Pairwise accelerated failure time models for infectious disease transmission with external sources of infection

Pairwise survival analysis handles dependent happenings in infectious disease transmission data by analyzing failure times in ordered pairs of individuals. The contact interval in the pair $ij$ is the time from the onset of infectiousness in $i$ to infectious contact from $i$ to $j$, where an infectious contact is sufficient to infect $j$ if he or she is susceptible. The contact interval distribution determines transmission probabilities and the infectiousness profile of infected individuals. Many important questions in infectious disease epidemiology involve the effects of covariates (e.g., age or vaccination status) on transmission. Here, we generalize earlier pairwise methods in two ways: First, we introduce an accelerated failure time model that allows the contact interval rate parameter to depend on infectiousness covariates for $i$, susceptibility covariates for $j$, and pairwise covariates. Second, we show how internal infections (caused by individuals under observation) and external infections (caused environmental or community sources) can be handled simultaneously. In simulations, we show that these methods produce valid point and interval estimates and that accounting for external infections is critical to consistent estimation. Finally, we use these methods to analyze household surveillance data from Los Angeles County during the 2009 influenza A(H1N1) pandemic.Comment: 24 pages, 4 figure

Estimating and interpreting secondary attack risk: Binomial considered harmful

The household secondary attack risk (SAR), often called the secondary attack rate or secondary infection risk, is the probability of infectious contact from an infectious household member A to a given household member B, where we define infectious contact to be a contact sufficient to infect B if he or she is susceptible. Estimation of the SAR is an important part of understanding and controlling the transmission of infectious diseases. In practice, it is most often estimated using binomial models such as logistic regression, which implicitly attribute all secondary infections in a household to the primary case. In the simplest case, the number of secondary infections in a household with m susceptibles and a single primary case is modeled as a binomial(m, p) random variable where p is the SAR. Although it has long been understood that transmission within households is not binomial, it is thought that multiple generations of transmission can be safely neglected when p is small. We use probability generating functions and simulations to show that this is a mistake. The proportion of susceptible household members infected can be substantially larger than the SAR even when p is small. As a result, binomial estimates of the SAR are biased upward and their confidence intervals have poor coverage probabilities even if adjusted for clustering. Accurate point and interval estimates of the SAR can be obtained using longitudinal chain binomial models or pairwise survival analysis, which account for multiple generations of transmission within households, the ongoing risk of infection from outside the household, and incomplete follow-up. We illustrate the practical implications of these results in an analysis of household surveillance data collected by the Los Angeles County Department of Public Health during the 2009 influenza A (H1N1) pandemic.Comment: 25 pages, 8 figure

Knowledge, Attitude and Practice on Smoking Among Students and Staff in Universiti Putra Malaysia

The aim of this study was to determine the prevalence of smoking and factors associated with smoking and to determine the knowledge, attitudes, practices on antismoking measures related to smoking among students and staff of Universiti Putra Malaysia (UPM), Serdang, Selangor, Malaysia. A cross-sectional study design was used. A multistage stratified proportionate to size sampling technique was used to select the sample. The list of students and staff of UPM served as sampling frame. The total population for this study was 28053. Sample size was 2364 and was computed using EPI - INFO. Data was collected from 5th July to 27" August 2004 using a structured pre-tested questionnaire. The response rate was 85%. Out of the 2008 respondents, 60% were female, 62.8% Malay and 67.2% were Muslims. The overall prevalence of ever and current smokers amongst students and staff was 13.7% and 9.9%, respectively. The prevalence of ever and current smokers among male was 30% and 21.8%; and for the female was 2.8% and 2%. The prevalence of ever and current smokers among students was 12.1% and 8.9%; and staff was 26% and 17.7%, respectively. The mean initiation age of smoking was 16.7 + SD 3.7 years and it was lowest among Malays (16.3 years) and diploma level students (15 years). Prevalence of current smokers was high among Indians (12.7%) and Malays (1 1.6%) ethnic groups; and, Hindus (13%) and Muslim (1 1.9%) religious groups. Technicians had the highest (31.8%) current smoking prevalence at the UPM. Just for fim (54.2%) was the main reason for starting smoking and residence (45.1%) was the favourite place for smoking. Amongst current smokers, 63.9% had low level of addiction to nicotine. The prevalence of smoking was associated with age, economic status, race, religious, family and peer groups smoking habits. Most students and staff had good knowledge on the health risk of smoking. Never smokers had better knowledge on hazards of smoking and more positive attitudes. In conclusion, UPM smoking prevalence is low as compared to the national prevalence. However, it still constitutes a problem among university students and staff in UPM, in spite of their knowledge of its hazards, attitude and practices. There is a need to implement an anti-smoking program for university students and staff

Modeling Zero-Inflated and Overdispersed Count Data With Application to Psychiatric Inpatient Service Use

Psychiatric disorders can be characterized as behavioral or mental states that cause significant distress and impaired personal functioning. Such disorders may occur as a single episode or persistent, relapsing, and perhaps leading to suicidal behaviours. The exact causes of psychiatric disorders are hard to determine but easy access to health care services can help to reduce the severity of the states. Inpatient psychiatric hospitalization is not only an expensive mode of treatment but also may represent the quality of health care system. The aim of this study was to investigate the factors associated with repeated hospitalizations among the patients with psychiatric illness, which may help the policy makers to target the high-risk groups in a more focused manner. The count of hospitalizations for psychiatric patients may be zero during a period of time for the huge majority of patients rather than a positive count. A common strategy to handle excessive zeros is to use zero-inflated models or hurdle models. In the field of health services research of mental health, very little literature is available comparing the relative fits of zero-inflated distributions and other count distributions to empirical data. A large linked administrative database consisting of 200,537 patients with psychiatric diagnosis in the years of 2008-2012 was used in this thesis. Various counts regression models were considered for analyzing the hospitalization rate among patients with psychiatric disorders within 3, 6 and 9 months follow-up since index visit date. The covariates for this study consist of sociodemographic and clinical characteristics of the patients. According to the Akaike Information Criteria, Vuong’s test and randomized quantile residuals, the hurdle negative binomial model was the best model. Our results showed that hospitalization rate depends on the patients’ socio-demographic characteristics and also on disease types. It also showed that having previously visited a general physician served a protective role for psychiatric hospitalization during our study period. Patients who had seen an outpatient psychiatrist were more likely to have a higher number of psychiatric hospitalizations. This may indicate that psychiatrists tend to see patients with more severe illnesses, who require hospital-based care for managing their illness. Having earlier and greater access to outpatient psychiatrist and community-based mental health care may alleviate the need for hospital-based psychiatric care

Water Entry Impact Dynamics of Diving Birds

Some seabirds (such as Northern Gannets and Brown Boobies) can dive from heights as high as 30 m reaching speeds of up to 24 m/s as they impact the water surface. It is perceived that physical geometry, particularly of the beak, allows them to endure relatively high impact forces that could otherwise kill non-diving birds. Acceleration data from simplified models of diving birds agree with simulated data for one species (Northern Gannet), however, no reliable experimental data with real bird geometries exist for comparison purposes. This study utilizes eleven 3D printed diving birds (five plunge-diving, five surface-diving and one dipper) with embedded accelerometers to measure water-entry impact accelerations for impact velocities ranging between 4.4 - 23.2 m/s. Impact forces for all bird types are found to be comparable under similar impact conditions and well within the safe zone characterized by neck strength as found in recent studies. However, the time each bird requires to reach maximum impact acceleration and its effect represented here by the derivative of acceleration (i.e., jerk), is different based on its beak and head shape. We show that surface diving birds cannot dive at high speeds as the non-dimensional jerk experienced exceeds a safe limit estimated from human impact analysis, whereas those by plunge divers do not

The risk of misclassifying subjects within principal component based asset index.

The asset index is often used as a measure of socioeconomic status in empirical research as an explanatory variable or to control confounding. Principal component analysis (PCA) is frequently used to create the asset index. We conducted a simulation study to explore how accurately the principal component based asset index reflects the study subjects' actual poverty level, when the actual poverty level is generated by a simple factor analytic model. In the simulation study using the PC-based asset index, only 1% to 4% of subjects preserved their real position in a quintile scale of assets; between 44% to 82% of subjects were misclassified into the wrong asset quintile. If the PC-based asset index explained less than 30% of the total variance in the component variables, then we consistently observed more than 50% misclassification across quintiles of the index. The frequency of misclassification suggests that the PC-based asset index may not provide a valid measure of poverty level and should be used cautiously as a measure of socioeconomic status

Multivariate Outlier Mining Using Cluster Analysis: Case Study - National Health Interview Survey

Outlier mining is a fundamental issue in many statistical analyses, especially in multivariate cases. Outliers may exert undue influence on outcomes of the analysis. In most cases, it is a big challenge to reveal the pattern of the outliers and the outlyingness . There are several approaches and methods to detect anomalous data points in data. But no single method is perfect for every data set especially when the data dimension and volume is high. In this thesis, I review distance-based clustering methods for multivariate outlier mining and demonstrate the usefulness of it in a medical setting. Specifically, I discuss Hierarchical clustering and the multivariate methods of determining appropriate cluster(s). After mining the multivariate outliers, I examine and describe the characteristics of the variables for those outliers. Finally, I demonstrate the application of these methods using the National Health Interview Survey (NHIS) 2008 database for the purposes of studying adolescent obesity

Incremental Principal Component Analysis Based Outliers Detection Methods for Spatiotemporal Data Streams

In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA) is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams

AN ADAPTIVE FRAMEWORK FOR REAL-TIME SPATIOTEMPORAL BIG DATA ANALYTICS

Due to advancements in and widespread usage of technologies such as smartphones, satellites, smart sensors, and social networks, collection of spatiotemporal data is growing rapidly. Such massive spatiotemporal data require appropriate techniques and technologies for their efficient analysis and processing. Analyzing massive spatiotemporal data efficiently and effectively is challenging since the data changes dynamically over space and time whereas, often, decisions followed by the analysis need to be made under real-time constraints. Compared to non-spatial data, spatiotemporal data, among other unique characteristics, are multidimensional (x, y, attributes, time) in nature, complex in structures and behaviors, and provides details at different resolutions and scales. These characteristics together make analyzing and processing massive spatiotemporal data in real time a challenging task. Resorting to high-performance computing (HPC) is a common approach for handling this computing challenge but to determine optimal solutions through data and computation analysis, appropriate analytics and computing solutions are needed. In this dissertation, we proposed a framework which is basically a platform providing spatiotemporal data-intensive analytics for data- and compute-intensive applications that require computation under real-time constraints on given computing resources. The framework is a layered structure consisting of four interrelated components (layers); three on analytics and one on adaptive computing. A graph-based approach is developed as the foundation of the analytics components which are: efficient analytics – providing acceptable solutions based on current data in the absence of historical data; predictive analytics – providing near-optimal solutions by learning from the patterns of historical data and predicting based on the learning; meta-analytics – providing optimal solutions by analyzing pattern of past data patterns; and adaptive computing that ensures appropriate analytics are applied and computation is completed in real time on available computing resources

Water entry impact dynamics of diving birds

Some seabirds (such as northern gannets and brown boobies) can dive from heights as high as 30 m reaching speeds of up to 24 m s−1 as they impact the water surface. The physical geometry of plunge diving birds, particularly of the beak, allows them to limit high impact forces compared to non-diving birds. Numerically simulated data for one species (northern gannet) provides some insight into the impact forces experienced during diving, however, no reliable experimental data with real bird geometries exist for comparison purposes. This study utilizes eleven 3D printed diving bird models of three types of birds: plunge-diving (five), surface-diving (five) and dipper (one), with embedded accelerometers to measure water-entry impact accelerations for impact velocities ranging between 4.4–23.2 m s−1 . Impact forces for all bird types are found to be comparable under similar impact conditions and well within the safe zone characterized by neck strength as found in recent studies. However, the time that each bird requires to reach maximum impact acceleration from impact is different based on its beak and head shape and so is its effect, represented here by its derivative (i.e. jerk). We show that surface diving birds have high non-dimensional jerk, which exceed a safe limit estimated from human impact analysis, whereas those by plunge divers do not