Characterizing superspreading potential of infectious disease: Decomposition of individual transmissibility

In the context of infectious disease transmission, high heterogeneity in individual infectiousness indicates that a few index cases can generate large numbers of secondary cases, a phenomenon commonly known as superspreading. The potential of disease superspreading can be characterized by describing the distribution of secondary cases (of each seed case) as a negative binomial (NB) distribution with the dispersion parameter, k. Based on the feature of NB distribution, there must be a proportion of individuals with individual reproduction number of almost 0, which appears restricted and unrealistic. To overcome this limitation, we generalized the compound structure of a Poisson rate and included an additional parameter, and divided the reproduction number into independent and additive fixed and variable components. Then, the secondary cases followed a Delaporte distribution. We demonstrated that the Delaporte distribution was important for understanding the characteristics of disease transmission, which generated new insights distinct from the NB model. By using real-world dataset, the Delaporte distribution provides improvements in describing the distributions of COVID-19 and SARS cases compared to the NB distribution. The model selection yielded increasing statistical power with larger sample sizes as well as conservative type I error in detecting the improvement in fitting with the likelihood ratio (LR) test. Numerical simulation revealed that the control strategy-making process may benefit from monitoring the transmission characteristics under the Delaporte framework. Our findings highlighted that for the COVID-19 pandemic, population-wide interventions may control disease transmission on a general scale before recommending the high-risk-specific control strategies.     

Stochastic two-group models with transmission dependent on host infectivity or susceptibility

ABSTRACT

Stochastic epidemic models with two groups are formulated and applied to emerging and re-emerging infectious diseases. In recent emerging diseases, disease spread has been attributed to superspreaders, highly infectious individuals that infect a large number of susceptible individuals. In some re-emerging infectious diseases, disease spread is attributed to waning immunity in susceptible hosts. We apply a continuous-time Markov chain (CTMC) model to study disease emergence or re-emergence from different groups, where the transmission rates depend on either the infectious host or the susceptible host. Multitype branching processes approximate the dynamics of the CTMC model near the disease-free equilibrium and are used to estimate the probability of a minor or a major epidemic. It is shown that the probability of a major epidemic is greater if initiated by an individual from the superspreader group or by an individual from the highly susceptible group. The models are applied to Severe Acute Respiratory Syndrome and measles.     

Different forms of superspreading lead to different outcomes: Heterogeneity in infectiousness and contact behavior relevant for the case of SARS-CoV-2

Superspreading events play an important role in the spread of several pathogens, such as SARS-CoV-2. While the basic reproduction number of the original Wuhan SARS-CoV-2 is estimated to be about 3 for Belgium, there is substantial inter-individual variation in the number of secondary cases each infected individual causes—with most infectious individuals generating no or only a few secondary cases, while about 20% of infectious individuals is responsible for 80% of new infections. Multiple factors contribute to the occurrence of superspreading events: heterogeneity in infectiousness, individual variations in susceptibility, differences in contact behavior, and the environment in which transmission takes place. While superspreading has been included in several infectious disease transmission models, research into the effects of different forms of superspreading on the spread of pathogens remains limited. To disentangle the effects of infectiousness-related heterogeneity on the one hand and contact-related heterogeneity on the other, we implemented both forms of superspreading in an individual-based model describing the transmission and spread of SARS-CoV-2 in a synthetic Belgian population. We considered its impact on viral spread as well as on epidemic resurgence after a period of social distancing. We found that the effects of superspreading driven by heterogeneity in infectiousness are different from the effects of superspreading driven by heterogeneity in contact behavior. On the one hand, a higher level of infectiousness-related heterogeneity results in a lower risk of an outbreak persisting following the introduction of one infected individual into the population. Outbreaks that did persist led to fewer total cases and were slower, with a lower peak which occurred at a later point in time, and a lower herd immunity threshold. Finally, the risk of resurgence of an outbreak following a period of lockdown decreased. On the other hand, when contact-related heterogeneity was high, this also led to fewer cases in total during persistent outbreaks, but caused outbreaks to be more explosive in regard to other aspects (such as higher peaks which occurred earlier, and a higher herd immunity threshold). Finally, the risk of resurgence of an outbreak following a period of lockdown increased. We found that these effects were conserved when testing combinations of infectiousness-related and contact-related heterogeneity.     

Policy Resistance Undermines Superspreader Vaccination Strategies for Influenza

Theoretical models of infection spread on networks predict that targeting vaccination at individuals with a very large number of contacts (superspreaders) can reduce infection incidence by a significant margin. These models generally assume that superspreaders will always agree to be vaccinated. Hence, they cannot capture unintended consequences such as policy resistance, where the behavioral response induced by a new vaccine policy tends to reduce the expected benefits of the policy. Here, we couple a model of influenza transmission on an empirically-based contact network with a psychologically structured model of influenza vaccinating behavior, where individual vaccinating decisions depend on social learning and past experiences of perceived infections, vaccine complications and vaccine failures. We find that policy resistance almost completely undermines the effectiveness of superspreader strategies: the most commonly explored approaches that target a randomly chosen neighbor of an individual, or that preferentially choose neighbors with many contacts, provide at best a relative improvement over their non-targeted counterpart as compared to when behavioral feedbacks are ignored. Increased vaccine coverage in super spreaders is offset by decreased coverage in non-superspreaders, and superspreaders also have a higher rate of perceived vaccine failures on account of being infected more often. Including incentives for vaccination provides modest improvements in outcomes. We conclude that the design of influenza vaccine strategies involving widespread incentive use and/or targeting of superspreaders should account for policy resistance, and mitigate it whenever possible.     

Connectivity,reproduction number,and mobility interact to determine communities’ epidemiological superspreader potential in a metapopulation network

Disease epidemic outbreaks on human metapopulation networks are often driven by a small number of superspreader nodes, which are primarily responsible for spreading the disease throughout the network. Superspreader nodes typically are characterized either by their locations within the network, by their degree of connectivity and centrality, or by their habitat suitability for the disease, described by their reproduction number (R). Here we introduce a model that considers simultaneously the effects of network properties and R on superspreaders, as opposed to previous research which considered each factor separately. This type of model is applicable to diseases for which habitat suitability varies by climate or land cover, and for direct transmitted diseases for which population density and mitigation practices influences R. We present analytical models that quantify the superspreader capacity of a population node by two measures: probability-dependent superspreader capacity, the expected number of neighboring nodes to which the node in consideration will randomly spread the disease per epidemic generation, and time-dependent superspreader capacity, the rate at which the node spreads the disease to each of its neighbors. We validate our analytical models with a Monte Carlo analysis of repeated stochastic Susceptible-Infected-Recovered (SIR) simulations on randomly generated human population networks, and we use a random forest statistical model to relate superspreader risk to connectivity, R, centrality, clustering, and diffusion. We demonstrate that either degree of connectivity or R above a certain threshold are sufficient conditions for a node to have a moderate superspreader risk factor, but both are necessary for a node to have a high-risk factor. The statistical model presented in this article can be used to predict the location of superspreader events in future epidemics, and to predict the effectiveness of mitigation strategies that seek to reduce the value of R, alter host movements, or both.     

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.     

Using Hawkes Processes to model imported and local malaria cases in near-elimination settings

Developing new methods for modelling infectious diseases outbreaks is important for monitoring transmission and developing policy. In this paper we propose using semi-mechanistic Hawkes Processes for modelling malaria transmission in near-elimination settings. Hawkes Processes are well founded mathematical methods that enable us to combine the benefits of both statistical and mechanistic models to recreate and forecast disease transmission beyond just malaria outbreak scenarios. These methods have been successfully used in numerous applications such as social media and earthquake modelling, but are not yet widespread in epidemiology. By using domain-specific knowledge, we can both recreate transmission curves for malaria in China and Eswatini and disentangle the proportion of cases which are imported from those that are community based.     

树木病虫害缺陷精准检测方法的综述和展望

我国疆土辽阔,有着丰富的森林资源。近年来,随着外来昆虫的入侵,全国树木病虫害的暴发率逐年上升,对我国生态经济造成了巨大的影响,但是树木病虫害的监测只能在局部地区,因而只能在已经暴发了较严重的病虫害后才能够发现和治理。树木病虫害如在早期未被发现和处理,可能会造成暴发的严重形势,且预防和治理将变得被动,因此,进行树木内部病虫害缺陷精准检测尤为重要。为更早和更加准确预报树木病虫害的发生,需要进行定期的检测和监控。针对早期传统方法如目测法、敲击辩声法和解剖观测法等方法的准确性低、时效性差和易对树木造成不可逆损害等缺点,近些年研究出了新的检测方法,包括指向性好、能量大的超声波检测法,低成本、穿透性强的应力波检测法,以及成像准确、适用于不同环境的电磁波检测法等。不同的检测方法分别有着相对应的成像算法,如走时法、频域分析法和Born近似法等。这些成像算法在分辨率、准确度、精度和计算速度上各有优势。通过对这3种检测方法的发展现状进行总结,了解各类检测方法的优缺点,有助于更好地针对不同情况下的树木病虫害做出精准检测,能够把树木病虫害暴发后的被动治理变成暴发前的主动预防,以提早做出应对方案。     

The effect of superspreading on epidemic outbreak size distributions

Recently, evidence has been presented to suggest that there are significant heterogeneities in the transmission of communicable diseases. Here, a stochastic simulation model of an epidemic process that allows for these heterogeneities is used to demonstrate the potentially considerable effect that heterogeneity of transmission will have on epidemic outbreak size distributions. Our simulation results agree well with approximations gained from the theory of branching processes. Outbreak size distributions have previously been used to infer basic epidemiological parameters. We show that if superspreading does occur then such distributions must be interpreted with care. The simulation results are discussed in relation to measles epidemics in isolated populations and in predominantly urban scenarios. The effect of three different disease control policies on outbreak size distributions are shown for varying levels of heterogeneity and disease control effort.     

The COVID-19 outbreak in Sichuan,China: Epidemiology and impact of interventions

In January 2020, a COVID-19 outbreak was detected in Sichuan Province of China. Six weeks later, the outbreak was successfully contained. The aim of this work is to characterize the epidemiology of the Sichuan outbreak and estimate the impact of interventions in limiting SARS-CoV-2 transmission. We analyzed patient records for all laboratory-confirmed cases reported in the province for the period of January 21 to March 16, 2020. To estimate the basic and daily reproduction numbers, we used a Bayesian framework. In addition, we estimated the number of cases averted by the implemented control strategies. The outbreak resulted in 539 confirmed cases, lasted less than two months, and no further local transmission was detected after February 27. The median age of local cases was 8 years older than that of imported cases. We estimated R₀ at 2.4 (95% CI: 1.6–3.7). The epidemic was self-sustained for about 3 weeks before going below the epidemic threshold 3 days after the declaration of a public health emergency by Sichuan authorities. Our findings indicate that, were the control measures be adopted four weeks later, the epidemic could have lasted 49 days longer (95% CI: 31–68 days), causing 9,216 more cases (95% CI: 1,317–25,545).     

Modeling the onset of symptoms of COVID-19: Effects of SARS-CoV-2 variant

Identifying order of symptom onset of infectious diseases might aid in differentiating symptomatic infections earlier in a population thereby enabling non-pharmaceutical interventions and reducing disease spread. Previously, we developed a mathematical model predicting the order of symptoms based on data from the initial outbreak of SARS-CoV-2 in China using symptom occurrence at diagnosis and found that the order of COVID-19 symptoms differed from that of other infectious diseases including influenza. Whether this order of COVID-19 symptoms holds in the USA under changing conditions is unclear. Here, we use modeling to predict the order of symptoms using data from both the initial outbreaks in China and in the USA. Whereas patients in China were more likely to have fever before cough and then nausea/vomiting before diarrhea, patients in the USA were more likely to have cough before fever and then diarrhea before nausea/vomiting. Given that the D614G SARS-CoV-2 variant that rapidly spread from Europe to predominate in the USA during the first wave of the outbreak was not present in the initial China outbreak, we hypothesized that this mutation might affect symptom order. Supporting this notion, we found that as SARS-CoV-2 in Japan shifted from the original Wuhan reference strain to the D614G variant, symptom order shifted to the USA pattern. Google Trends analyses supported these findings, while weather, age, and comorbidities did not affect our model’s predictions of symptom order. These findings indicate that symptom order can change with mutation in viral disease and raise the possibility that D614G variant is more transmissible because infected people are more likely to cough in public before being incapacitated with fever.     

生态位模型在流行病学中的应用

随着新冠肺炎(COVID-19)疫情在全球逐渐开始蔓延, 对其传播范围以及强度的风险评估工作越来越受到人们的重视。作为生态学和生物地理学中常用的研究手段, 生态位模型也被应用到该项工作中来。虽然预测流行病的传播热点和趋势是生态位模型的应用方向之一, 但由于新冠病毒(SARS-CoV-2)自身特点, 生态位模型并非预测其潜在传播范围的有力工具。本文回顾了近些年来生态位模型在各种流行病学研究中的应用, 比较了疫病传播中常用生态位建模方法的优势与不足, 分析了适用生态位建模的疫病案例以及不适用于生态位建模的疫病特点, 明确指出, 生态位模型只能用于分析流行病在传播过程中受自然环境干扰的部分, 如中间宿主的潜在分布等。而对于包括COVID-19在内的主要通过人传人的流行病, 生态位模型尚无有效的手段进行预测。尽管生态位模型可用于分析流行病的传播范围, 但在使用时需要根据疾病特点有针对性地选择合适的建模方法与建模对象。为了量化疫病传播风险, 还需要考虑其他干扰因素, 以便准确测试和评估生态位模型。若不加选择地滥用生态位模型的工具, 反而会误导决策者的判断。总之, 在应用生态位模型进行研究工作, 特别是预测流行病的传播范围时, 首先要考虑建模对象是否满足生态学假设。     

The dynamics of infectious diseases in orchards with roguing and replanting as control strategy

 Roguing and replanting is a widely adopted control strategy of infectious diseases in orchards. Little is known about the effect of this type of management on the dynamics of the infectious disease. In this paper we analyze a structured population model for the dynamics of an S-I-R type epidemic under roguing and replanting management. The model is structured with respect to the total number of infections and the number of post-infectious infections on a tree. Trees are assumed to be rogued, and replaced by uninfected trees, when the total number of infections on the tree reaches a threshold value. Stability analysis and numerical exploration of the model show that for specific parameter combinations the internal equilibrium can become unstable and large amplitude periodic fluctuations arise. Several hypothesis on the mechanism causing the destabilisation of the steady-state are considered. The mechanism leading to the large amplitude fluctuations is identified and biologically interpreted. Received 2 September 1994     

Age‐specific infectious period shapes dynamics of pneumonia in bighorn sheep

Superspreading, the phenomenon where a small proportion of individuals contribute disproportionately to new infections, has profound effects on disease dynamics. Superspreading can arise through variation in contacts, infectiousness or infectious periods. The latter has received little attention, yet it drives the dynamics of many diseases of critical public health, livestock health and conservation concern. Here, we present rare evidence of variation in infectious periods underlying a superspreading phenomenon in a free‐ranging wildlife system. We detected persistent infections of Mycoplasma ovipneumoniae, the primary causative agent of pneumonia in bighorn sheep (Ovis canadensis), in a small number of older individuals that were homozygous at an immunologically relevant genetic locus. Interactions among age‐structure, genetic composition and infectious periods may drive feedbacks in disease dynamics that determine the magnitude of population response to infection. Accordingly, variation in initial conditions may explain divergent population responses to infection that range from recovery to catastrophic decline and extirpation.     

Two-Step Source Tracing Strategy of Yersinia pestis and Its Historical Epidemiology in a Specific Region

Source tracing of pathogens is critical for the control and prevention of infectious diseases. Genome sequencing by high throughput technologies is currently feasible and popular, leading to the burst of deciphered bacterial genome sequences. Utilizing the flooding genomic data for source tracing of pathogens in outbreaks is promising, and challenging as well. Here, we employed Yersinia pestis genomes from a plague outbreak at Xinghai county of China in 2009 as an example, to develop a simple two-step strategy for rapid source tracing of the outbreak. The first step was to define the phylogenetic position of the outbreak strains in a whole species tree, and the next step was to provide a detailed relationship across the outbreak strains and their suspected relatives. Through this strategy, we observed that the Xinghai plague outbreak was caused by Y. pestis that circulated in the local plague focus, where the majority of historical plague epidemics in the Qinghai-Tibet Plateau may originate from. The analytical strategy developed here will be of great help in fighting against the outbreaks of emerging infectious diseases, by pinpointing the source of pathogens rapidly with genomic epidemiological data and microbial forensics information.     

Cytokine storm in COVID-19: from viral infection to immune responses,diagnosis and therapy

The COVID-19 outbreak is emerging as a significant public health challenge. Excessive production of proinflammatory cytokines, also known as cytokine storm, is a severe clinical syndrome known to develop as a complication of infectious or inflammatory diseases. Clinical evidence suggests that the occurrence of cytokine storm in severe acute respiratory syndrome secondary to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is closely associated with the rapid deterioration and high mortality of severe cases. In this review, we aim to summarize the mechanism of SARS-CoV-2 infection and the subsequent immunological events related to excessive cytokine production and inflammatory responses associated with ACE2-AngII signaling. An overview of the diagnosis and an update on current therapeutic regimens and vaccinations is also provided.     

树鼩独立换气笼具的设计

目的设计研究一种满足于树鼩感染性疾病动物模型实验生物安全要求的独立换气专用隔离笼具。方法根据树驹的生物学特性、实验生物安全要求及有关实验动物笼具标准进行设计。结果该笼舍完全适用于感染性疾病实验树鼢的饲养和实验操作。结论该笼具能达到维护实验动物福利,保证实验动物质量,保障人身健康,保护环境的要求,对于使用树鼩开展人类重大传染病研究具有广泛的应用价值和市场前景。     

Epidemiological,Clinical and Serological Characteristics of Children with Coronavirus Disease 2019 in Wuhan: A Single-centered,Retrospective Study

In December 2019, SARS-CoV-2 was first detected in the samples obtained from three adult patients who suffered from an unknown viral pneumonia in Wuhan (Li et al. 2020). This unknown viral pneumonia is further named as coronavirus disease 2019 (COVID-19) by the World Health Organization. To date, the number of new COVID-19 cases has continued to skyrocket and the impact of SARS-CoV-2 on humans is far greater than any pathogen of this century in both breadth and depth. Previous studies have shown that adults with COVID-19 have symptoms of fever, dry cough, dyspnea, fatigue and lymphocytopenia. Moreover, COVID-19 is more likely to cause death in the elderly, especially those with chronic comorbidities (Huang et al. 2020). In Wuhan, more than 50, 000 COVID-19 cases have been confirmed, including over 780 pediatric patients, and only one child death case (Lu et al. 2020). Although the number of children cases was far fewer than that of adults, COVID-19 might endanger children's health and the information on children remains limited, especially in serological study. In the retrospective study, the investigators analyzed the epidemiological, clinical and serological characteristics of children with COVID-19 in Wuhan in the early stages of the outbreak, which might provide theoretical and practical help in controlling COVID-19 and similar emerging infectious diseases in the future.     

An analytical study of the factors that influence COVID-19 spread

The outbreak of the coronavirus disease 2019 (COVID-19) continues to constitute an international public health emergency. Seasonality is a long-recognized attribute of many viral infections of humans. Nevertheless, the relationship between environmental factors and the spread of infection, particularly for person-to-person communicable diseases, remains poorly understood. This study explores the relationship between environmental factors and the incidence of COVID-19 in 188 countries with reported COVID-19 cases as of April 13, 2020. Here we show that COVID-19 growth rates peaked in temperate zones in the Northern Hemisphere during the outbreak period, while they were lower in tropical zones. The relationships between COVID-19 and environmental factors were resistant to the potentially confounding effects of air pollution, sea level, and population. To prove the effect of those factors, study, and analysis of the prevalence of COVID-19 in Italy, Spain, and China was undertaken. A fuzzy logic system was designed to predict the effects of that variables on the rate of viral spread of COVID-19.     

抗新型冠状病毒中和抗体药物临床研究进展北大核心CSCD

自1998年预防呼吸合胞病毒的帕利珠单抗药物上市以来,多种靶向病毒的治疗性抗体药物已成功用于感染性疾病的临床治疗。新型冠状病毒肺炎疫情暴发后,多种中和抗体药物快速进入临床研究阶段,展现出积极的治疗及预防效果,并以紧急使用授权的方式用于疫情防控。本文对抗新型冠状病毒中和抗体药物的临床进展和主要临床试验结果进行总结,以期为包括新型冠状病毒肺炎在内的新发、突发传染病中和抗体药物研发提供参考。