An online analytical processing multi-dimensional data warehouse for malaria data

Malaria is a vector-borne disease that contributes substantially to the global burden of morbidity and mortality. The management of malaria-related data from heterogeneous, autonomous, and distributed data sources poses unique challenges and requirements. Although online data storage systems exist that address specific malaria-related issues, a globally integrated online resource to address different aspects of the disease does not exist. In this article, we describe the design, implementation, and applications of a multidimensional, online analytical processing data warehouse, named the VecNet Data Warehouse (VecNet-DW). It is the first online, globally-integrated platform that provides efficient search, retrieval and visualization of historical, predictive, and static malaria-related data, organized in data marts. Historical and static data are modelled using star schemas, while predictive data are modelled using a snowflake schema. The major goals, characteristics, and components of the DW are described along with its data taxonomy and ontology, the external data storage systems and the logical modelling and physical design phases. Results are presented as screenshots of a Dimensional Data browser, a Lookup Tables browser, and a Results Viewer interface. The power of the DW emerges from integrated querying of the different data marts and structuring those queries to the desired dimensions, enabling users to search, view, analyse, and store large volumes of aggregated data, and responding better to the increasing demands of users

Monitoring spread of epidemic diseases by using clinical data from multiple hospitals: a data warehouse approach

A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Master’s in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyMany countries apply data science techniques to enhance their health sectors and the surveillance of diseases. The success of the innovations lies on the availability and quality of datasets to be analyzed. In Tanzania, while different Hospital Management Information Systems (HoMIS) like the Government of Tanzania Hospital Management Information System (GoTHoMIS) are installed in various hospitals, the data stored in the systems are not integrated. This causes unavailability of high quality, timely, anonymous, harmonized, and integrated datasets that can be shared and exhaustively analyzed for epidemic diseases surveillance. This study intended to develop a data warehouse to host patients’ demographic and clinical particulars essential for epidemic diseases surveillance from a multi-node GoT-HoMIS, and yield an integrated dataset that can be used for epidemic diseases surveillance. Interviews were conducted in three strategic health facilities and the Ministry responsible for Health in Tanzania. Documents were reviewed, and observation done on the patient’s registration process in the GoT-HoMIS. Thereafter, a data warehouse was developed to run under MariaDB database server, and using Hypertext Preprocessor an Extract, Transform, and Load (ETL) module was developed. The ETL module was deployed at six health facilities, and the resulting integrated dataset of 152 104 facts was visualized by using FusionCharts libraries. The study demonstrates a novel means to extract data straight from the GoT-HoMIS nodes, which has the potential to make available and provide timely data and integrated reports for decision-making on epidemics. By scaling the innovation to other health facilities, epidemics surveillance can be significantly enhanced

31th International Conference on Information Modelling and Knowledge Bases

Information modelling is becoming more and more important topic for researchers, designers, and users of information systems.The amount and complexity of information itself, the number of abstractionlevels of information, and the size of databases and knowledge bases arecontinuously growing. Conceptual modelling is one of the sub-areas ofinformation modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers

Health information systems integration : a data warehouse architecture model for the Ministry of Health in Mozambique

This thesis presents a study about a data warehouse architecture model for the Ministry of Health (MoH) in Mozambique. The study combines two areas: health care and information systems areas. It was conducted using two research methodologies: system development and case study of the Ministry of Health. The model aims at integrating data from different sources in the Ministry of Health. The fieldwork was carried out in the southern area of Mozambique, in the Ministry of Health in Maputo province and in two districts of Gaza province: Manjacaze and Chibuto. The choice of these sites for the fieldwork was based on the implementation of the District Health Information System project. This research is a contribution to the current process of data and information integration in the Ministry of Health. Integration of information and data warehouse (DW) technology are tools that offer resources to obtain managerial information needed to establish control over management process. Data warehousing aims at providing, managing and exploiting a set of integrated data for decision support within an organization. The research methods applied to this study include interviews, observations, questionnaires, document analysis and analysis of existing systems. During the development of the model I applied part of the data warehouse life cycle. Taking into account the system development phases, I covered the first three phases of the data warehouse life cycle, in order to document the existing legacy systems, to create a model of data warehouse and to clean the data. As a result, I present a data warehouse architecture model for the Ministry of Health. The proposed model integrates different heterogeneous systems and provides integrated information for health workers m(administrative personnel and managers). The empirical findings proved that for the data warehouse project the Ministry of Health will need to put more effort on the data quality control, because the quality of data influence the decision-making process

Interventions to improve access to medicine in developing countries: mapping WHO’s building blocks and supply chain functions

Access to medicine remains poor and inequitable in many low- and middle-income countries (LMICs). This is a complex and multi-dimensional issue calling for holistic solutions. Studies in this area focus on singular disciplines, highlighting one or two main issues; this paper seeks to consider this issue from a multi-disciplinary perspective. It first enumerates the supply chain bottlenecks which lead to poor access to medicine. Since access is dependent on a host of factors, it is critical to understand each of these in the context of LMICs. Secondly, the paper proposes interventions to improve access by focusing on availability, affordability, quality and obtainability of medicine. These interventions are categorised into broader areas of focus to help stakeholders understand their role and responsibilities across the supply chain functions. Finally, the paper establishes a rationale for each intervention, matching it to a WHO Building Block and the corresponding supply chain management function. The resulting map will allow stakeholders to envision policies that will contribute to comprehensive solutions that strengthen the public health supply chains in LMIC

Point-of-Care Detection Devices for Healthcare

With recent technological advances in multiple research fields such as materials science, micro-/nano-technology, cellular and molecular biology, bioengineering and the environment, much attention is shifting toward the development of new detection tools that not only address needs for high sensitivity and specificity but fulfil economic, environmental, and rapid point-of-care needs for groups and individuals with constrained resources and, possibly, limited training. Miniaturized fluidics-based platforms that precisely manipulate tiny body fluid volumes can be used for medical, healthcare or even environmental (e.g., heavy metal detection) diagnosis in a rapid and accurate manner. These new detection technologies are potentially applicable to different healthcare or environmental issues, since they are disposable, inexpensive, portable, and easy to use for the detection of human diseases or environmental issues—especially when they are manufactured based on low-cost materials, such as paper. The topics in this book (original and review articles) would cover point-of-care detection devices, microfluidic or paper-based detection devices, new materials for making detection devices, and others

Study on comparison of biochemistry between Trogoderma granarium Everts and Trogoderma variabile Ballion

Stored grains are paramount commodities to be preserved and stocked for future supply to the market according to the requirement. However, one of the major problems during storage is insect pests, of which insects from Trogoderma sp. especially khapra beetle (Trogoderma granarium) is considered the world most dangerous stored grain insect pests. Therefore, it has been listed as quarantine insect pests in many counties. For timely management of quarantine pest, effective and rapid diagnostic methods are required. Until now, diagnostic technology is mainly based on morphology of insects which require trained taxonomists. Recently, diagnostics based on metabolites and hyperspectral imaging coupled with machine learning is gaining importance. However, very little is known about the metabolites in Trogoderma sp. and how the host grain, gender, and geographical distribution affect the metabolomic profiling in these species is still unknown. In this thesis, volatile organic compounds (VOCs) emitted by Trogoderma variabile at different life stages were analysed as biomarkers which can help us to understand the biochemistry and metabolomic. Some compounds were identified from T. variabile different stages, which could be used as diagnostic tool for this insect. Gas chromatography coupled to mass spectrometry (GC–MS) was used as a technique to study the metabolite profile of T. variabile in different host grains. However, there are several factors that affect the volatile organic compounds including extraction time and number of insects. The results indicated that the optimal number of insects required for volatile organic compounds (VOC) extraction at each life stage was 25 and 20 for larvae and adults respectively. Sixteen hours were selected as the optimal extraction time for larvae and adults. Some of the VOCs compounds identified from this insect can be used as biomarkers such as pentanoic acid; diethoxymethyl acetate; 1-decyne; naphthalene, 2-methyl-; n-decanoic acid; dodecane, 1-iodo- and m-camphorene from larvae. While butanoic acid, 2-methyl-; pentanoic acid; heptane, 1,1'-oxybis- 2(3H)-Furanone, 5-ethyldihydro-; pentadecane, 2,6,10-trimethyl-; and 1,14-tetradecanediol VOCs, were found in male, whereas pentadecane; nonanic acid; pentadecane, 2,6,10-trimethyl-; undecanal and hexadecanal were identified from female. Additionaly, direct immersion-solid phase microextraction (DI-SPME) was employed, followed by gas chromatography mass spectrometry analysis (GC-MS) for the collection, separation, and identification of the chemical compounds from T. variabile adults fed on four different host grains. Results showed that insect host grains have a significant difference on the chemical compounds that were identified from female and male. There were 23 compounds identified from adults reared on canola and wheat. However, there were 26 and 28 compounds detected from adults reared on oats and barley respectively. Results showed that 11-methylpentacosane; 13-methylheptacosane; heptacosane; docosane, 1-iodo- and nonacosane were the most significant compounds that identified form T. variabile male reared on different host grains. However, the main compounds identified from female cultured on different host grains include docosane, 1-iodo-; 1-butanamine, N-butyl-; oleic acid; heptacosane; 13-methylheptacosane; hexacosane; nonacosane; 2-methyloctacosane; n-hexadecanoic acid and docosane. A novel diagnostic tool to identify between T. granarium and T. variabile were developed using visible near infrared hyperspectral imaging and deep learning models including Convolutional Neural Networks (CNN) and Capsule Network. Ventral orientation showed a better accuracy over dorsal orientation of the insects for both larvae and adult stages. This technology offers a new approach and possibility of an effective identification of T. granarium and T. variabile. from its body fragments and larvae skins. The results showed high accuracy to identify between T. granarium and T. variabile. The accuracy was 93.4 and 96.2% for adults and larvae respectively, and the accuracies of 91.6, 91.7 and 90.3% were achieved for larvae skin, adult fragments, larvae fragment respectively