Spatial autocorrelation analysis of health care hotspots in Taiwan in 2006

<p>Abstract</p> <p>Background</p> <p>Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions. These analytical approaches can be used to not only identify the location of such hotspots, but also their spatial patterns.</p> <p>Methods</p> <p>In this study, we utilize spatial autocorrelation methodologies, including Global Moran's I and Local Getis-Ord statistics, to describe and map spatial clusters, and areas in which these are situated, for the 20 leading causes of death in Taiwan. In addition, we use the fit to a logistic regression model to test the characteristics of similarity and dissimilarity by gender.</p> <p>Results</p> <p>Gender is compared in efforts to formulate the common spatial risk. The mean found by local spatial autocorrelation analysis is utilized to identify spatial cluster patterns. There is naturally great interest in discovering the relationship between the leading causes of death and well-documented spatial risk factors. For example, in Taiwan, we found the geographical distribution of clusters where there is a prevalence of tuberculosis to closely correspond to the location of aboriginal townships.</p> <p>Conclusions</p> <p>Cluster mapping helps to clarify issues such as the spatial aspects of both internal and external correlations for leading health care events. This is of great aid in assessing spatial risk factors, which in turn facilitates the planning of the most advantageous types of health care policies and implementation of effective health care services.</p

Typhoid Fever and Its Association with Environmental Factors in the Dhaka Metropolitan Area of Bangladesh: A Spatial and Time-Series Approach

Typhoid fever is a major cause of death worldwide with a major part of the disease burden in developing regions such as the Indian sub-continent. Bangladesh is part of this highly endemic region, yet little is known about the spatial and temporal distribution of the disease at a regional scale. This research used a Geographic Information System to explore, spatially and temporally, the prevalence of typhoid in Dhaka Metropolitan Area (DMA) of Bangladesh over the period 2005-9. This paper provides the first study of the spatio-temporal epidemiology of typhoid for this region. The aims of the study were: (i) to analyse the epidemiology of cases from 2005 to 2009; (ii) to identify spatial patterns of infection based on two spatial hypotheses; and (iii) to determine the hydro-climatological factors associated with typhoid prevalence. Case occurrences data were collected from 11 major hospitals in DMA, geocoded to census tract level, and used in a spatio-temporal analysis with a range of demographic, environmental and meteorological variables. Analyses revealed distinct seasonality as well as age and gender differences, with males and very young children being disproportionately infected. The male-female ratio of typhoid cases was found to be 1.36, and the median age of the cases was 14 years. Typhoid incidence was higher in male population than female (χ2 = 5.88, p0.05). A statistically significant inverse association was found between typhoid incidence and distance to major waterbodies. Spatial pattern analysis showed that there was a significant clustering of typhoid distribution in the study area. Moran\u27s I was highest (0.879; p<0.01) in 2008 and lowest (0.075; p<0.05) in 2009. Incidence rates were found to form three large, multi-centred, spatial clusters with no significant difference between urban and rural rates. Temporally, typhoid incidence was seen to increase with temperature, rainfall and river level at time lags ranging from three to five weeks. For example, for a 0.1 metre rise in river levels, the number of typhoid cases increased by 4.6% (95% CI: 2.4-2.8) above the threshold of 4.0 metres (95% CI: 2.4-4.3). On the other hand, with a 1°C rise in temperature, the number of typhoid cases could increase by 14.2% (95% CI: 4.4-25.0)

Performance of the Whale Optimization Algorithm in Space Steel Frame Optimization Problems

6th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2020, IstanbulFrame optimization that contains highly non-linear and irregular functions and discrete design variables is one of the most challenging optimization problems. Therefore, gradient-based optimization techniques cannot be successful in such problems. Metaheuristic techniques, especially population-based metaheuristic techniques, perform highly effective in solving the frame optimization problem. However, stochastic processes’ performances included in metaheuristic techniques vary based on the problem. Accordingly, researches on the performance of novel metaheuristic techniques on challenging engineering problems continue. One of the novel metaheuristic techniques is the whale optimization algorithm (WOA) which is inspired by the bubble-net feeding behavior of humpback whales. The aim of this study is testing the performance of WOA for space steel frame optimization problems. For this purpose, WOA-cased frame optimization program will be developed. Benchmark frame structures are selected to compare optimum solutions with literature results. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.No sponso

Transportation path assignment within the airports in Turkey

No sponso

Strategic Network Restoration

Disaster management, Infrastructure repair, Connectivity, Cost, Project scheduling,

Assessing the Reliability of Street Networks: A Case Study Based on the Swiss Street Network

The protection of critical infrastructure such as power grids, water supply, and street networks, has become a priority of homeland security. In this chapter, we propose a decision support system that enables the assessment of the reliability of street networks based on publicly available data. The system consists of three main components: a graph construction tool that transforms OpenStreetMap data into a directed graph, a traffic estimator that defines the traffic volume between origin-destination pairs, and an optimization model that determines an optimal flow of traffic from the origins to the respective destinations. To demonstrate the applicability of the proposed system, we apply it to the nation-wide street network of Switzerland. We also discuss how this system may apply to the analysis of train networks and point to opportunities for future research