Modelling the Proportion of Influenza Infections within Households during Pandemic and Non-Pandemic Years

Background: The key epidemiological difference between pandemic and seasonal influenza is that the population is largely susceptible during a pandemic, whereas, during non-pandemic seasons a level of immunity exists. The population-level efficacy of household-based mitigation strategies depends on the proportion of infections that occur within households. In general, mitigation measures such as isolation and quarantine are more effective at the population level if the proportion of household transmission is low. Methods/Results: We calculated the proportion of infections within households during pandemic years compared with non-pandemic years using a deterministic model of household transmission in which all combinations of household size and individual infection states were enumerated explicitly. We found that the proportion of infections that occur within households was only partially influenced by the hazard h of infection within household relative to the hazard of infection outside the household, especially for small basic reproductive numbers. During pandemics, the number of within-household infections was lower than one might expect for a given h because many of the susceptible individuals were infected from the community and the number of susceptible individuals within household was thus depleted rapidly. In addition, we found that for the value of h at which 30% of infections occur within households during non-pandemic years, a similar 31% of infections occur within households during pandemic years. Interpretation: We suggest that a trade off between the community force of infection and the number of susceptible individuals in a household explains an apparent invariance in the proportion of infections that occur in households in our model. During a pandemic, although there are more susceptible individuals in a household, the community force of infection is very high. However, during non-pandemic years, the force of infection is much lower but there are fewer susceptible individuals within the household. © 2011 Kwok et al.published_or_final_versio

The spatial resolution of epidemic peaks

The emergence of novel respiratory pathogens can challenge the capacity of key health care resources, such as intensive care units, that are constrained to serve only specific geographical populations. An ability to predict the magnitude and timing of peak incidence at the scale of a single large population would help to accurately assess the value of interventions designed to reduce that peak. However, current disease-dynamic theory does not provide a clear understanding of the relationship between: epidemic trajectories at the scale of interest (e.g. city); population mobility; and higher resolution spatial effects (e.g. transmission within small neighbourhoods). Here, we used a spatially-explicit stochastic meta-population model of arbitrary spatial resolution to determine the effect of resolution on model-derived epidemic trajectories. We simulated an influenza-like pathogen spreading across theoretical and actual population densities and varied our assumptions about mobility using Latin-Hypercube sampling. Even though, by design, cumulative attack rates were the same for all resolutions and mobilities, peak incidences were different. Clear thresholds existed for all tested populations, such that models with resolutions lower than the threshold substantially overestimated population-wide peak incidence. The effect of resolution was most important in populations which were of lower density and lower mobility. With the expectation of accurate spatial incidence datasets in the near future, our objective was to provide a framework for how to use these data correctly in a spatial meta-population model. Our results suggest that there is a fundamental spatial resolution for any pathogen-population pair. If underlying interactions between pathogens and spatially heterogeneous populations are represented at this resolution or higher, accurate predictions of peak incidence for city-scale epidemics are feasible

Polyclonal mucosa-associated invariant T cells have unique innate functions in bacterial infection

Mucosa-associated invariant T (MAIT) cells are a unique population of αβ T cells in mammals that reside preferentially in mucosal tissues and express an invariant Vα paired with limited Vβ T-cell receptor (TCR) chains. Furthermore, MAIT cell development is dependent upon the expression of the evolutionarily conserved major histocompatibility complex (MHC) class Ib molecule MR1. Using in vitro assays, recent studies have shown that mouse and human MAIT cells are activated by antigen-presenting cells (APCs) infected with diverse microbes, including numerous bacterial strains and yeasts, but not viral pathogens. However, whether MAIT cells play an important, and perhaps unique, role in controlling microbial infection has remained unclear. To probe MAIT cell function, we show here that purified polyclonal MAIT cells potently inhibit intracellular bacterial growth of Mycobacterium bovis BCG in macrophages (MΦ) in coculture assays, and this inhibitory activity was dependent upon MAIT cell selection by MR1, secretion of gamma interferon (IFN-γ), and an innate interleukin 12 (IL-12) signal from infected MΦ. Surprisingly, however, the cognate recognition of MR1 by MAIT cells on the infected MΦ was found to play only a minor role in MAIT cell effector function. We also report that MAIT cell-deficient mice had higher bacterial loads at early times after infection compared to wild-type (WT) mice, demonstrating that MAIT cells play a unique role among innate lymphocytes in protective immunity against bacterial infection

Environmental Predictors of Seasonal Influenza Epidemics across Temperate and Tropical Climates

Human influenza infections exhibit a strong seasonal cycle in temperate regions. Recent laboratory and epidemiological evidence suggests that low specific humidity conditions facilitate the airborne survival and transmission of the influenza virus in temperate regions, resulting in annual winter epidemics. However, this relationship is unlikely to account for the epidemiology of influenza in tropical and subtropical regions where epidemics often occur during the rainy season or transmit year-round without a well-defined season. We assessed the role of specific humidity and other local climatic variables on influenza virus seasonality by modeling epidemiological and climatic information from 78 study sites sampled globally. We substantiated that there are two types of environmental conditions associated with seasonal influenza epidemics: “cold-dry” and “humid-rainy”. For sites where monthly average specific humidity or temperature decreases below thresholds of approximately 11–12 g/kg and 18–21°C during the year, influenza activity peaks during the cold-dry season (i.e., winter) when specific humidity and temperature are at minimal levels. For sites where specific humidity and temperature do not decrease below these thresholds, seasonal influenza activity is more likely to peak in months when average precipitation totals are maximal and greater than 150 mm per month. These findings provide a simple climate-based model rooted in empirical data that accounts for the diversity of seasonal influenza patterns observed across temperate, subtropical and tropical climates

CO2 fixation employing an iridium(i)-hydroxide complex

The reactivity of a number of IrI complexes towards CO 2 is explored using [Ir(NHC)(OH)] as a key synthon. CO2 insertion into Ir-O and Ir-N bonds proved facile, yielding a number of Ir I-carbonates and -carbamates. Most importantly, reaction between CO2 and IrI-OH led to isolation of the novel [{Ir I}2-(μ-κ1:κ2-CO 3)] complex

CD46 Engagement on Human CD4+ T Cells Produces T Regulatory Type 1-Like Regulation of Antimycobacterial T Cell Responses ▿

Understanding the regulation of human immune responses is critical for vaccine development and treating infectious diseases. We have previously shown that simultaneous engagement of the T cell receptor (TCR) and complement regulator CD46 on human CD4+ T cells in the presence of interleukin-2 (IL-2) induces potent secretion of the immunomodulatory cytokine IL-10. These T cells mediate IL-10-dependent suppression of bystander CD4+ T cells activated in vitro with anti-CD3 and anti-CD28 costimulation, reflecting a T regulatory type 1 (Tr1)-like phenotype. However, CD46-mediated negative regulation of pathogen-specific T cells has not been described. Therefore, we studied the ability of CD46-activated human CD4+ T cells to suppress T cell responses to Mycobacterium bovis BCG, the live vaccine that provides infants protection against the major human pathogen Mycobacterium tuberculosis. Our results demonstrate that soluble factors secreted by CD46-activated human CD4+ T cells suppress mycobacterium-specific CD4+, CD8+, and γ9δ2 TCR+ T cells. Dendritic cell functions were not downregulated in our experiments, indicating that CD46-triggered factors directly suppress pathogen-specific T cells. Interestingly, IL-10 appeared to play a less pronounced role in our system, especially in the suppression of γ9δ2 TCR+ T cells, suggesting the presence of additional undiscovered soluble immunoregulatory factors. Blocking endogenous CD46 signaling 3 days after mycobacterial infection enhanced BCG-specific T cell responses in a subset of volunteers. Taken together, these results indicate that CD46-dependent negative regulatory mechanisms can impair T cell responses vital for immune defense against mycobacteria. Therefore, modulating CD46-induced immune regulation could be integral to the development of improved tuberculosis therapeutics or vaccines