International Journal of Logistics Systems and Management, Volume 11, Issue 1, 2012
“With malaria emerging as one of the deadliest infectious diseases for young children and women in Sub-Saharan African countries, a growing number of healthcare organisations and government authorities have increased their relentless efforts to control malaria epidemics in Africa. One of the causes for malaria epidemics is the lack of accessibility to anti-malarial drugs that results from archaic logistics infrastructure, inefficient distribution channels and disruptive black markets in Africa. This paper identifies factors that either enhance or hinder the accessibility of anti-malarial drugs to African population sectors vulnerable to malaria epidemics. In addition, it develops a comprehensive supply chain map that reveals the labyrinths of the African logistics infrastructure, distribution channels, government regulations and business customs. Based on this supply chain map, this paper proposes various supply chain strategies that improve the access of anti-malarial drugs and reduce the possibility of drug supply chain disruptions.”
Dissertaion, Doctor of Philosophy, Biostatistics, Emory University, 2011
Jeffrey M. Switchenko
“Infectious disease models attempt to evaluate the effects on the spread and transmission of disease. One particular model, the susceptible-infected-recovered (SIR) model, places individuals into classes of disease progression, where a series of differential equations tracks the rates of transmission and recovery for a given disease through a susceptible population. Two parameters, the transmission parameter and the recovery parameter, drive the dynamics of the model, and their ratio, R0, is the average number of cases caused by one infectious individual within a completely susceptible population. R0 is seen as one of the most important quantities in the study of epidemics, and signals how quickly a particular disease can spread amongst a susceptible population. Previous analyses have focused primarily on tracking these epidemic disease parameters over time, and classifying individuals due to baseline differences which reflect heterogeneity within the population. For example, these differences can be based on age, gender, vaccination status, or behavior.
Estimates for R0 across Baltimore using the following set of xed R0 values: f1:0; 1:1; 1:2g, f1:3; 1:4; 1:5g, f1:6; 1:7; 1:8g, f1:9; 2:0; 2:1g
“However, we choose to quantify the spatial heterogeneity that exists in spatially-referenced data in an effort to define core areas of disease rates and transmission. We first consider geographically weighted regression (GWR) models in an effort to assess the spatial variability that exists between disease rates and baseline tract- level characteristics which can define core disease areas. Next, we build hierarchical Bayesian models which incorporate random effects structures, inducing correlation in local estimates of disease transmission with exchangeable random effects, which smooth local estimates based on global averages, and conditionally autoregressive (CAR) random effects, which smooth local estimates based on neighboring estimates. We extend a chain binomial model to predict the spread of disease, while considering two different parameterizations of the chain binomial model, and simulate outbreaks to assess model performance. In addition, we extend a general epidemic model, which incorporates aspects of frailty models in assessing heterogeneity within the population. Through our modeling approaches, we are able to identify cores areas for the transmission of sexually transmitted infections (STIs) in Baltimore, Maryland from 2002-05.”
Geophysical Prospecting, published online 09 January 2012
Matthieu Bourges, Jean-Luc Mari, and Nicolas Jeannée
“Nowadays, geostatistics is commonly applied for numerous gridding or modelling tasks. However, it is still under used and unknown for classical geophysical applications. This paper highlights the main geostatistical methods relevant for geophysical issues, for instance to improve the quality of seismic data such as velocity cubes or interpreted horizons. These methods are then illustrated through four examples. The first example, based on a gravity survey presents how a geostatistical interpolation can be used to filter out a global trend, in order to better define real anomalies. In the second case study, dedicated to refraction surveying, geostatistical filtering is used to filter out acquisition artefacts and identify the main geological structures. The third one is an example of porosity being integrated geostatistically with a seismic acoustic impedance map. The last example deals with geostatistical time to depth conversion; the interest of performing geostatistical simulations is finally discussed.”
Proceedings of the Eighth International Space Syntax Symposium, Santiago, PUC, 2012
Safoora MOKHTARZADEH, Mostafa ABBASZADEGAN, and Omid RISMANCHIAN
“This research aims to study the relation between spatial structure and sustainable development level with the case of Mashhad, a city at the north‐east of Iran. The literature suggests that there is a positive relation between socio‐economic processes and the spatial form in a city, thus in order to comprehend socio‐economic processes, understanding the spatial form of the city is essential. Also the socio‐economic relations in different parts of a city can indicate sustainable development level of the areas by which the development indicators could be assessed. In order to study this relation, 136 neighbourhoods in Mashhad have been examined in which space syntax is used to study the spatial structure of the city and factor analysis is used to identify sustainable development level. In this study 20 indicators in different subjects including social, economic, physical, environmental, and welfare are combined and are considered in the analysis as the indicator of the quality development.
Mashhad socio‐economic condition (Farnahad, 2009)
“The results suggest that there is a positive correlation between local integration and integration r‐r with the changes in sustainable development level; however, this is not the case for global integration. Thus, one of the main reasons for having inequality in socio‐economic conditions in different parts of the city could be a heterogeneous spatial structure in the city.”