Two-phase importance sampling for inference about transmission trees

There has been growing interest in the statistics community to develop methods for inferring transmission pathways of infectious pathogens from molecular sequence data. For many datasets, the computational challenge lies in the huge dimension of the missing data. Here, we introduce an importance sampling scheme in which the transmission trees and phylogenies of pathogens are both sampled from reasonable importance distributions, alleviating the inference. Using this approach, arbitrary models of transmission could be considered, contrary to many earlier proposed methods. We illustrate the scheme by analysing transmissions of Streptococcus pneumoniae from household to household within a refugee camp, using data in which only a fraction of hosts is observed, but which is still rich enough to unravel the within-household transmission dynamics and pairs of households between whom transmission is plausible. We observe that while probability of direct transmission is low even for the most prominent cases of transmission, still those pairs of households are geographically much closer to each other than expected under random proximity.     

Integrating genetic and epidemiological data to determine transmission pathways of foot-and-mouth disease virus

Estimating detailed transmission trees that reflect the relationships between infected individuals or populations during a disease outbreak often provides valuable insights into both the nature of disease transmission and the overall dynamics of the underlying epidemiological process. These trees may be based on epidemiological data that relate to the timing of infection and infectiousness, or genetic data that show the genetic relatedness of pathogens isolated from infected individuals. Genetic data are becoming increasingly important in the estimation of transmission trees of viral pathogens due to their inherently high mutation rate. Here, we propose a maximum-likelihood approach that allows epidemiological and genetic data to be combined within the same analysis to infer probable transmission trees. We apply this approach to data from 20 farms infected during the 2001 UK foot-and-mouth disease outbreak, using complete viral genome sequences from each infected farm and information on when farms were first estimated to have developed clinical disease and when livestock on these farms were culled. Incorporating known infection links due to animal movement prior to imposition of the national movement ban results in the reduction of the number of trees from 41472 that are consistent with the genetic data to 1728, of which just 4 represent more than 95% of the total likelihood calculated using a model that accounts for the epidemiological data. These trees differ in several ways from those constructed prior to the availability of genetic data.     

Optimizing agent-based transmission models for infectious diseases

Background

Infectious disease modeling and computational power have evolved such that large-scale agent-based models (ABMs) have become feasible. However, the increasing hardware complexity requires adapted software designs to achieve the full potential of current high-performance workstations.

Results

We have found large performance differences with a discrete-time ABM for close-contact disease transmission due to data locality. Sorting the population according to the social contact clusters reduced simulation time by a factor of two. Data locality and model performance can also be improved by storing person attributes separately instead of using person objects. Next, decreasing the number of operations by sorting people by health status before processing disease transmission has also a large impact on model performance. Depending of the clinical attack rate, target population and computer hardware, the introduction of the sort phase decreased the run time from 26 % up to more than 70 %. We have investigated the application of parallel programming techniques and found that the speedup is significant but it drops quickly with the number of cores. We observed that the effect of scheduling and workload chunk size is model specific and can make a large difference.

Conclusions

Investment in performance optimization of ABM simulator code can lead to significant run time reductions. The key steps are straightforward: the data structure for the population and sorting people on health status before effecting disease propagation. We believe these conclusions to be valid for a wide range of infectious disease ABMs. We recommend that future studies evaluate the impact of data management, algorithmic procedures and parallelization on model performance.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0612-2) contains supplementary material, which is available to authorized users.     

Integrating data mining and transmission theory in the ecology of infectious diseases

Our understanding of ecological processes is built on patterns inferred from data. Applying modern analytical tools such as machine learning to increasingly high dimensional data offers the potential to expand our perspectives on these processes, shedding new light on complex ecological phenomena such as pathogen transmission in wild populations. Here, we propose a novel approach that combines data mining with theoretical models of disease dynamics. Using rodents as an example, we incorporate statistical differences in the life history features of zoonotic reservoir hosts into pathogen transmission models, enabling us to bound the range of dynamical phenomena associated with hosts, based on their traits. We then test for associations between equilibrium prevalence, a key epidemiological metric and data on human outbreaks of rodent‐borne zoonoses, identifying matches between empirical evidence and theoretical predictions of transmission dynamics. We show how this framework can be generalized to other systems through a rubric of disease models and parameters that can be derived from empirical data. By linking life history components directly to their effects on disease dynamics, our mining‐modelling approach integrates machine learning and theoretical models to explore mechanisms in the macroecology of pathogen transmission and their consequences for spillover infection to humans.     

野生鸟类传染性疾病研究进展

由于具有独特的飞行能力和极强的地理扩散能力,鸟类活动为某些传染性疾病的快速传播和扩散带来了潜在风险.自20世纪以来,以禽霍乱、禽波特淋菌病、西尼罗河热、禽流感等为代表的鸟类疾病频繁暴发,导致为数众多的野生鸟类、家禽甚至人类死亡,给社会造成巨大的经济损失.因此,有关鸟类传染性疾病的研究已引起了国内外学者的广泛关注.从鸟类传染性疾病的生态学特征、疾病对鸟类与人类社会的影响、鸟类对疾病的传播、鸟类疾病的监测、预警和防控等方面对野生鸟类的传染性疾病研究进展进行了综述.不同疾病导致的鸟类死亡量、易感物种数量、暴发频率和地理扩散等特征差异显著.20世纪以来,疾病已成为全球生物多样性的七大威胁因子之一.疾病可能造成鸟类大量死亡,从而对鸟类种群,特别是濒危鸟类种群造成严重影响.其中,人畜共患病还会导致家禽家畜甚至人类的死亡,从而对社会产生严重的影响.野生鸟类作为多种疾病传播的媒介,其移动和迁徙可能会导致疾病的传播与扩散.开展全面的监测活动和建立疾病预警体系,对于疾病的防控具有重要意义.     

A Bayesian approach for inferring the dynamics of partially observed endemic infectious diseases from space-time-genetic data

We describe a statistical framework for reconstructing the sequence of transmission events between observed cases of an endemic infectious disease using genetic, temporal and spatial information. Previous approaches to reconstructing transmission trees have assumed all infections in the study area originated from a single introduction and that a large fraction of cases were observed. There are as yet no approaches appropriate for endemic situations in which a disease is already well established in a host population and in which there may be multiple origins of infection, or that can enumerate unobserved infections missing from the sample. Our proposed framework addresses these shortcomings, enabling reconstruction of partially observed transmission trees and estimating the number of cases missing from the sample. Analyses of simulated datasets show the method to be accurate in identifying direct transmissions, while introductions and transmissions via one or more unsampled intermediate cases could be identified at high to moderate levels of case detection. When applied to partial genome sequences of rabies virus sampled from an endemic region of South Africa, our method reveals several distinct transmission cycles with little contact between them, and direct transmission over long distances suggesting significant anthropogenic influence in the movement of infected dogs.     

H5N8亚型高致病性禽流感病毒的全球流行概况

H5N8亚型高致病性禽流感病毒(highly pathogenic avian influenza virus,HPAIV)随候鸟的迁徙活动及商业贸易活动现已蔓延至亚洲、欧洲、非洲、美洲等国家和地区.2014-2015和2016-2019年H5N8亚型HPAIV已引发两波全球疫情,现正经历第三波疫情,导致家禽及野生鸟类...     

Identification of a key amino acid in hemagglutinin that increases human-type receptor binding and transmission of an H6N2 avian influenza virus

Binding exclusively to human-type receptors is a prerequisite for avian influenza viruses to transmit from human to human. We previously reported that 34% of H6 avian influenza viruses recognize the human-type receptor, but their affinity for the avian-type receptor remains higher than that for the human-type receptor. Here, we found that a single amino acid change from glutamine to leucine at position 226 of hemagglutinin caused a switch in receptor-binding preference from avian-type to human-type receptors and rendered A/chicken/Guangdong/S1312/2010(H6N2) capable of respiratory droplet transmission in guinea pigs.     

分子生物学在禽流感诊断及预防中的应用

主要介绍了PCR、RT-PCR、基因芯片、基因重组等现代分子生物学技术在禽流感诊断与预防上的应用及研究现状。     

人禽流感疾病流行病学特征及其防治措施

最近几年来,因禽流感病毒感染而死亡的患者逐年增多,人禽流感在世界各地的流行已给许多国家和地区造成了不同程度的经济损失。为了更好地防止该病毒在全世界大规模流行,研究者开始重视对禽流感病原学特性、临床症状、传播途径、治疗方法及预防措施等进行研究与总结。我们针对人禽流感的研究进展及其预防与控制措施做简要概述。     

Contrasting the epidemiological and evolutionary dynamics of influenza spatial transmission

In the past decade, rapid increases in the availability of high-resolution molecular and epidemiological data, combined with developments in statistical and computational methods to simulate and infer migration patterns, have provided key insights into the spatial dynamics of influenza A viruses in humans. In this review, we contrast findings from epidemiological and molecular studies of influenza virus transmission at different spatial scales. We show that findings are broadly consistent in large-scale studies of inter-regional or inter-hemispheric spread in temperate regions, revealing intense epidemics associated with multiple viral introductions, followed by deep troughs driven by seasonal bottlenecks. However, aspects of the global transmission dynamics of influenza viruses are still debated, especially with respect to the existence of tropical source populations experiencing high levels of genetic diversity and the extent of prolonged viral persistence between epidemics. At the scale of a country or community, epidemiological studies have revealed spatially structured diffusion patterns in seasonal and pandemic outbreaks, which were not identified in molecular studies. We discuss the role of sampling issues in generating these conflicting results, and suggest strategies for future research that may help to fully integrate the epidemiological and evolutionary dynamics of influenza virus over space and time.     

水禽流感的流行特点及预防控制

禽流感已有100多年的历史,遍布世界许多国家和地区,不仅给养禽业造成了巨大的经济损失,而且已可直接传染给人,威胁人类公共卫生安全。众所周知,流感病毒（AIV）最复杂的生态系统在鸭、鹅等水禽,而水禽不仅是AIV的巨大贮存库和传染源,其本身也对AIV高度易感,可自然感染HPAIV,发病并死亡。随着禽流感威胁的不断升级,水禽流感的研究也不断深入。本文从水禽流感的流行病学和公共卫生学意义,以及在传播过程中的作用等方面作扼要综述,并对水禽流感的预防和控制提出一些建议。     

利用系统进化树对H7N9大数据预测传播模型的评估

活禽贸易和H7N9禽流感病毒传播之间存在关联,应用大数据技术分析活禽交易网络数据,可进行疫情溯源并预测未来传播趋势。从流感研究数据库中获取了截止至2013年分离得到的H7N9毒株的血凝素基因核酸序列,构建系统进化树推断2013年上半年疫情中H7N9在各省及城市间的传播情况,并与大数据推断结果进行对比分析。结果表明,系统进化树推断结果更为准确,但大数据分析能够提供更多地区的信息且具有更好的时效性,同时推断的传播模型准确度较高,在H7N9疫情的应对中具有较高的应用价值。     

Estimating individuals’ genetic and non-genetic effects underlying infectious disease transmission from temporal epidemic data

Individuals differ widely in their contribution to the spread of infection within and across populations. Three key epidemiological host traits affect infectious disease spread: susceptibility (propensity to acquire infection), infectivity (propensity to transmit infection to others) and recoverability (propensity to recover quickly). Interventions aiming to reduce disease spread may target improvement in any one of these traits, but the necessary statistical methods for obtaining risk estimates are lacking. In this paper we introduce a novel software tool called SIRE (standing for “Susceptibility, Infectivity and Recoverability Estimation”), which allows for the first time simultaneous estimation of the genetic effect of a single nucleotide polymorphism (SNP), as well as non-genetic influences on these three unobservable host traits. SIRE implements a flexible Bayesian algorithm which accommodates a wide range of disease surveillance data comprising any combination of recorded individual infection and/or recovery times, or disease diagnostic test results. Different genetic and non-genetic regulations and data scenarios (representing realistic recording schemes) were simulated to validate SIRE and to assess their impact on the precision, accuracy and bias of parameter estimates. This analysis revealed that with few exceptions, SIRE provides unbiased, accurate parameter estimates associated with all three host traits. For most scenarios, SNP effects associated with recoverability can be estimated with highest precision, followed by susceptibility. For infectivity, many epidemics with few individuals give substantially more statistical power to identify SNP effects than the reverse. Importantly, precise estimates of SNP and other effects could be obtained even in the case of incomplete, censored and relatively infrequent measurements of individuals’ infection or survival status, albeit requiring more individuals to yield equivalent precision. SIRE represents a new tool for analysing a wide range of experimental and field disease data with the aim of discovering and validating SNPs and other factors controlling infectious disease transmission.     

Seasonality and the dynamics of infectious diseases

Seasonal variations in temperature, rainfall and resource availability are ubiquitous and can exert strong pressures on population dynamics. Infectious diseases provide some of the best-studied examples of the role of seasonality in shaping population fluctuations. In this paper, we review examples from human and wildlife disease systems to illustrate the challenges inherent in understanding the mechanisms and impacts of seasonal environmental drivers. Empirical evidence points to several biologically distinct mechanisms by which seasonality can impact host–pathogen interactions, including seasonal changes in host social behaviour and contact rates, variation in encounters with infective stages in the environment, annual pulses of host births and deaths and changes in host immune defences. Mathematical models and field observations show that the strength and mechanisms of seasonality can alter the spread and persistence of infectious diseases, and that population-level responses can range from simple annual cycles to more complex multiyear fluctuations. From an applied perspective, understanding the timing and causes of seasonality offers important insights into how parasite–host systems operate, how and when parasite control measures should be applied, and how disease risks will respond to anthropogenic climate change and altered patterns of seasonality. Finally, by focusing on well-studied examples of infectious diseases, we hope to highlight general insights that are relevant to other ecological interactions.     

Relating Phylogenetic Trees to Transmission Trees of Infectious Disease Outbreaks

Transmission events are the fundamental building blocks of the dynamics of any infectious disease. Much about the epidemiology of a disease can be learned when these individual transmission events are known or can be estimated. Such estimations are difficult and generally feasible only when detailed epidemiological data are available. The genealogy estimated from genetic sequences of sampled pathogens is another rich source of information on transmission history. Optimal inference of transmission events calls for the combination of genetic data and epidemiological data into one joint analysis. A key difficulty is that the transmission tree, which describes the transmission events between infected hosts, differs from the phylogenetic tree, which describes the ancestral relationships between pathogens sampled from these hosts. The trees differ both in timing of the internal nodes and in topology. These differences become more pronounced when a higher fraction of infected hosts is sampled. We show how the phylogenetic tree of sampled pathogens is related to the transmission tree of an outbreak of an infectious disease, by the within-host dynamics of pathogens. We provide a statistical framework to infer key epidemiological and mutational parameters by simultaneously estimating the phylogenetic tree and the transmission tree. We test the approach using simulations and illustrate its use on an outbreak of foot-and-mouth disease. The approach unifies existing methods in the emerging field of phylodynamics with transmission tree reconstruction methods that are used in infectious disease epidemiology.     

Why genetic selection to reduce the prevalence of infectious diseases is way more promising than currently believed

Genetic selection for improved disease resistance is an important part of strategies to combat infectious diseases in agriculture. Quantitative genetic analyses of binary disease status, however, indicate low heritability for most diseases, which restricts the rate of genetic reduction in disease prevalence. Moreover, the common liability threshold model suggests that eradication of an infectious disease via genetic selection is impossible because the observed-scale heritability goes to zero when the prevalence approaches zero. From infectious disease epidemiology, however, we know that eradication of infectious diseases is possible, both in theory and practice, because of positive feedback mechanisms leading to the phenomenon known as herd immunity. The common quantitative genetic models, however, ignore these feedback mechanisms. Here, we integrate quantitative genetic analysis of binary disease status with epidemiological models of transmission, aiming to identify the potential response to selection for reducing the prevalence of endemic infectious diseases. The results show that typical heritability values of binary disease status correspond to a very substantial genetic variation in disease susceptibility among individuals. Moreover, our results show that eradication of infectious diseases by genetic selection is possible in principle. These findings strongly disagree with predictions based on common quantitative genetic models, which ignore the positive feedback effects that occur when reducing the transmission of infectious diseases. Those feedback effects are a specific kind of Indirect Genetic Effects; they contribute substantially to the response to selection and the development of herd immunity (i.e., an effective reproduction ratio less than one).     

Accuracy of estimated phylogenetic trees from molecular data

Summary The accuracies and efficiencies of four different methods for constructing phylogenetic trees from molecular data were examined by using computer simulation. The methods examined are UPGMA, Fitch and Margoliash's (1967) (F/M) method, Farris' (1972) method, and the modified Farris method (Tateno, Nei, and Tajima, this paper). In the computer simulation, eight OTUs (32 OTUs in one case) were assumed to evolve according to a given model tree, and the evolutionary change of a sequence of 300 nucleotides was followed. The nucleotide substitution in this sequence was assumed to occur following the Poisson distribution, negative binomial distribution or a model of temporally varying rate. Estimates of nucleotide substitutions (genetic distances) were then computed for all pairs of the nucleotide sequences that were generated at the end of the evolution considered, and from these estimates a phylogenetic tree was reconstructed and compared with the true model tree. The results of this comparison indicate that when the coefficient of variation of branch length is large the Farris and modified Farris methods tend to be better than UPGMA and the F/M method for obtaining a good topology. For estimating the number of nucleotide substitutions for each branch of the tree, however, the modified Farris method shows a better performance than the Farris method. When the coefficient of variation of branch length is small, however, UPGMA shows the best performance among the four methods examined. Nevertheless, any tree-making method is likely to make errors in obtaining the correct topology with a high probability, unless all branch lengths of the true tree are sufficiently long. It is also shown that the agreement between patristic and observed genetic distances is not a good indicator of the goodness of the tree obtained.     

2005～2006年湖南省人感染高致病性禽流感(H5N1)实验室诊断及病毒基因特性研究

为了对2005~2006年湖南省2例不明原因肺炎病例进行实验室诊断,确定病因以及对其进行病原学研究,采集病例呼吸道标本和血清标本,对呼吸道标本采用实时荧光定量逆转录聚合酶链式反应(Real-time RT-PCR)和逆转录聚合酶链式反应(RT-PCR)方法检测H5亚型禽流感病毒核酸,对血清标本采用血凝抑制试验检测特异性抗体,并对其中1例死亡病例(病例2)的肺穿刺物标本进行病毒分离,所获毒株予以测序及同源性分析。结果显示,2例病例H5亚型禽流感病毒核酸检测均为阳性,病例1恢复期血清H5N1特异性抗体阳性,并且较急性期血清呈4倍以上增长;病例2急性期血清特异性抗体阴性,2例均为人感染高致病性禽流感病毒(H5N1)确诊病例。从病例2分离得到毒株A/Hunan/1/2006,测序及分子特性分析表明,其8个基因片段均为禽源,且与湖南本地禽类分离的病毒相似,并未与人流感病毒发生基因重组或产生显著变异。