Estimating within-household contact networks from egocentric data

Acute respiratory diseases are transmitted over networks of social contacts. Large-scale simulation models are used to predict epidemic dynamics and evaluate the impact of various interventions, but the contact behavior in these models is based on simplistic and strong assumptions which are not informed by survey data. These assumptions are also used for estimating transmission measures such as the basic reproductive number and secondary attack rates. Development of methodology to infer contact networks from survey data could improve these models and estimation methods. We contribute to this area by developing a model of within-household social contacts and using it to analyze the Belgian POLYMOD data set, which contains detailed diaries of social contacts in a 24-hour period. We model dependency in contact behavior through a latent variable indicating which household members are at home. We estimate age-specific probabilities of being at home and age-specific probabilities of contact conditional on two members being at home. Our results differ from the standard random mixing assumption. In addition, we find that the probability that all members contact each other on a given day is fairly low: 0.49 for households with two 0--5 year olds and two 19--35 year olds, and 0.36 for households with two 12--18 year olds and two 36+ year olds. We find higher contact rates in households with 2--3 members, helping explain the higher influenza secondary attack rates found in households of this size.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS474 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Estimating within-school contact networks to understand influenza transmission

Many epidemic models approximate social contact behavior by assuming random mixing within mixing groups (e.g., homes, schools and workplaces). The effect of more realistic social network structure on estimates of epidemic parameters is an open area of exploration. We develop a detailed statistical model to estimate the social contact network within a high school using friendship network data and a survey of contact behavior. Our contact network model includes classroom structure, longer durations of contacts to friends than nonfriends and more frequent contacts with friends, based on reports in the contact survey. We performed simulation studies to explore which network structures are relevant to influenza transmission. These studies yield two key findings. First, we found that the friendship network structure important to the transmission process can be adequately represented by a dyad-independent exponential random graph model (ERGM). This means that individual-level sampled data is sufficient to characterize the entire friendship network. Second, we found that contact behavior was adequately represented by a static rather than dynamic contact network.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS505 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Estimating Influenza Vaccine Efficacy From Challenge and Community-based Study Data

In this paper, the authors provide estimates of 4 measures of vaccine efficacy for live, attenuated and inactivated influenza vaccine based on secondary analysis of 5 experimental influenza challenge studies in seronegative adults and community-based vaccine trials. The 4 vaccine efficacy measures are for susceptibility (VES), symptomatic illness given infection (VEP), infection and illness (VESP), and infectiousness (VEI). The authors also propose a combined (VEC) measure of the reduction in transmission in the entire population based on all of the above efficacy measures. Live influenza vaccine and inactivated vaccine provided similar protection against laboratory-confirmed infection (for live vaccine: VES  = 41%, 95% confidence interval (CI): 15, 66; for inactivated vaccine: VES  = 43%, 95% CI: 8, 79). Live vaccine had a higher efficacy for illness given infection (VEP  = 67%, 95% CI: 24, 100) than inactivated vaccine (VEP  = 29%, 95% CI: −19, 76), although the difference was not statistically significant. VESP for the live vaccine was higher than for the inactivated vaccine. VEI estimates were particularly low for these influenza vaccines. VESP and VEC can remain high for both vaccines, even when VEI is relatively low, as long as the other 2 measures of vaccine efficacy are relatively high

The role of 1,25-dihydroxyvitamin D in the inhibition of bone formation induced by skeletal unloading

Skeletal unloading results in osteopenia. To examine the involvement of vitamin D in this process, the rear limbs of growing rats were unloaded and alterations in bone calcium and bone histology were related to changes in serum calcium (Ca), inorganic phosphorus (P sub i), 25-hydroxyvitamin D (25-OH-D), 24,25-dihydroxyvitamin D (24,25(OH)2D and 1,25-dihydroxyvitamin D (1,25(OH)2D. Acute skeletal unloading induced a transitory inhibition of Ca accumulation in unloaded bones. This was accompanied by a transitory rise in serum Ca, a 21% decrease in longitudinal bone growth (P 0.01), a 32% decrease in bone surface lined with osteoblasts (P .05), no change in bone surface lined with osteoclasts and a decrease in circulating (1,25(OH)2D. No significant changes in the serum concentrations of P sub i, 25-OH-D or 24,25(OH)2D were observed. After 2 weeks of unloading, bone Ca stabilized at approximately 70% of control and serum Ca and 1,25(OH)2D returned to control values. Maintenance of a constant serum 1,25(OH)2D concentration by chronic infusion of 1,25(OH)2D (Alza osmotic minipump) throughout the study period did not prevent the bone changes induced by acute unloading. These results suggest that acute skeletal unloading in the growing rat produces a transitory inhibition of bone formation which in turn produces a transitory hypercalcemia

Simulations for designing and interpreting intervention trials in infectious diseases.

BACKGROUND: Interventions in infectious diseases can have both direct effects on individuals who receive the intervention as well as indirect effects in the population. In addition, intervention combinations can have complex interactions at the population level, which are often difficult to adequately assess with standard study designs and analytical methods. DISCUSSION: Herein, we urge the adoption of a new paradigm for the design and interpretation of intervention trials in infectious diseases, particularly with regard to emerging infectious diseases, one that more accurately reflects the dynamics of the transmission process. In an increasingly complex world, simulations can explicitly represent transmission dynamics, which are critical for proper trial design and interpretation. Certain ethical aspects of a trial can also be quantified using simulations. Further, after a trial has been conducted, simulations can be used to explore the possible explanations for the observed effects. CONCLUSION: Much is to be gained through a multidisciplinary approach that builds collaborations among experts in infectious disease dynamics, epidemiology, statistical science, economics, simulation methods, and the conduct of clinical trials

Integrated post-occupancy evaluation and intervention that achieve real-world zero-carbon buildings

Many building standards are available worldwide to support sustainable building design. However, most of them only define compliance before occupancy, overlooking the real building usage and its implications for meeting zero-carbon targets. This research proposes a systematic post-occupancy evaluation and intervention (POEI) protocol to analyse real building performance and support cost-effective and zero-carbon upgrading. The novelty lies in integrating novel diagnostic data science techniques to identify performance gaps, advanced physics-driven energy modelling iteratively calibrated with POE data to disaggregate energy use by end-use, and cost-optimal intervention analysis. The POEI protocol has been validated in a university building. The results demonstrated the POEI benefits for the cost-optimal upgrading of in-use building assets based on real needs. By optimising control, reducing demand, increasing efficiency, and implementing renewables, the building can reach the nearly zero-carbon building target with a payback period of 13 years and a 23 % lower life-cycle cost compared to the baseline scenario. Building energy management interventions were identified as the most important actions to reduce the performance gap, reducing energy consumption by 20 % and carbon emissions by 24 %. The results also highlight the critical role of Information and Communication Technologies (ICT), which may represent up to 49 % of energy use in the future zero-carbon building scenario if overlooked

Requirements for In-Situ Authoring of Location Based Experiences

In this paper we describe an investigation into the requirements for and the use of in-situ authoring in the creation of location based pervasive and UbiComp experiences. We will focus on the co-design process with users that resulted in a novel visitor experience to a historic country estate. This has informed the design of new, in-situ, authoring tools supplemented with tools for retrospective revisiting and reorganization of content. An initial trial of these new tools will be discussed and conclusions drawn as to the appropriateness of such tools. Further enhancements as part of future trials will also be described