models are parameterized, implemented, and compared in the research context of the 2009 H1N1 influenza outbreak in Arizona. The modified model with heterogeneous coping behaviors forecasts influenza spread dynamics better than the standard model when evaluated against the empirical data, especially for the beginning of the 2009–2010 normal influenza season starting in October 2009 (i.e., the beginning of the second wave of 2009 H1N1). We end the paper with a discussion of the use of simulation models in efforts to help communities effectively prepare for and respond to influenza pandemics.     

From behavioural to population level: Growth and competition

The aim of this work is to build models of population dynamics for growth and competition interaction by starting with detailed models at the individual level. At the individual level, we start with detailed models where the growth is described by linear terms. By considering individual interferences and by using aggregation methods, we show that the population level, different growth equation can emerge. We present an example of the emergence of logistic growth and an example of the emergence of logistic growth with Allee effect. Furthermore, in the case of two populations, we show that individual interferences can lead at the population level, to a model which has the same qualitative dynamics behaviour as the Lotka-Volterra competition model. Finally, we show that our model brings to light the effects of spatial heterogeneity on competition models. First, we find the stabilizing effects but also we show that destabilizing effects can occur.     

Living with influenza: Impacts of government imposed and voluntarily selected interventions

Focusing on mitigation strategies for global pandemic influenza, we use elementary mathematical models to evaluate the implementation and timing of non-pharmaceutical intervention strategies such as travel restrictions, social distancing and improved hygiene. A spreadsheet model of infection spread between several linked heterogeneous communities is based on analytical calculations and Monte Carlo simulations. Since human behavior will likely change during the course of a pandemic, thereby altering the dynamics of the disease, we incorporate a feedback parameter into our model to reflect altered behavior. Our results indicate that while a flu pandemic could be devastating; there are coping methods that when implemented quickly and correctly can significantly mitigate the severity of a global outbreak.     

EFFECTS OF INDIVIDUAL HETEROGENEITY IN ESTIMATING THE PERSISTENCE OF SMALL POPULATIONS

ABSTRACT. Population viability models are commonly used to estimate the probability of persistence of small, threatened, or endangered populations. Demographic, temporal, spatial, and individual heterogeneity are important factors affecting the probability of persistence of small populations. Because stochastic process are intractable analytically (Lud-wig [1996]), computer simulation models are often used for estimating population viability via numerical techniques. Although demographic, spatial, and temporal stochasticity have been incorporated into some population viability models, individual heterogeneity has not been included. In this paper we include individual heterogeneity in a simulation model and examine probabilities of population persistence at different levels of heterogeneity and population size. Individual heterogeneity may increase the probability of persistence of small populations. The mechanism for the extension in persistence may be explained by natural selection. Genotypes persisting through a decline may be those that survive better under the conditions causing the decline. These individuals that survive and reproduce in the face of adverse conditions may extend the probability that a small population persists.     

Modeling stochastic mortality for joint lives through subordinators

There is a burgeoning literature on mortality models for joint lives. In this paper, we propose a new model in which we use time-changed Brownian motion with dependent subordinators to describe the mortality of joint lives. We then employ this model to estimate the mortality rate of joint lives in a well-known Canadian insurance data set. Specifically, we first depict an individual’s death time as the stopping time when the value of the hazard rate process first reaches or exceeds an exponential random variable, and then introduce the dependence through dependent subordinators. Compared with existing mortality models, this model better interprets the correlation of death between joint lives, and allows more flexibility in the evolution of the hazard rate process. Empirical results show that this model yields highly accurate estimations of mortality compared to the baseline non-parametric (Dabrowska) estimation.     

Using multiple objectives to approximate normative models

This paper discusses the rationale for the use of additive models involving multiple objectives as approximations to normative analyses. Experience has shown us that organizations often evaluate important decisions with multiple objective models rather than reducing all aspects of the problem to a single criterion, dollars, as many normative economic models prescribe. We justify this practice on two grounds: managers often prefer to think about a problem in terms of several dimensions and a multiple objective model may provide an excellent approximation to the more complex normative model. We argue that a useful analysis based on a multiple objective model will fulfill both conditions—it will provide insights for the decision maker as well as a good approximation to the normative model. We report several real-world examples of managers using multiple objective models to approximate such normative models as the risk-adjusted net present value and the value of information models. The agreement between the approximate models and the normative models is shown to be quite good. Next, we cite a portion of the behavioral decision theory literature which establishes that linear models of multiple attributes provide quite robust approximations to individual decision-making processes. We then present more general theoretical and empirical results which support our contention that linear multiple attribute models can provide good approximations to more complex models.     

SIR epidemics with stochastic infectious periods

We consider an SIR model for the spread of an epidemic in a closed and homogeneously mixing population, where the infectious periods are represented by an arbitrary absorbing Markov process. A version of this process starts whenever an infection occurs, and the contamination rate of the newly infected individual is a function of its state. When his process is absorbed, the individual becomes a removed case. We use a martingale approach to derive the distribution of the final epidemic size and severity for this class of models. Next, we examine some special cases. In particular, we focus on situations where the infection processes are Brownian motions and where they are Markov-modulated fluid flows. In the latter case, we use matrix-analytic methods to provide more explicit results. We conclude with some numerical illustrations.     

Agent-behaviour and network influence on energy innovation diffusion

An agent-based model is developed for investigating the role of individual behaviour and network influence on energy innovation diffusion. Behaviour is based on how agents value specific attributes of a technology, and network effects are disaggregated into indirect influence through exposure to a larger population, and direct influence through personal contacts. We find that network influence can have a positive effect on accelerating the diffusion of new energy innovations, but can be counteracted by risk adverse behaviour. Combined direct and indirect network effects can have as strong an influence on adoption behaviour as personal preferences. Interestingly, we find that indirect influence from the larger population can have a greater effect than direct personal contacts on an individual. This implies a feedback between population and sub-population level signals on adoption behaviour which warrants further exploration as a mechanism to induce individual level change.     

Mathematical modelling of acute virus influenza A infections

This article models the immune system and the virus dynamics of acute influenza infection mathematically. We use the model to study the virus dynamics of some well-known and severe and mild types of viruses. Linkages to well-known models in the literature are illustrated. Simulations are compared with experimental results in vivo by comparing with results from infected ferrets where infection closely resembles those in humans. Good agreement is achieved between the model calculations and the experimental values for influenza A viruses. For the Spanish flu virus H1N1 peak virus load is high and virtually all cells are infected in the nostril. In general, the H1N1 viruses show much more prolonged infections than the H3N2 in the nostril. We suggest that the reason is that unspecific immunity attacks H3N2-budded viruses but not H1N1 viruses.     

Multi-person multi-attribute decision making models under intuitionistic fuzzy environment

Intuitionistic fuzzy numbers, each of which is characterized by the degree of membership and the degree of non-membership of an element, are a very useful means to depict the decision information in the process of decision making. In this article, we investigate the group decision making problems in which all the information provided by the decision makers is expressed as intuitionistic fuzzy decision matrices where each of the elements is characterized by intuitionistic fuzzy number, and the information about attribute weights is partially known, which may be constructed by various forms. We first use the intuitionistic fuzzy hybrid geometric (IFHG) operator to aggregate all individual intuitionistic fuzzy decision matrices provided by the decision makers into the collective intuitionistic fuzzy decision matrix, then we utilize the score function to calculate the score of each attribute value and construct the score matrix of the collective intuitionistic fuzzy decision matrix. Based on the score matrix and the given attribute weight information, we establish some optimization models to determine the weights of attributes. Furthermore, we utilize the obtained attribute weights and the intuitionistic fuzzy weighted geometric (IFWG) operator to fuse the intuitionistic fuzzy information in the collective intuitionistic fuzzy decision matrix to get the overall intuitionistic fuzzy values of alternatives by which the ranking of all the given alternatives can be found. Finally, we give an illustrative example.     

A fractional order epidemic model for the simulation of outbreaks of influenza A(H1N1)

In this paper, we propose a nonlinear fractional order model in order to explain and understand the outbreaks of influenza A(H1N1). In the fractional model, the next state depends not only upon its current state but also upon all of its historical states. Thus, the fractional model is more general than the classical epidemic models. In order to deal with the fractional derivatives of the model, we rely on the Caputo operator and on the Grünwald–Letnikov method to numerically approximate the fractional derivatives. We conclude that the nonlinear fractional order epidemic model is well suited to provide numerical results that agree very well with real data of influenza A(H1N1) at the level population. In addition, the proposed model can provide useful information for the understanding, prediction, and control of the transmission of different epidemics worldwide. Copyright © 2013 John Wiley & Sons, Ltd.     

A new multiple attribute group decision making method in intuitionistic fuzzy setting

The multiple attribute group decision making (MAGDM) problem with intuitionistic fuzzy information investigated in this paper is very useful for solving complicated decision problems under uncertain circumstances. Since experts have their own characteristics, they are familiar with some of the attributes, but not others, the weights of the decision makers to different attributes should be different. We derive the weights of the decision makers by aggregating the individual intuitionistic fuzzy decision matrices into a collective intuitionistic fuzzy decision matrix. The expert has a big weight if his evaluation value is close to the mean value and has a small weight if his evaluation value is far from the mean value. For the incomplete attribute weight information, we establish some optimization models to determine the attribute weights. Furthermore, we develop several algorithms for ranking alternatives under different situations, and then extend the developed models and algorithms to the MAGDM problem with interval-valued intuitionistic fuzzy information. Numerical results finally illustrate the practicality and efficiency of our new algorithms.     

A simulation case study to improve staffing decisions at mass immunization clinics for pandemic influenza

Mass immunization clinics (MICs) are an important component of pandemic influenza control strategies in many jurisdictions. Decisions about staffing levels at MICs affect several factors of concern to public health authorities: total vaccination volume, patient wait-times, operating costs, and intra-facility influenza transmission risk. We present a discrete-event simulation of an MIC to assess how strongly staffing changes affect these factors. The simulation is based on data from Canadian clinics responding to pandemic H1N1 in 2009. This study is the first to model flu transmission risk at an MIC, and the first to relate such risk to staffing decisions. We show that the marginal benefit of adding staff is greatly underestimated if indirect waiting costs and intra-facility infections are not considered.     

Efficient Data Augmentation for Fitting Stochastic Epidemic Models to Prevalence Data

Stochastic epidemic models describe the dynamics of an epidemic as a disease spreads through a population. Typically, only a fraction of cases are observed at a set of discrete times. The absence of complete information about the time evolution of an epidemic gives rise to a complicated latent variable problem in which the state space size of the epidemic grows large as the population size increases. This makes analytically integrating over the missing data infeasible for populations of even moderate size. We present a data augmentation Markov chain Monte Carlo (MCMC) framework for Bayesian estimation of stochastic epidemic model parameters, in which measurements are augmented with subject-level disease histories. In our MCMC algorithm, we propose each new subject-level path, conditional on the data, using a time-inhomogenous continuous-time Markov process with rates determined by the infection histories of other individuals. The method is general, and may be applied to a broad class of epidemic models with only minimal modifications to the model dynamics and/or emission distribution. We present our algorithm in the context of multiple stochastic epidemic models in which the data are binomially sampled prevalence counts, and apply our method to data from an outbreak of influenza in a British boarding school. Supplementary material for this article is available online.     

Modelling heterogeneity in a manpower system: a review

Manpower planning is an essential methodology for business and industry; it allows managers to make more efficient use of human resources. However, human behaviour is highly variable and it is therefore essential for manpower planning that population heterogeneity is successfully modelled. In this paper we review methods of incorporating population heterogeneity into manpower modelling. The analysis of differentials in a manpower system is emphasized since they are a source of aggregation error in stochastic models. Two strategies have been stressed, the use of observable sources of heterogeneity as they affect wastage, and the latent sources which cannot be identified precisely but are known to affect the key parameters of most models. Copyright © 2000 John Wiley & Sons, Ltd.     

基于Kolmogorov前向方程评估甲型H1N1流感疫情的动态变化北大核心CSCD

基于个体水平的传染病模型可以揭示随机性在传染病疫情防控中的重要作用.研究此类模型的普遍方法是通过事件驱动的、大量重复的随机模拟来确定预测变量的范围.而基于Kolmogorov前向方程(KFE)研究个体水平的传染病模型,不仅不需要大量的重复模拟来确定预测变量的范围,而且可以同时考虑每种状态发生的概率.因此,基于2009年西安市第八医院甲型H1N1流感数据,建立了基于社交网络的个体决策心理模型,以确定行为改变率;进一步地,为得到传染病传播过程中各状态的概率分布,基于改进的个体SIR模型,通过Markov过程推导出KFE.结果表明:通过数值求解KFE可以得到整个爆发过程中每种状态发生的概率分布、最严重的时间段及相应的概率,从而能更快、更准确地了解甲型H1N1疫情的传播过程,因此有助于高效地进行甲型H1N1疫情防控.     

Estimating consumers' willingness to pay for individual quality attributes with DEA

In a highly competitive environment, a product's commercial success depends increasingly more upon the ability to satisfy consumers' preferences that are highly diversified. Since a product typically comprises a host of technological attributes, its market value incorporates all of the individual values of technological attributes. If the willingness-to-pay (WTP) for individual quality attributes of a product is known, one can conjecture the overall WTP or the imputed market price for the product. The market price listed by the producer has to be equal to or lower than this WTP for the commercial survival of the product. In this paper, we propose a methodology for estimating the value of individual product characteristics and thus the overall WTP of the product with DEA. Our methodology is based on a model derived from consumer demand theory on the one hand, and the recent developments in DEA on the other hand. The paper also presents a real case study for the mobile phone market, which is characterized by its high speed of innovation. On the theoretical side, we expect our framework to provide a possibility of combining DEA and consumer demand theory. We also expect that the empirical application will shed some light on the nature of the process of product differentiation based on consumers' valuation.     

A CA-based epidemic model for HIV/AIDS transmission with heterogeneity

The complex dynamics of HIV transmission and subsequent progression to AIDS make the mathematical analysis untraceable and problematic. In this paper, we develop an extended CA simulation model to study the dynamical behaviors of HIV/AIDS transmission. The model incorporates heterogeneity into agents’ behaviors. Agents have various attributes such as infectivity and susceptibility, varying degrees of influence on their neighbors and different mobilities. Additional, we divide the post-infection process of AIDS disease into several sub-stages in order to facilitate the study of the dynamics in different development stages of epidemics. These features make the dynamics more complicated. We find that the epidemic in our model can generally end up in one of the two states: extinction and persistence, which is consistent with other researchers’ work. Higher population density, higher mobility, higher number of infection source, and greater neighborhood are more likely to result in high levels of infections and in persistence. Finally, we show in four-class agent scenario, variation in susceptibility (or infectivity) and various fractions of four classes also complicates the dynamics, and some of the results are contradictory and needed for further research.     

Enhanced annuities and the impact of individual underwriting on an insurer’s profit situation

We analyze the effect of enhanced annuities on an insurer engaging in individual underwriting. We use a frailty model for heterogeneity of the insured population and model individual underwriting by a random variable that positively correlates with the corresponding frailty factor. For a given annuity portfolio, we analyze the effect of the quality of the underwriting on the insurer’s profit/loss situation and the impact of adverse selection effects.