Assessing the Sensitivity of the Canadian Adverse Event Following Immunization Surveillance System ( CAEFISS)

Background: Vaccines are important to public health, but because of the way they are manufactured, their mechanism of action, and their indicated population, careful monitoring of their adverse events is necessary. Canada has a national surveillance system that collects reports on adverse events that may be associated with vaccine administration. Sensitivity is one of the tools used with surveillance systems to study the extent and characteristics of reporting of a surveillance system. To date, the sensitivity of the Canadian system has not been assessed. Purpose: To assess the sensitivity of the Canadian Adverse Event Following Immunization Surveillance System (CAEFISS). Methods: Based on specific adverse events following immunization (AEFI) and vaccines chosen for the study, a thorough literature search was completed to find the best source which identifies expected rates of AEFI. Studies used were assessed based on quality and sample size. The expected rates of AEFI, in combination with public health estimates of vaccine coverage rates, were used to estimate the expected number of reports. The reports provided the actual number of events used to calculate the sensitivity. Sensitivity was compared based on year of administration, age group, and type of AEFI. Results: The overall sensitivity of the CAEFISS varied from 1.0% to 136.6% for various AEFI for the years 1997 to 2008. For influenza the sensitivity was found to be 93.6% and 136.3% for GBS and anaphylaxis respectively. For DTaP, the rates were found to be 15.0%, 1.0%, and 21.2% for anaphylaxis, HHE, and seizures respectively, and for MMR the rates were 16.5%, 52.7%, and 12.7% in relation to anaphylaxis, thrombocytopenia, and seizures respectively. Conclusions: This is the first assessment of the sensitivity of the CAEFISS, and this study found that the system has reasonable ability to detect AEFI on a national level. CAEFISS had comparable senstivity to other vaccine reporting systems. Many of the AEFI had sensitivity values higher than the 5%-10% range traditionally seen in other passive surveillance systems related to adverse events. The greatest variation of sensitivity was seen between vaccines. Rarity and timing of the AEFI may also impact the sensitivity. Variation of sensitivity and the variation found in the sensitivity analysis lend to the further development and implementations of case definitions for rarer adverse events, especially anaphylaxis. Further research of other factors that impact reporting is necessary

Proactive Resource Allocation: Harnessing the Diversity and Multicast Gains

This paper introduces the novel concept of proactive resource allocation through which the predictability of user behavior is exploited to balance the wireless traffic over time, and hence, significantly reduce the bandwidth required to achieve a given blocking/outage probability. We start with a simple model in which the smart wireless devices are assumed to predict the arrival of new requests and submit them to the network T time slots in advance. Using tools from large deviation theory, we quantify the resulting prediction diversity gain} to establish that the decay rate of the outage event probabilities increases with the prediction duration T. This model is then generalized to incorporate the effect of the randomness in the prediction look-ahead time T. Remarkably, we also show that, in the cognitive networking scenario, the appropriate use of proactive resource allocation by the primary users improves the diversity gain of the secondary network at no cost in the primary network diversity. We also shed lights on multicasting with predictable demands and show that the proactive multicast networks can achieve a significantly higher diversity gain that scales super-linearly with T. Finally, we conclude by a discussion of the new research questions posed under the umbrella of the proposed proactive (non-causal) wireless networking framework

Subcellular Microanatomy by 3D Deconvolution Brightfield Microscopy: Method and Analysis Using Human Chromatin in the Interphase Nucleus

Anatomy has advanced using 3-dimensional (3D) studies at macroscopic (e.g., dissection, injection moulding of vessels, radiology) and microscopic (e.g., serial section reconstruction with light and electron microscopy) levels. This paper presents the first results in human cells of a new method of subcellular 3D brightfield microscopy. Unlike traditional 3D deconvolution and confocal techniques, this method is suitable for general application to brightfield microscopy. Unlike brightfield serial sectioning it has subcellular resolution. Results are presented of the 3D structure of chromatin in the interphase nucleus of two human cell types, hepatocyte and plasma cell. I show how the freedom to examine these structures in 3D allows greater morphological discrimination between and within cell types and the 3D structural basis for the classical “clock-face” motif of the plasma cell nucleus is revealed. Potential for further applications discussed

Learning-aided Stochastic Network Optimization with Imperfect State Prediction

We investigate the problem of stochastic network optimization in the presence of imperfect state prediction and non-stationarity. Based on a novel distribution-accuracy curve prediction model, we develop the predictive learning-aided control (PLC) algorithm, which jointly utilizes historic and predicted network state information for decision making. PLC is an online algorithm that requires zero a-prior system statistical information, and consists of three key components, namely sequential distribution estimation and change detection, dual learning, and online queue-based control. Specifically, we show that PLC simultaneously achieves good long-term performance, short-term queue size reduction, accurate change detection, and fast algorithm convergence. In particular, for stationary networks, PLC achieves a near-optimal $[O(\epsilon)$, $O(\log(1/\epsilon)^2)]$ utility-delay tradeoff. For non-stationary networks, \plc{} obtains an $[O(\epsilon), O(\log^2(1/\epsilon)$ $+ \min(\epsilon^{c/2-1}, e_w/\epsilon))]$ utility-backlog tradeoff for distributions that last $\Theta(\frac{\max(\epsilon^{-c}, e_w^{-2})}{\epsilon^{1+a}})$ time, where $e_w$ is the prediction accuracy and $a=\Theta(1)>0$ is a constant (the Backpressue algorithm \cite{neelynowbook} requires an $O(\epsilon^{-2})$ length for the same utility performance with a larger backlog). Moreover, PLC detects distribution change $O(w)$ slots faster with high probability ($w$ is the prediction size) and achieves an $O(\min(\epsilon^{-1+c/2}, e_w/\epsilon)+\log^2(1/\epsilon))$ convergence time. Our results demonstrate that state prediction (even imperfect) can help (i) achieve faster detection and convergence, and (ii) obtain better utility-delay tradeoffs

Proactive Data Download and User Demand Shaping for Data Networks

In this work, we propose and study optimal proactive resource allocation and demand shaping for data networks. Motivated by the recent findings on the predictability of human behavior patterns in data networks, and the emergence of highly capable handheld devices, our design aims to smooth out the network traffic over time and minimize the data delivery costs. Our framework utilizes proactive data services as well as smart content recommendation schemes for shaping the demand. Proactive data services take place during the off-peak hours based on a statistical prediction of a demand profile for each user, whereas smart content recommendation assigns modified valuations to data items so as to render the users' demand less uncertain. Hence, our recommendation scheme aims to boost the performance of proactive services within the allowed flexibility of user requirements. We conduct theoretical performance analysis that quantifies the leveraged cost reduction through the proposed framework. We show that the cost reduction scales at the same rate as the cost function scales with the number of users. Further, we prove that \emph{demand shaping} through smart recommendation strictly reduces the incurred cost even below that of proactive downloads without recommendation