For infectious pathogens such as Staphylococcus aureus and Streptococcus pneumoniae , some hosts may carry the pathogen and transmit it to others, yet display no symptoms themselves. These asymptomatic carriers contribute to the spread of disease but go largely undetected and can therefore undermine efforts to control transmission. Understanding the natural history of carriage and its relationship to disease is important for the design of effective interventions to control transmission. Mathematical models of infectious diseases are frequently used to inform decisions about control and should therefore accurately capture the role played by asymptomatic carriers. In practice, incorporating asymptomatic carriers into models is challenging due to the sparsity of direct evidence. This absence of data leads to uncertainty in estimates of model parameters and, more fundamentally, in the selection of an appropriate model structure. To assess the implications of this uncertainty, we systematically reviewed published models of carriage and propose a new model of disease transmission with asymptomatic carriage. Analysis of our model shows how different assumptions about the role of asymptomatic carriers can lead to different conclusions about the transmission and control of disease. Critically, selecting an inappropriate model structure, even when parameters are correctly estimated, may lead to over- or under-estimates of intervention effectiveness. Our results provide a more complete understanding of the role of asymptomatic carriers in transmission and highlight the importance of accurately incorporating carriers into models used to make decisions about disease control.

Background: Pregnant women have an elevated risk of illness and hospitalisation from influenza. Pregnant women are recommended to be prioritised for influenza vaccination during any stage of pregnancy. The risk of seasonal influenza varies substantially throughout the year in temperate climates; however, there is limited knowledge of how vaccination timing during pregnancy impacts the benefits received by the mother and foetus.Objectives: To compare antenatal vaccination timing with regard to influenza vaccine immunogenicity during pregnancy and transplacental transfer to their newborns.Methods: Studies were eligible for inclusion if immunogenicity to influenza vaccine was evaluated in women stratified by trimester of pregnancy. Haemagglutination inhibition (HI) titres, stratified by trimester of vaccination, had to be measured at either pre‐vaccination and within one month post‐vaccination, post‐vaccination and at delivery in the mother, or in cord/newborn blood. Authors searched PubMed, Scopus, Web of Science and EMBASE databases from inception until June 2016 and authors of identified studies were contacted for additional data. Extracted data were tabulated and summarised via random‐effect meta‐analyses and qualitative methods.Results: Sixteen studies met the inclusion criteria. Meta‐analyses found that compared with women vaccinated in an earlier trimester, those vaccinated in a later trimester had a greater fold increase in HI titres (1.33‐ to 1.96‐fold) and higher HI titres in cord/newborn blood (1.21‐ to 1.64‐fold).Conclusions: This review provides comparative analysis of the effect of vaccination timing on maternal immunogenicity and protection of the infant that is informative and relevant to current vaccine scheduling for pregnant women.

The demographic structure of populations in both more developed and less developed countries is changing: increases in life expectancy and declining fertility have led to older populations and smaller households. The implications of these demographic changes for the spread and control of infectious diseases are not fully understood. Here we use an individual based model with realistic and dynamic age and household structure to demonstrate the marked effect that demographic change has on disease transmission at the population and household level. The decline in fertility is associated with a decrease in disease incidence and an increase in the age of first infection, even in the absence of vaccination or other control measures. Although large households become rarer as fertility decreases, we show that there is a proportionate increase in incidence of disease in these households as the accumulation of susceptible clusters increases the potential for explosive outbreaks. By modelling vaccination, we provide a direct comparison of the relative importance of demographic change and vaccination on incidence of disease. We highlight the increased risks associated with unvaccinated households in a low fertility setting if vaccine behaviour is correlated with household membership. We suggest that models that do not account for future demographic change, and especially its effect on household structure, may potentially overestimate the impact of vaccination.

Computer-simulated synthetic populations are used by researchers and policy makers to help understand and predict the aggregate behaviour of large numbers of individuals. Research aims include explaining the structural and dynamic characteristics of populations, and the implications of these characteristics for dynamic processes such as the spread of disease, opinions and social norms. Policy makers planning for the future economic, healthcare or infrastructure needs of a population want to be able to evaluate the possible effects of their policies. In both cases, it is desirable that the structure and dynamic behaviour of synthetic populations be statistically congruent to that of real populations. Here, we present a parsimonious individual-based model for generating synthetic population dynamics that focuses on the effects that demographic change have on the structure and composition of households.

Early estimation of the probable impact of a pandemic influenza outbreak can assist public health authorities to ensure that response measures are proportionate to the scale of the threat. Recently, frameworks based on transmissibility and severity have been proposed for initial characterization of pandemic impact. Data requirements to inform this assessment may be provided by "First Few Hundred" (FF100) studies, which involve surveillance-possibly in person, or via telephone-of household members of confirmed cases. This process of enhanced case finding enables detection of cases across the full spectrum of clinical severity, including the date of symptom onset. Such surveillance is continued until data for a few hundred cases, or satisfactory characterization of the pandemic strain, has been achieved. We present a method for analysing these data, at the household level, to provide a posterior distribution for the parameters of a model that can be interpreted in terms of severity and transmissibility of a pandemic strain. We account for imperfect case detection, where individuals are only observed with some probability that can increase after a first case is detected. Furthermore, we test this methodology using simulated data generated by an independent model, developed for a different purpose and incorporating more complex disease and social dynamics. Our method recovers transmissibility and severity parameters to a high degree of accuracy and provides a computationally efficient approach to estimating the impact of an outbreak in its early stages.

The structure of many biological, social and technological systems can usefully be described in terms of complex networks. Although often portrayed as fixed in time, such networks are inherently dynamic, as the edges that join nodes are cut and rewired, and nodes themselves update their states. Understanding the structure of these networks requires us to understand the dynamic processes that create, maintain and modify them. Here, we build upon existing models of coevolving networks to characterize how dynamic behaviour at the level of individual nodes generates stable aggregate behaviours. We focus particularly on the dynamics of groups of nodes formed endogenously by nodes that share similar properties (represented as node state) and demonstrate that, under certain conditions, network modularity based on state compares well with network modularity based on topology. We show that if nodes rewire their edges based on fixed node states, the network modularity reaches a stable equilibrium which we quantify analytically. Furthermore, if node state is not fixed, but can be adopted from neighbouring nodes, the distribution of group sizes reaches a dynamic equilibrium, which remains stable even as the composition and identity of the groups change. These results show that dynamic networks can maintain the stable community structure that has been observed in many social and biological systems.