Meta-dynamical adaptive systems and their applications to a fractal algorithm and a biological model

In this article, one defines two models of adaptive systems: the meta-dynamical adaptive system using the notion of Kalman dynamical systems and the adaptive differential equations using the notion of variable dimension spaces. This concept of variable dimension spaces relates the notion of spaces to the notion of dimensions. First, a computational model of the Douady's Rabbit fractal is obtained by using the meta-dynamical adaptive system concept. Then, we focus on a defense-attack biological model described by our two formalisms

Assessing optimal target populations for influenza vaccination programmes: an evidence synthesis and modelling study.

BACKGROUND: Influenza vaccine policies that maximise health benefit through efficient use of limited resources are needed. Generally, influenza vaccination programmes have targeted individuals 65 y and over and those at risk, according to World Health Organization recommendations. We developed methods to synthesise the multiplicity of surveillance datasets in order to evaluate how changing target populations in the seasonal vaccination programme would affect infection rate and mortality. METHODS AND FINDINGS: Using a contemporary evidence-synthesis approach, we use virological, clinical, epidemiological, and behavioural data to develop an age- and risk-stratified transmission model that reproduces the strain-specific behaviour of influenza over 14 seasons in England and Wales, having accounted for the vaccination uptake over this period. We estimate the reduction in infections and deaths achieved by the historical programme compared with no vaccination, and the reduction had different policies been in place over the period. We find that the current programme has averted 0.39 (95% credible interval 0.34-0.45) infections per dose of vaccine and 1.74 (1.16-3.02) deaths per 1,000 doses. Targeting transmitters by extending the current programme to 5-16-y-old children would increase the efficiency of the total programme, resulting in an overall reduction of 0.70 (0.52-0.81) infections per dose and 1.95 (1.28-3.39) deaths per 1,000 doses. In comparison, choosing the next group most at risk (50-64-y-olds) would prevent only 0.43 (0.35-0.52) infections per dose and 1.77 (1.15-3.14) deaths per 1,000 doses. CONCLUSIONS: This study proposes a framework to integrate influenza surveillance data into transmission models. Application to data from England and Wales confirms the role of children as key infection spreaders. The most efficient use of vaccine to reduce overall influenza morbidity and mortality is thus to target children in addition to older adults. Please see later in the article for the Editors' Summary

Extending the elderly- and risk-group programme of vaccination against seasonal influenza in England and Wales: a cost-effectiveness study.

BACKGROUND: The present study aims to evaluate the cost-effectiveness of extending the pre-2013 influenza immunisation programme for high-risk and elderly individuals to those at low risk of developing complications following infection with seasonal influenza. METHODS: We performed an economic evaluation comparing different extensions of the pre-2013 influenza programme to seven possible age groups of low-risk individuals (aged 2-4 years, 50-64 years, 5-16 years, 2-4 and 50-64 years, 2-16 years, 2-16 and 50-64 years, and 2-64 years). These extensions are evaluated incrementally on four base scenarios (no vaccination, risk group only with coverage as observed between 1995 and 2009, risk group and 65+, and risk group with 75% coverage and 65+). Impact of vaccination is assessed using a transmission model built and parameterised from a previously published study. The study population is all individuals of all ages in England and Wales representing an average total of 52.6 million people over 14 influenza seasons (1995-2009). RESULTS: The influenza programme (risk group and elderly) prior to 2013 is likely to be cost effective (incremental cost effectiveness ratio: 7,475 £/QALY, net benefit: 253 M£ [15-829]). Extension to any one of the low-risk target groups defined earlier is likely to be cost-effective. However, strategies that do not include vaccination of school-aged children are less likely to be cost-effective. The most efficient strategy is extension to the 5-16 year age group while universal vaccination (extension to all low-risk individuals over 2 years) will achieve the highest net benefit. While extension to the 2-16 year age group is likely to be very cost effective, the cost-effectiveness of extensions beyond 2-16 years is very uncertain. Extension to the 5-16 year age group would likely remain cost-effective even without herd immunity effects to other age groups. As our study includes a strong historical component, our results depend on the efficacy of the influenza vaccine remaining at levels similar to the ones achieved in the past over a long-period of time (assumed to vary between 28% and 70% depending of the circulating strains and age groups). CONCLUSIONS: Making use of surveillance data from over a decade in conjunction with a dynamic model, we find that vaccination of children in the United Kingdom is likely to be highly cost-effective, not only for their own benefit but also to reduce the disease burden in the rest of the community

Understanding differences in cervical cancer incidence in Western Europe: comparing Portugal and England.

Background: Cervical cancer incidence has decreased over time in England particularly after the introduction of organized screening. In Portugal, where opportunistic screening has been widely available with only slightly lower coverage than that of the organized programme in England, rates of cervical cancer have been higher than in England. We compared the burden of cervical cancer, risk factors and preventive interventions over time in both countries, to identify elements hindering the further decline in incidence and mortality in Portugal. Methods: We used joinpoint regression to identify significant changes in rate time-trends. We also analyzed individual-level Portuguese data on sexual behaviour and human papillomavirus prevalence, and recent aggregate data on organized and opportunistic screening coverage. We compared published estimates of survival, risk factors and historical screening coverage for both countries. Results: Despite stable incidence, cervical cancer mortality has declined in both countries in the last decade. The burden has been 4 cases and 1 death per 100 000 women annually higher in Portugal than in England. Differences in human papillomavirus prevalence and risk factors for infection and disease progression do not explain the difference found in cervical cancer incidence. Significant mortality declines in both countries followed the introduction of different screening policies, although England showed a greater decline than Portugal over nearly 2 decades after centralizing organized screening. Conclusion: The higher rates of cervical cancer in Portugal compared to England can be explained by differences in screening quality and coverage

fluEvidenceSynthesis: An R package for evidence synthesis based analysis of epidemiological outbreaks.

Public health related decisions often have to balance the cost of intervention strategies with the benefit of the reduction in disease burden. While the cost can often be inferred, forward modelling of the effect of different intervention options is complicated and disease specific. Here we introduce a package that is aimed to simplify this process. The package allows one to infer parameters using a Bayesian approach, perform forward modelling of the likely results of the proposed intervention and finally perform cost effectiveness analysis of the results. The package is based on a method previously used in the United Kingdom to inform vaccination strategies for influenza, with extensions to make it easily adaptable to other diseases and data sources

Cost-effectiveness analysis of quadrivalent seasonal influenza vaccines in England.

BACKGROUND: As part of the national seasonal influenza vaccination programme in England and Wales, children receive a quadrivalent vaccine offering protection against two influenza A strains and two influenza B strains. Healthy children receive a quadrivalent live attenuated influenza vaccine (QLAIV), whilst children with contraindications receive the quadrivalent inactivated influenza vaccine (QIIV). Individuals aged younger than 65 years in the clinical risk populations and elderly individuals aged 65+ years receive either a trivalent inactivated influenza vaccine (TIIV) offering protection from two A strains and one B strain or the QIIV at the choice of their general practitioner. The cost-effectiveness of quadrivalent vaccine programmes is an open question. The original analysis that supported the paediatric programme only considered a trivalent live attenuated vaccine (LAIV). The cost-effectiveness of the QIIV to other patients has not been established. We sought to estimate the cost-effectiveness of these programmes, establishing a maximum incremental total cost per dose of quadrivalent vaccines over trivalent vaccines. METHODS: We used the same mathematical model as the analysis that recommended the introduction of the paediatric influenza vaccination programme. The incremental cost of the quadrivalent vaccine is the additional cost over that of the existing trivalent vaccine currently in use. RESULTS: Introducing quadrivalent vaccines can be cost-effective for all targeted groups. However, the cost-effectiveness of the programme is dependent on the choice of target cohort and the cost of the vaccines: the paediatric programme is cost-effective with an increased cost of £6.36 per dose, though an extension to clinical risk individuals younger than 65 years old and further to all elderly individuals means the maximum incremental cost is £1.84 and £0.20 per dose respectively. CONCLUSIONS: Quadrivalent influenza vaccines will bring substantial health benefits, as they are cost-effective in particular target groups

Evaluating the next generation of RSV intervention strategies:a mathematical modelling study and cost-effectiveness analysis

BACKGROUND: With a suite of promising new RSV prophylactics on the horizon, including long-acting monoclonal antibodies and new vaccines, it is likely that one or more of these will replace the current monoclonal Palivizumab programme. However, choosing the optimal intervention programme will require balancing the costs of the programmes with the health benefits accrued. METHODS: To compare the next generation of RSV prophylactics, we integrated a novel transmission model with an economic analysis. We estimated key epidemiological parameters by calibrating the model to 7 years of historical epidemiological data using a Bayesian approach. We determined the cost-effective and affordable maximum purchase price for a comprehensive suite of intervention programmes. FINDINGS: Our transmission model suggests that maternal protection of infants is seasonal, with 38-62% of infants born with protection against RSV. Our economic analysis found that to cost-effectively and affordably replace the current monoclonal antibody Palivizumab programme with long-acting monoclonal antibodies, the purchase price per dose would have to be less than around £4350 but dropping to £200 for vaccinated heightened risk infants or £90 for all infants. A seasonal maternal vaccine would have to be priced less than £85 to be cost-effective and affordable. While vaccinating pre-school and school-age children is likely not cost-effective relative to elderly vaccination programmes, vaccinating the elderly is not likely to be affordable. Conversely, vaccinating infants at 2 months seasonally would be cost-effective and affordable if priced less than £80. CONCLUSIONS: In a setting with seasonal RSV epidemiology, maternal protection conferred to newborns is also seasonal, an assumption not previously incorporated in transmission models of RSV. For a country with seasonal RSV dynamics like England, seasonal programmes rather than year-round intervention programmes are always optimal

Influenza Interaction with Cocirculating Pathogens, and Its Impact on Surveillance, Pathogenesis and Epidemic Profile: A Key Role for Mathematical Modeling

ABSTRACTEvidence is mounting that influenza virus, a major contributor to the global disease burden, interacts with other pathogens infecting the human respiratory tract. Taking into account interactions with other pathogens may be critical to determining the real influenza burden and the full impact of public health policies targeting influenza. That necessity is particularly true for mathematical modeling studies, which have become critical in public health decision-making, despite their usually focusing on lone influenza virus acquisition and infection, thereby making broad oversimplifications regarding pathogen ecology. Herein, we review evidence of influenza virus interaction with bacteria and viruses, and the modeling studies that incorporated some of these. Despite the many studies examining possible associations between influenza andStreptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Neisseria meningitides, respiratory syncytial virus, human rhinoviruses, human parainfluenza viruses, etc., very few mathematical models have integrated other pathogens alongside influenza. A notable exception is the recent modeling of the pneumococcus-influenza interaction, which highlighted potential influenza-related increased pneumococcal transmission and pathogenicity. That example demonstrates the power of dynamic modeling as an approach to test biological hypotheses concerning interaction mechanisms and estimate the strength of those interactions. We explore how different interference mechanisms may lead to unexpected incidence trends and misinterpretations. Using simple transmission models, we illustrate how existing interactions might impact public health surveillance systems and demonstrate that the development of multipathogen models is essential to assess the true public health burden of influenza, and help improve planning and evaluation of control measures. Finally, we identify the public health needs, surveillance, modeling and biological challenges, and propose avenues of research for the coming years.Author SummaryInfluenza is a major pathogen responsible for important morbidity and mortality burdens worldwide. Mathematical models of influenza virus acquisition have been critical to understanding its epidemiology and planning public health strategies of infection control. It is increasingly clear that microbes do not act in isolation but potentially interact within the host. Hence, studying influenza alone may lead to masking effects or misunderstanding information on its transmission and severity. Herein, we review the literature on bacterial and viral species that interact with the influenza virus, interaction mechanisms, and mathematical modeling studies integrating interactions. We report evidence that, beyond the classic secondary bacterial infections, many pathogenic bacteria and viruses probably interact with influenza. Public health relevance of pathogen interactions is detailed, showing how potential misreading or a narrow outlook might lead to mistaken public health decisionmaking. We describe the role of mechanistic transmission models in investigating this complex system and obtaining insight into interactions between influenza and other pathogens. Finally, we highlight benefits and challenges in modeling, and speculate on new opportunities made possible by taking a broader view: including basic science, clinical relevance and public health.</jats:sec

Introduction to particle Markov-chain Monte Carlo for disease dynamics modellers.

The particle Markov-chain Monte Carlo (PMCMC) method is a powerful tool to efficiently explore high-dimensional parameter space using time-series data. We illustrate an overall picture of PMCMC with minimal but sufficient theoretical background to support the readers in the field of biomedical/health science to apply PMCMC to their studies. Some working examples of PMCMC applied to infectious disease dynamic models are presented with R code