On the role of different age groups during pertussis epidemics in California, 2010 and 2014.

There is limited information on the roles of different age groups in propagating pertussis outbreaks, and the temporal changes in those roles since the introduction of acellular pertussis vaccines. The relative roles of different age groups in propagating the 2010 and the 2014 pertussis epidemics in California were evaluated using the relative risk (RR) statistic that measures the change in the group's proportion among all detected cases before vs. after the epidemic peak. For the 2010-11 epidemic, evidence for a predominant transmission age group was weak, with the largest RR estimates being 1.26 (95% CI 1.08-1.46) (aged 11-13 years); 1.19 (1.01-1.4) (aged 9-10 years); 1.17 (0.86-1.59) (aged 14-15 years); 1.12 (0.86-1.46) (aged 16-19 years) and 1.1 (0.89-1.36) (aged 7-8 years). The 2014 epidemic showed a strong signal of the role of older adolescents, with the highest RR estimate being in those aged 14-15 years (RR = 1.83, 1.61-2.07), followed by adolescents aged 16-19 years (RR = 1.41, 1.24-1.61) and 11-13 years (RR = 1.26, 1.12-1.41), with lower RR estimates in other age groups. As the time following introduction of acellular pertussis vaccines in California progressed, older adolescents played an increasing role in transmission during the major pertussis outbreaks. Booster pertussis vaccination for older adolescents with vaccines effective against pertussis transmission should be considered with the aim of mitigating future pertussis epidemics in the community.

Comparative dynamics, seasonality in transmission, and predictability of childhood infections in Mexico.

The seasonality and periodicity of infections, and the mechanisms underlying observed dynamics, can have implications for control efforts. This is particularly true for acute childhood infections. Among these, the dynamics of measles is the best understood and has been extensively studied, most notably in the UK prior to the start of vaccination. Less is known about the dynamics of other childhood diseases, particularly outside Europe and the United States. In this paper, we leverage a unique dataset to examine the epidemiology of six childhood infections - measles, mumps, rubella, varicella, scarlet fever and pertussis - across 32 states in Mexico from 1985 to 2007. This dataset provides us with a spatio-temporal probe into the dynamics of six common childhood infections, and allows us to compare them in the same setting over the same time period. We examine three key epidemiological characteristics of these infections - the age profile of infections, spatio-temporal dynamics, and seasonality in transmission - and compare them with predictions from existing theory and past findings. Our analysis reveals interesting epidemiological differences between the six pathogens, and variations across space. We find signatures of term-time forcing (reduced transmission during the summer) for measles, mumps, rubella, varicella, and scarlet fever; for pertussis, a lack of term-time forcing could not be rejected.

Drivers of measles mortality: the historic fatality burden of famine in Bangladesh.

Measles is a major cause of childhood morbidity and mortality in many parts of the world. Estimates of the case-fatality rate (CFR) of measles have varied widely from place to place, as well as in the same location over time. Amongst populations that have experienced famine or armed conflict, measles CFR can be especially high, although past work has mostly focused on refugee populations. Here, we estimate measles CFR between 1970 and 1991 in a rural region of Bangladesh, which experienced civil war and famine in the 1970s. We use historical measles mortality data and a mechanistic model of measles transmission to estimate the CFR of measles. We first demonstrate the ability of this model to recover the CFR in the absence of incidence data, using simulated mortality data. Our method produces CFR estimates that correspond closely to independent estimates from surveillance data and we can capture both the magnitude and the change in CFR suggested by these previous estimates. We use this method to quantify the sharp increase in CFR that resulted in a large number of deaths during a measles outbreak in the region in 1976. Most of the children who died during this outbreak were born during a famine in 1974, or in the 2 years preceding the famine. Our results suggest that the period of turmoil during and after the 1971 war and the sustained effects of the famine, is likely to have contributed to the high fatality burden of the 1976 measles outbreak in Matlab.

A matrix representation for sustainable activities.

The present paper develops a matrix framework to determine the sustainability of multiple activities simultaneously. We define an activity as an action or process involving multiple resources; each activity depends upon other activities at a higher level. First, the problem of sustainability is framed in the context of an input-output model. Later, an infinite layer approach is adopted to represent different sectors of the economy and both renewable and non-renewable resources. Using the infinite layer approach, the concentration of renewable energy usage is calculated for each activity. The conversion to productive use varies from one resource to another. The current endeavor also focuses on enhancing energy efficiency to reduce non-renewable resource dependency.

Force delivery of NiTi orthodontic arch wire at different magnitude of deflections and temperatures: A finite element study.

NiTi arch wires are used widely in orthodontic treatment due to its superelastic and biocompatibility properties. In brackets configuration, the force released from the arch wire is influenced by the sliding resistances developed on the arch wire-bracket contact. This study investigated the evolution of the forces released by a rectangular NiTi arch wire towards possible intraoral temperature and deflection changes. A three dimensional finite element model was developed to measure the force-deflection behavior of superelastic arch wire. Finite element analysis was used to distinguish the martensite fraction and phase state of arch wire microstructure in relation to the magnitude of wire deflection. The predicted tensile and bending results from the numerical model showed a good agreement with the experimental results. As contact developed between the wire and bracket, binding influenced the force-deflection curve by changing the martensitic transformation plateau into a slope. The arch wire recovered from greater magnitude of deflection released lower force than one recovered from smaller deflection. In contrast, it was observed that the plateau slope increased from 0.66N/mm to 1.1N/mm when the temperature was increased from 26°C to 46°C.

Predictability in a highly stochastic system: final size of measles epidemics in small populations.

A standard assumption in the modelling of epidemic dynamics is that the population of interest is well mixed, and that no clusters of metapopulations exist. The well-known and oft-used SIR model, arguably the most important compartmental model in theoretical epidemiology, assumes that the disease being modelled is strongly immunizing, directly transmitted and has a well-defined period of infection, in addition to these population mixing assumptions. Childhood infections, such as measles, are prime examples of diseases that fit the SIR-like mechanism. These infections have been well studied for many systems with large, well-mixed populations with endemic infection. Here, we consider a setting where populations are small and isolated. The dynamics of infection are driven by stochastic extinction-recolonization events, producing large, sudden and short-lived epidemics before rapidly dying out from a lack of susceptible hosts. Using a TSIR model, we fit prevaccination measles incidence and demographic data in Bornholm, the Faroe Islands and four districts of Iceland, between 1901 and 1965. The datasets for each of these countries suffer from different levels of data heterogeneity and sparsity. We explore the potential for prediction of this model: given historical incidence data and up-to-date demographic information, and knowing that a new epidemic has just begun, can we predict how large it will be? We show that, despite a lack of significant seasonality in the incidence of measles cases, and potentially severe heterogeneity at the population level, we are able to estimate the size of upcoming epidemics, conditioned on the first time step, to within reasonable confidence. Our results have potential implications for possible control measures for the early stages of new epidemics in small populations.