Epidemic patch models applied to pandemic influenza: Contact matrix, stochasticity, robustness of predictions

Due to the recent emergence of H5N1 virus, the modelling of pandemic influenza has become a relevant issue. Here we present an SEIR model formulated to simulate a possible outbreak in Italy, analysing its structure and, more generally, the effect of including specific details into a model. These details regard population heterogeneities, such as age and spatial distribution, as well as stochasticity, that regulates the epidemic dynamics when the number of infectives is low. We discuss and motivate the specific modelling choices made when building the model and investigate how the model details influence the predicted dynamics. Our analysis may help in deciding which elements of complexity are worth including in the design of a deterministic model for pandemic influenza, in a balance between, on the one hand, keeping the model computationally efficient and the number of parameters low and, on the other hand, maintaining the necessary realistic features.     

Optimal control for pandemic influenza: The role of limited antiviral treatment and isolation

The implementation of optimal control strategies involving antiviral treatment and/or isolation measures can reduce significantly the number of clinical cases of influenza. Pandemic-level control measures must be carefully assessed specially in resource-limited situations. A model for the transmission dynamics of influenza is used to evaluate the impact of isolation and/or antiviral drug delivery measures during an influenza pandemic. Five pre-selected control strategies involving antiviral treatment and isolation are tested under the “unlimited” resource assumption followed by an exploration of the impact of these “optimal” policies when resources are limited in the context of a 1918-type influenza pandemic scenario. The implementation of antiviral treatment at the start of a pandemic tends to reduce the magnitude of epidemic peaks, spreading the maximal impact of an outbreak over an extended window in time. Hence, the controls’ timing and intensity can reduce the pressures placed on the health care infrastructure by a pandemic reducing the stress put on the system during epidemic peaks. The role of isolation strategies is highlighted in this study particularly when access to antiviral resources is limited.     

Emergence of drug resistance: implications for antiviral control of pandemic influenza

Given the danger of an unprecedented spread of the highly pathogenic avian influenza strain H5N1 in humans, and great challenges to the development of an effective influenza vaccine, antiviral drugs will probably play a pivotal role in combating a novel pandemic strain. A critical limitation to the use of these drugs is the evolution of highly transmissible drug-resistant viral mutants. Here, we develop a mathematical model to evaluate the potential impact of an antiviral treatment strategy on the emergence of drug resistance and containment of a pandemic. The results show that elimination of the wild-type strain depends crucially on both the early onset of treatment in indexed cases and population-level treatment. Given the probable delay of 0.5-1 day in seeking healthcare and therefore initiating therapy, the findings indicate that a single strategy of antiviral treatment will be unsuccessful at controlling the spread of disease if the reproduction number of the wild-type strain (R0s) exceeds 1.4. We demonstrate the possible occurrence of a self-sustaining epidemic of resistant strain, in terms of its transmission fitness relative to the wild-type, and the reproduction number R0s. Considering reproduction numbers estimated for the past three pandemics, the findings suggest that an uncontrollable pandemic is likely to occur if resistant viruses with relative transmission fitness above 0.4 emerge. While an antiviral strategy is crucial for containing a pandemic, its effectiveness depends critically on timely and strategic use of drugs.     

The role of health care workers and antiviral drugs in the control of pandemic influenza

Until a vaccine against the new strain becomes available, the response to newly emerged pandemic influenza will consist of the use of antiviral drugs and measures that limit exposure to infectious individuals. These first-line defence measures include isolating cases upon diagnosis, reducing close contacts, the use of personal protective equipment and hygiene, and using antiviral drugs for treatment and prophylaxis. There are significant 'costs' associated with control measures, so to justify such interventions it is important to assess their potential to reduce transmission. In this paper, we determine the effect that a number of different antiviral interventions have on the reproduction number of infectives and the probability that an imported infection fades out, and determine parameter scenarios for which these interventions are able to eliminate an emerging pandemic of influenza. We also assess the role that health care workers play in transmission and the extent to which providing them with antiviral prophylaxis and personal protective equipment modifies this role. Our results indicate that this class requires protection to avoid a greatly disproportionate contribution to early infective numbers, and for the maintenance of a stable health care system. Further, we show that the role children play in increasing transmission is moderate, in spite of closer mixing with other children.     

Optimal control strategy for prevention of avian influenza pandemic

The spread of H5N1 virus to Europe and continued human infection in Southeast Asia have heightened pandemic concern. Although, fortunately, sustained human-to-human transmissions have not been reported yet, it is said that a pandemic virus which can be easily transmitted among humans certainly emerges in the future. In this study, we extended the previous studies for the prevention of the pandemic influenza to evaluate the time-dependent optimal prevention policies, which are associated with elimination policy and quarantine policy, considering its execution cost. Actually, the execution cost affects the optimal strategy of prevention policies and the prevention of the disease spread. We found that the quarantine policy is very important rather than the elimination policy during the disease spread, even if the unit execution cost of the quarantine policy is more expensive than that of the elimination policy. And also, the change of the unit execution cost does affect the total cumulative cost of the optimal prevention policies but does not affect the relative frequency of each cumulative execution cost. Furthermore, interestingly, we revealed that an optimal strategy to reduce the number of total infected humans might increase a chance of invadability of the mutant influenza.     

Neuraminidase inhibitors for treatment of human and avian strain influenza: A comparative modeling study

Treatment of seasonal influenza viral infections using antivirals such as neuraminidase inhibitors (NAIs) has been proven effective if administered within 48 h post-infection. However, there is growing evidence that antiviral treatment of infections with avian-derived strains even as late as 6 days post-infection (dpi) can significantly reduce infection severity and duration. Using a mathematical model of in-host influenza viral infections which can capture the kinetics of both a short-lived, typical, seasonal infection and a severe infection exhibiting sustained viral titer, we explore differences in the effects of NAI treatment on both types of influenza viral infections. Comparison of our model's behavior against experimental data from patients naturally infected with avian strains yields estimates for the times at which patients were infected that are consistent with those reported by the patients, and estimates of drug efficacies that are lower for patients who died than for those who recovered. In addition, our model suggests that the sustained, high, viral titers often seen in more severe influenza virus infections are the reason why antiviral treatment delayed by as much as 6 dpi will still lead to reduced viral titers and shortened illness. We conclude that NAIs may be an effective and beneficial treatment strategy against more severe strains of influenza virus characterized by high, sustained, viral titers. We believe that our mathematical model will be an effective tool in guiding treatment of severe influenza viral infections with antivirals.     

A susceptible-infected model of early detection of respiratory infection outbreaks on a background of influenza

The threat of biological warfare and the emergence of new infectious agents spreading at a global scale have highlighted the need for major enhancements to the public health infrastructure. Early detection of epidemics of infectious diseases requires both real-time data and real-time interpretation of data. Despite moderate advancements in data acquisition, the state of the practice for real-time analysis of data remains inadequate. We present a nonlinear mathematical framework for modeling the transient dynamics of influenza, applied to historical data sets of patients with influenza-like illness. We estimate the vital time-varying epidemiological parameters of infections from historical data, representing normal epidemiological trends. We then introduce simulated outbreaks of different shapes and magnitudes into the historical data, and estimate the parameters representing the infection rates of anomalous deviations from normal trends. Finally, a dynamic threshold-based detection algorithm is devised to assess the timeliness and sensitivity of detecting the irregularities in the data, under a fixed low false-positive rate. We find that the detection algorithm can identify such designated abnormalities in the data with high sensitivity with specificity held at 97%, but more importantly, early during an outbreak. The proposed methodology can be applied to a broad range of influenza-like infectious diseases, whether naturally occurring or a result of bioterrorism, and thus can be an integral component of a real-time surveillance system.     

A dynamic model for tuberculosis transmission and optimal treatment strategies in South Korea

We have developed a dynamic model for tuberculosis (TB) transmission in South Korea using a SEIR model with the time-dependent parameters. South Korea ranked the highest TB incidence among members of the Organization for Economic Cooperation and Development (OECD) in 2005 yr. The observed data from the Korea Center for Disease Control and Prevention (KCDC) shows a certain rise of active-TB incidence individuals after 2001 yr. Because of this sudden jump, we have considered two different periods for best fitting the model: prior to 2001 yr and posterior to 2001 yr. The least-squares fitting has been used for estimating model parameters to the observed data of active-TB incidence. Our model agrees well with the observed data. In this work, we also propose optimal treatment strategies of TB model in South Korea for the future. We have considered three control mechanisms representing distancing, case finding and case holding efforts. Optimal control programs have been proposed in various scenarios, in order to minimize the number of exposed and infectious individuals and the cost of implementing the control treatment.     

Source-sink dynamics shape the evolution of antibiotic resistance and its pleiotropic fitness cost

Understanding the conditions that favour the evolution and maintenance of antibiotic resistance is the central goal of epidemiology. A crucial feature explaining the adaptation to harsh, or 'sink', environments is the supply of beneficial mutations via migration from a 'source' population. Given that antibiotic resistance is frequently associated with antagonistic pleiotropic fitness costs, increased migration rate is predicted not only to increase the rate of resistance evolution but also to increase the probability of fixation of resistance mutations with minimal fitness costs. Here we report in vitro experiments using the nosocomial pathogenic bacterium Pseudomonas aeruginosa that support these predictions: increasing rate of migration into environments containing antibiotics increased the rate of resistance evolution and decreased the associated costs of resistance. Consistent with previous theoretical work, we found that resistance evolution arose more rapidly in the presence of a single antibiotic than two. Evolution of resistance was also more rapid when bacteria were subjected to sequential exposure with two antibiotics (cycling therapy) compared with simultaneous exposure (bi-therapy). Furthermore, pleiotropic fitness costs of resistance to two antibiotics were higher than for one antibiotic, and were also higher under bi-therapy than cycling therapy, although the cost of resistance depended on the order of the antibiotics through time. These results may be relevant to the clinical setting where immigration is known to be important between chemotherapeutically treated patients, and demonstrate the importance of ecological and evolutionary dynamics in the control of antibiotic resistance.     

Genetics and evolution of resistance to insecticides

The evolution of resistance to insecticides has become a serious problem world-wide. It is important to identify patterns of insecticide use whereby insecticides can be used in integrated pest management programmes to help control insect numbers, but in such a manner that the evolution of resistance to insecticides will be retarded. The principal mechanisms of insecticide action and of resistance to these are reviewed. Some generalizations that can be made about the evolution of resistance are examined. In general, to control resistance it appears better to use an intense dose of non-persistent pesticides over a circumscribed area. Some features of the problem where population genetics and evolutionary theory might contribute to controlling resistance are discussed.     

The onion model, a simple neutral model for the evolution of diversity in bacterial biofilms

Bacterial biofilms are particularly resistant to a wide variety of antimicrobial compounds. Their persistence in the face of antibiotic therapies causes significant problems in the treatment of infectious diseases. Seldom have evolutionary processes like genetic drift and mutation been invoked to explain how resistance to antibiotics emerges in biofilms, and we lack a simple and tractable model for the genetic and phenotypic diversification that occurs in bacterial biofilms. Here, we introduce the 'onion model', a simple neutral evolutionary model for phenotypic diversification in biofilms. We explore its properties and show that the model produces patterns of diversity that are qualitatively similar to observed patterns of phenotypic diversity in biofilms. We suggest that models like our onion model, which explicitly invoke evolutionary process, are key to understanding biofilm resistance to bactericidal and bacteriostatic agents. Elevated phenotypic variance provides an insurance effect that increases the likelihood that some proportion of the population will be resistant to imposed selective agents and may thus enhance persistence of the biofilm. Accounting for evolutionary change in biofilms will improve our ability to understand and counter diseases that are caused by biofilm persistence.     

Impact of myxomatosis in relation to local persistence in wild rabbit populations: the role of waning immunity and the reproductive period

Many diseases are less severe when they are contracted in early life. For highly lethal diseases, such as myxomatosis in rabbits, getting infected early in life can represent the best chance for an individual to survive the disease. For myxomatosis, early infections are attenuated by maternal antibodies. This may lead to the immunisation of the host, preventing the subsequent development of the lethal form of the disease. But early infection of young individuals requires specific demographic and epidemiological contexts, such as a high transmission rate of the pathogen agent. To investigate other factors involved in the impact of such diseases, we have built a stochastic model of a rabbit metapopulation infected by myxomatosis. We show that the impact of the pathogen agent can be reduced by early infections only when the agent has a long local persistence time and/or when the host subpopulations are highly connected. The length of the reproductive period and the duration of acquired immunity are also important factors influencing the persistence of the pathogen and thus, the impact of the disease. Besides confirming the role of classical factors in the persistence of a pathogen agent, such as the size of the subpopulation or the degree of connectivity, our results highlight novel factors that can modulate the impact of diseases whose severity increase with age.     

Genetic model for longitudinal studies of aging, health, and longevity and its potential application to incomplete data

Many longitudinal studies of aging collect genetic information only for a sub-sample of participants of the study. These data also do not include recent findings, new ideas and methodological concepts developed by distinct groups of researchers. The formal statistical analyses of genetic data ignore this additional information and therefore cannot utilize the entire research potential of the data. In this paper, we present a stochastic model for studying such longitudinal data in joint analyses of genetic and non-genetic sub-samples. The model incorporates several major concepts of aging known to date and usually studied independently. These include age-specific physiological norms, allostasis and allostatic load, stochasticity, and decline in stress resistance and adaptive capacity with age. The approach allows for studying all these concepts in their mutual connection, even if respective mechanisms are not directly measured in data (which is typical for longitudinal data available to date). The model takes into account dependence of longitudinal indices and hazard rates on genetic markers and permits evaluation of all these characteristics for carriers of different alleles (genotypes) to address questions concerning genetic influence on aging-related characteristics. The method is based on extracting genetic information from the entire sample of longitudinal data consisting of genetic and non-genetic sub-samples. Thus it results in a substantial increase in the accuracy of statistical estimates of genetic parameters compared to methods that use only information from a genetic sub-sample. Such an increase is achieved without collecting additional genetic data. Simulation studies illustrate the increase in the accuracy in different scenarios for datasets structurally similar to the Framingham Heart Study. Possible applications of the model and its further generalizations are discussed.     

斜纹夜蛾抗药性及其防治对策的研究进展

斜纹夜蛾Spodopteralitura是多种作物的重要害虫 ,对有机氯、有机磷、氨基甲酸酯类、拟除虫菊酯类以及Bt制剂等杀虫剂均产生抗药性。本文对斜纹夜蛾抗药性的形成与发展、抗性机理及其防治对策等方面进行了综述     

Extension and verification of the SEIR model on the 2009 influenza A (H1N1) pandemic in Japan

In order to understand the evolution of the 2009 influenza A (H1N1) pandemic within local regions of Japan, we studied the significance of regional migration between these regions. For this purpose, we have employed an extended SEIR model to describe the immigration of infected people and the stochastic variation of the infectious efficiency. We then applied a data assimilation technique in order to study how the agreement of the simulation results with the observed data depends on the presence/absence of immigration and the degree of variation of the infectious efficiency. Reproducibility is evaluated by log-likelihood values. The log-likelihood does not indicate the significance of immigration. Although there are multiple waves in the time course of the number of reported infected individuals, these waves could be explained by the stochastic nature of infectious events.     

杀虫剂抗性: 遗传学、基因组学及应用启示

杀虫剂抗性已成为害虫防治工作需要解决的一个重要问题,也是一种人为的、自然选择的重要的进化现象,开展抗药性的研究不仅为抗性的监测、治理和农药工业的发展提供科学参考,还可以揭示生物进化的一些基本规律。在过去的10年,昆虫对许多化学杀虫剂抗药性的分子基础得到了进一步阐明,已从果蝇Drosophila melanogaster中克隆了杀虫剂的靶标基因,还查明了一些害虫的与抗性相关联的基因突变。最近,随着经注释的昆虫基因组的出现,由复杂多基因酶系如酯酶、细胞色素P450酶及谷胱甘肽S-转移酶介导的抗性的机制有了突破性的进展,有关杀虫剂抗性的进化以及抗性基因的传播模式也逐步得到揭示。基因组技术在揭示昆虫其他可能的抗药性机制以及在发现新的杀虫剂靶标方面将发挥更大的作用。