On Time Series Analysis of Public Health and Biomedical Data

A time series is a sequence of observations made over time. Examples in public health include daily ozone concentrations, weekly admissions to an emergency department or annual expenditures on health care in the United States. Time series models are used to describe the dependence of the response at each time on predictor variables including covariates and possibly previous values in the series. Time series methods are necessary to account for the correlation among repeated responses over time. This paper gives an overview of time series ideas and methods used in public health research.

A call for uniform reporting standards in studies assessing endovascular treatment for chronic ischaemia of lower limb arteries

Endovascular therapy is a rapidly evolving field for the treatment of patients with peripheral arterial disease, and a magnitude of studies reporting on various modern revascularization concepts have been recently published. Thus, studies assessing the efficacy of endovascular therapy of peripheral arteries do not operate with uniformly defined endpoints, rendering a direct comparison of studies difficult. The purpose of this consensus statement is to highlight differences in the terminology used in the current literature and to propose some standardized criteria that must be considered when reporting results of endovascular revascularization for chronic ischaemia of lower limb arteries.

Price risk management in the copper market using commodity derivatives and options strategies

Metals price risk management is a key issue related to financial risk in metal markets because of uncertainty of commodity price fluctuation, exchange rate, interest rate changes and huge price risk either to metals’ producers or consumers. Thus, it has been taken into account by all participants in metal markets including metals’ producers, consumers, merchants, banks, investment funds, speculators, traders and so on. Managing price risk provides stable income for both metals’ producers and consumers, so it increases the chance that a firm will invest in attractive projects. The purpose of this research is to evaluate risk management strategies in the copper market. The main tools and strategies of price risk management are hedging and other derivatives such as futures contracts, swaps and options contracts. Hedging is a transaction designed to reduce or eliminate price risk. Derivatives are financial instruments, whose returns are derived from other financial instruments and they are commonly used for managing financial risks. Although derivatives have been around in some form for centuries, their growth has accelerated rapidly during the last 20 years. Nowadays, they are widely used by financial institutions, corporations, professional investors, and individuals. This project is focused on the over-the-counter (OTC) market and its products such as exotic options, particularly Asian options. The first part of the project is a description of basic derivatives and risk management strategies. In addition, this part discusses basic concepts of spot and futures (forward) markets, benefits and costs of risk management and risks and rewards of positions in the derivative markets. The second part considers valuations of commodity derivatives. In this part, the options pricing model DerivaGem is applied to Asian call and put options on London Metal Exchange (LME) copper because it is important to understand how Asian options are valued and to compare theoretical values of the options with their market observed values. Predicting future trends of copper prices is important and would be essential to manage market price risk successfully. Therefore, the third part is a discussion about econometric commodity models. Based on this literature review, the fourth part of the project reports the construction and testing of an econometric model designed to forecast the monthly average price of copper on the LME. More specifically, this part aims at showing how LME copper prices can be explained by means of a simultaneous equation structural model (two-stage least squares regression) connecting supply and demand variables. A simultaneous econometric model for the copper industry is built: {█(Q_t^D=e^((-5.0485))∙P_((t-1))^((-0.1868) )∙〖GDP〗_t^((1.7151) )∙e^((0.0158)∙〖IP〗_t ) @Q_t^S=e^((-3.0785))∙P_((t-1))^((0.5960))∙T_t^((0.1408))∙P_(OIL(t))^((-0.1559))∙〖USDI〗_t^((1.2432))∙〖LIBOR〗_((t-6))^((-0.0561))@Q_t^D=Q_t^S )┤ P_((t-1))^CU=e^((-2.5165))∙〖GDP〗_t^((2.1910))∙e^((0.0202)∙〖IP〗_t )∙T_t^((-0.1799))∙P_(OIL(t))^((0.1991))∙〖USDI〗_t^((-1.5881))∙〖LIBOR〗_((t-6))^((0.0717) Where, Q_t^D and Q_t^Sare world demand for and supply of copper at time t respectively. P(t-1) is the lagged price of copper, which is the focus of the analysis in this part. GDPt is world gross domestic product at time t, which represents aggregate economic activity. In addition, industrial production should be considered here, so the global industrial production growth that is noted as IPt is included in the model. Tt is the time variable, which is a useful proxy for technological change. A proxy variable for the cost of energy in producing copper is the price of oil at time t, which is noted as POIL(t ) . USDIt is the U.S. dollar index variable at time t, which is an important variable for explaining the copper supply and copper prices. At last, LIBOR(t-6) is the 6-month lagged 1-year London Inter bank offering rate of interest. Although, the model can be applicable for different base metals' industries, the omitted exogenous variables such as the price of substitute or a combined variable related to the price of substitutes have not been considered in this study. Based on this econometric model and using a Monte-Carlo simulation analysis, the probabilities that the monthly average copper prices in 2006 and 2007 will be greater than specific strike price of an option are defined. The final part evaluates risk management strategies including options strategies, metal swaps and simple options in relation to the simulation results. The basic options strategies such as bull spreads, bear spreads and butterfly spreads, which are created by using both call and put options in 2006 and 2007 are evaluated. Consequently, each risk management strategy in 2006 and 2007 is analyzed based on the day of data and the price prediction model. As a result, applications stemming from this project include valuing Asian options, developing a copper price prediction model, forecasting and planning, and decision making for price risk management in the copper market.

Comparison between two caller groups of a medical call centre in Switzerland

OBJECTIVES: The incidence distribution of triage advice in the medical call centre Medi24 and the pattern of service utilisation were analysed with respect to two groups of callers with different insurance schemes. Individuals having contracted insurance of the Medi24 model could use the telephone consultation service of the medical call centre Medi24 (mainly part of the mandatory basic health insurance) voluntarily and free of charge whereas individuals holding an insurance policy of the Telmed model (special contract within the mandatory basic health insurance with a premium discount ranging from 8% to 12%) were obliged to have a telephone consultation before arranging an appointment with a medical doctor. METHODS: A cross-sectional study was carried out in the medical call centre Medi24 based on all triage datasets of the Medi24 and Telmed groups collected during the one year period from July 1st 2005 to June 30th 2006. The distribution of the six different urgency levels within the two groups and their respective pattern of service utilisation was determined. In a multivariable logistic regression model the Odds Ratio for every enquiry originating from the Telmed group versus those originating from the Medi24 group was calculated. RESULTS: During a one-year period 48 388 triage requests reached the medical call centre Medi24, 56% derived from the Telmed group and 44% from the Medi24 group. Within the Medi24 group more than 25% of the individuals received self-care advice, within the Telmed group, on the other hand, only about 18% received such advice. In contrast, 27% of the Telmed triage requests but only 18% of the Medi24 triage requests resulted in the advice to make a routine appointment with a medical doctor. The probability that an individual of the Telmed group obtained the advice to go to the accident and emergency department was lower than for an individual of the Medi24 group (OR 0.77, 95% CI 0.60-0.99). Likewise, the probability of self-care advice was decreased in regard to the Medi24 group (OR 0.80, 95% CI 0.75-0.85). However, regarding the advice to make a routine appointment with a medical doctor, the Telmed group was represented more frequently than the Medi24 group (OR 1.36, 95% CI 1.28-1.44). CONCLUSION: In respect of the triage advice, the Telmed group differed significantly from the Medi24 group within all urgency levels. The differences between the two groups in respect of the advice given were still less pronounced than expected against the background of their different contract conditions and the disparate temporal pattern of utilisation. We interprete this finding with the fact that appraising the urgency of health problems appropriately seems to be very difficult for the majority of people seeking advice.

MODULARITY BASED FUZZY COMMUNITY DETECTION IN SOCIAL NETWORKS

Fuzzy community detection is to identify fuzzy communities in a network, which are groups of vertices in the network such that the membership of a vertex in one community is in [0,1] and that the sum of memberships of vertices in all communities equals to 1. Fuzzy communities are pervasive in social networks, but only a few works have been done for fuzzy community detection. Recently, a one-step forward extension of Newman’s Modularity, the most popular quality function for disjoint community detection, results into the Generalized Modularity (GM) that demonstrates good performance in finding well-known fuzzy communities. Thus, GMis chosen as the quality function in our research. We first propose a generalized fuzzy t-norm modularity to investigate the effect of different fuzzy intersection operators on fuzzy community detection, since the introduction of a fuzzy intersection operation is made feasible by GM. The experimental results show that the Yager operator with a proper parameter value performs better than the product operator in revealing community structure. Then, we focus on how to find optimal fuzzy communities in a network by directly maximizing GM, which we call it Fuzzy Modularity Maximization (FMM) problem. The effort on FMM problem results into the major contribution of this thesis, an efficient and effective GM-based fuzzy community detection method that could automatically discover a fuzzy partition of a network when it is appropriate, which is much better than fuzzy partitions found by existing fuzzy community detection methods, and a crisp partition of a network when appropriate, which is competitive with partitions resulted from the best disjoint community detections up to now. We address FMM problem by iteratively solving a sub-problem called One-Step Modularity Maximization (OSMM). We present two approaches for solving this iterative procedure: a tree-based global optimizer called Find Best Leaf Node (FBLN) and a heuristic-based local optimizer. The OSMM problem is based on a simplified quadratic knapsack problem that can be solved in linear time; thus, a solution of OSMM can be found in linear time. Since the OSMM algorithm is called within FBLN recursively and the structure of the search tree is non-deterministic, we can see that the FMM/FBLN algorithm runs in a time complexity of at least O (n2). So, we also propose several highly efficient and very effective heuristic algorithms namely FMM/H algorithms. We compared our proposed FMM/H algorithms with two state-of-the-art community detection methods, modified MULTICUT Spectral Fuzzy c-Means (MSFCM) and Genetic Algorithm with a Local Search strategy (GALS), on 10 real-world data sets. The experimental results suggest that the H2 variant of FMM/H is the best performing version. The H2 algorithm is very competitive with GALS in producing maximum modularity partitions and performs much better than MSFCM. On all the 10 data sets, H2 is also 2-3 orders of magnitude faster than GALS. Furthermore, by adopting a simply modified version of the H2 algorithm as a mutation operator, we designed a genetic algorithm for fuzzy community detection, namely GAFCD, where elite selection and early termination are applied. The crossover operator is designed to make GAFCD converge fast and to enhance GAFCD’s ability of jumping out of local minimums. Experimental results on all the data sets show that GAFCD uncovers better community structure than GALS.

Data-driven estimates of the number of clusters in multivariate time series

An important problem in unsupervised data clustering is how to determine the number of clusters. Here we investigate how this can be achieved in an automated way by using interrelation matrices of multivariate time series. Two nonparametric and purely data driven algorithms are expounded and compared. The first exploits the eigenvalue spectra of surrogate data, while the second employs the eigenvector components of the interrelation matrix. Compared to the first algorithm, the second approach is computationally faster and not limited to linear interrelation measures.

Call for a reference model of chronic hind limb ischemia to investigate therapeutic angiogenesis

A large number of studies utilize animal models to investigate therapeutic angiogenesis. However, the lack of a standardized experimental model leaves the comparison of different studies problematic. To establish a reference model of prolonged moderate tissue ischemia, we created unilateral hind limb ischemia in athymic rnu-rats by surgical excision of the femoral vessels. Blood flow of the limb was monitored for 60 days by laser Doppler imaging. Following a short postoperative period of substantially depressed perfusion, the animals showed a status of moderate hind limb ischemia from day 14 onwards. Thereafter, the perfusion remained at a constant level (55.5% of normal value) until the end of the observation period. Histopathological assessment of the ischemic musculature on postoperative days 28 and 60 showed essentially no inflammatory cell infiltrate or fibrosis. However, the mitochondrial activity and capillary-to-fiber ratio of the muscular tissue was reduced to 52.7% of normal, presenting with a significant weakness of the ischemic limb evidenced by a progressive decline in performance. Intramuscular injection of culture-expanded human endothelial progenitor cells (EPC) resulted in a significant increase in blood flow (82.0+/-3.5% of normal), capillary density (1.60+/-0.08/muscle fiber) and smooth muscle covered arterioles (8.0+/-0.6/high power field) in the ischemic hind limb as compared to controls (55.0+/-3.1%; 0.99+/-0.03; 5.0+/-0.2). In conclusion, chronic, moderate hind limb ischemia with consistently reduced perfusion levels persisting over a prolonged period can be established reliably in rnu athymic nude rats and is responsive to pro-angiogenic treatments such as EPC transplantation. This study provides a detailed protocol of a highly reproducible reference model to test novel therapeutic options for limb ischemia.