scholarly journals A Semantic Framework to Improve Interoperability of Malaria Surveillance Systems

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Jon Hael Simon Brenas ◽  
Mohammad S. Al-Manir ◽  
Kate Zinszer ◽  
Christopher J. Baker ◽  
Arash Shaban-Nejad
Keyword(s):  
Semantic Web ◽  
Insecticide Resistance ◽  
Data Access ◽  
Data Sources ◽  
Surveillance Systems ◽  
Malaria Surveillance ◽  
Semantic Framework ◽  
Ontology Language ◽  
The Impact ◽  
Target Data

ObjectiveMalaria is one of the top causes of death in Africa and some other regions in the world. Data driven surveillance activities are essential for enabling the timely interventions to alleviate the impact of the disease and eventually eliminate malaria. Improving the interoperability of data sources through the use of shared semantics is a key consideration when designing surveillance systems, which must be robust in the face of dynamic changes to one or more components of a distributed infrastructure. Here we introduce a semantic framework to improve interoperability of malaria surveillance systems (SIEMA).IntroductionIn 2015, there were 212 million new cases of malaria, and about 429,000 malaria death, worldwide. African countries accounted for almost 90% of global cases of malaria and 92% of malaria deaths. Currently, malaria data are scattered across different countries, laboratories, and organizations in different heterogeneous data formats and repositories. The diversity of access methodologies makes it difficult to retrieve relevant data in a timely manner. Moreover, lack of rich metadata limits the reusability of data and its integration. The current process of discovering, accessing and reusing the data is inefficient and error-prone profoundly hindering surveillance efforts.As our knowledge about malaria and appropriate preventive measures becomes more comprehensive malaria data management systems, data collection standards, and data stewardship are certain to change regularly. Collectively these changes will make it more difficult to perform accurate data analytics or achieve reliable estimates of important metrics, such as infection rates. Consequently, there is a critical need to rapidly re-assess the integrity of data and knowledge infrastructures that experts depend on to support their surveillance tasks.MethodsIn order to address the challenge of heterogeneity of malaria data sources we recruit domain specific ontologies in the field (e.g. IDOMAL (1)) that define a shared lexicon of concepts and relations. These ontologies are expressed in the standard Web Ontology Language (OWL).To over come challenges in accessing distributed data resources we have adopted the Semantic Automatic Discovery & Integration framework (SADI) (2) to ensure interoperability. SADI provides a way to describe services that provide access to data, detailing inputs and outputs of services and a functional description. Existing ontology terms are used when building SADI Service descriptions. The services can be discovered by querying a registry and combined into complex workflows. Users can issue SPARQL syntax to a query engine which can plan complex workflows to fetch actual data, without having to know how target data is structured or where it is located.In order to tackle changes in target data sources, the ontologies or the service definitions, we create a Dashboard (3) that can report any changes. The Dashboard reuses some existing tools to perform a series of checks. These tools compare versions of ontologies and databases allowing the Dashboard to report these changes. Once a change has been identified, as series of recommendations can be made, e.g. services can be retired or updated so that data access can continue.ResultsWe used the Mosquito Insecticide Resistance Ontology (MIRO) (5) to define the common lexicon for our data sources and queries. The sources we created are CSV files that use the IRbase (4) schema. With the data defined using we specified several SPARQL queries and the SADI services needed to answer them. These services were designed to enabled access to the data separated in different files using different formats. In order to showcase the capabilities of our Dashboard, we also modified parts of the service definitions, of the ontology and of the data sources. This allowed us to test our change detection capabilities. Once changes where detected, we manually updated the services to comply with a revised ontology and data sources and checked that the changes we proposed where yielding services that gave the right answers. In the future, we plan to make the updating of the services automatic.ConclusionsBeing able to make the relevant information accessible to a surveillance expert in a seamless way is critical in tackling and ultimately curing malaria. In order to achieve this, we used existing ontologies and semantic web services to increase the interoperability of the various sources. The data as well as the ontologies being likely to change frequently, we also designed a tool allowing us to detect and identify the changes and to update the services so that the whole surveillance systems becomes more resilient.References1. P. Topalis, E. Mitraka, V Dritsou, E. Dialynas and C. Louis, “IDOMAL: the malaria ontology revisited” in Journal of Biomedical Semantics, vol. 4, no. 1, p. 16, Sep 2013.2. M. D. Wilkinson, B. Vandervalk and L. McCarthy, “The Semantic Automated Discovery and Integration (SADI) web service design-pattern, API and reference implementation” in Journal of Biomedical Semantics, vol. 2, no. 1, p. 8, 2011.3. J.H. Brenas, M.S. Al-Manir, C.J.O. Baker and A. Shaban-Nejad, “Change management dashboard for the SIEMA global surveillance infrastructure”, in International Semantic Web Conference, 20174. E. Dialynas, P. Topalis, J. Vontas and C. Louis, "MIRO and IRbase: IT Tools for the Epidemiological Monitoring of Insecticide Resistance in Mosquito Disease Vectors", in PLOS Neglected Tropical Diseases 2009

scholarly journals A Surveillance Infrastructure for Malaria Analytics: Provisioning Data Access and Preservation of Interoperability (Preprint)

2018 ◽  
Author(s):  
Mohammad Sadnan Al Manir ◽  
Jon Haël Brenas ◽  
Christopher JO Baker ◽  
Arash Shaban-Nejad
Keyword(s):  
Web Services ◽  
Semantic Web ◽  
Data Access ◽  
Data Sources ◽  
World Health ◽  
Semantic Web Services ◽  
Distributed Data ◽  
Malaria Surveillance ◽  
Management Platform ◽  
The Impact

BACKGROUND According to the World Health Organization, malaria surveillance is weakest in countries and regions with the highest malaria burden. A core obstacle is that the data required to perform malaria surveillance are fragmented in multiple data silos distributed across geographic regions. Furthermore, consistent integrated malaria data sources are few, and a low degree of interoperability exists between them. As a result, it is difficult to identify disease trends and to plan for effective interventions. OBJECTIVE We propose the Semantics, Interoperability, and Evolution for Malaria Analytics (SIEMA) platform for use in malaria surveillance based on semantic data federation. Using this approach, it is possible to access distributed data, extend and preserve interoperability between multiple dynamic distributed malaria sources, and facilitate detection of system changes that can interrupt mission-critical global surveillance activities. METHODS We used Semantic Automated Discovery and Integration (SADI) Semantic Web Services to enable data access and improve interoperability, and the graphical user interface-enabled semantic query engine HYDRA to implement the target queries typical of malaria programs. We implemented a custom algorithm to detect changes to community-developed terminologies, data sources, and services that are core to SIEMA. This algorithm reports to a dashboard. Valet SADI is used to mitigate the impact of changes by rebuilding affected services. RESULTS We developed a prototype surveillance and change management platform from a combination of third-party tools, community-developed terminologies, and custom algorithms. We illustrated a methodology and core infrastructure to facilitate interoperable access to distributed data sources using SADI Semantic Web services. This degree of access makes it possible to implement complex queries needed by our user community with minimal technical skill. We implemented a dashboard that reports on terminology changes that can render the services inactive, jeopardizing system interoperability. Using this information, end users can control and reactively rebuild services to preserve interoperability and minimize service downtime. CONCLUSIONS We introduce a framework suitable for use in malaria surveillance that supports the creation of flexible surveillance queries across distributed data resources. The platform provides interoperable access to target data sources, is domain agnostic, and with updates to core terminological resources is readily transferable to other surveillance activities. A dashboard enables users to review changes to the infrastructure and invoke system updates. The platform significantly extends the range of functionalities offered by malaria information systems, beyond the state-of-the-art.

Semantic Web and Geospatial Unique Features Based Geospatial Data Integration

2019 ◽  
pp. 230-253
Author(s):  
Ying Zhang ◽  
Chaopeng Li ◽  
Na Chen ◽  
Shaowen Liu ◽  
Liming Du ◽  
...  
Keyword(s):  
Semantic Web ◽  
Data Integration ◽  
High Performance ◽  
Data Access ◽  
Heterogeneous Data ◽  
Geospatial Data ◽  
Data Sources ◽  
Modeling Process ◽  
Translation Function ◽  
Data Source

Since large amount of geospatial data are produced by various sources and stored in incompatible formats, geospatial data integration is difficult because of the shortage of semantics. Despite standardised data format and data access protocols, such as Web Feature Service (WFS), can enable end-users with access to heterogeneous data stored in different formats from various sources, it is still time-consuming and ineffective due to the lack of semantics. To solve this problem, a prototype to implement the geospatial data integration is proposed by addressing the following four problems, i.e., geospatial data retrieving, modeling, linking and integrating. First, we provide a uniform integration paradigm for users to retrieve geospatial data. Then, we align the retrieved geospatial data in the modeling process to eliminate heterogeneity with the help of Karma. Our main contribution focuses on addressing the third problem. Previous work has been done by defining a set of semantic rules for performing the linking process. However, the geospatial data has some specific geospatial relationships, which is significant for linking but cannot be solved by the Semantic Web techniques directly. We take advantage of such unique features about geospatial data to implement the linking process. In addition, the previous work will meet a complicated problem when the geospatial data sources are in different languages. In contrast, our proposed linking algorithms are endowed with translation function, which can save the translating cost among all the geospatial sources with different languages. Finally, the geospatial data is integrated by eliminating data redundancy and combining the complementary properties from the linked records. We mainly adopt four kinds of geospatial data sources, namely, OpenStreetMap(OSM), Wikmapia, USGS and EPA, to evaluate the performance of the proposed approach. The experimental results illustrate that the proposed linking method can get high performance in generating the matched candidate record pairs in terms of Reduction Ratio(RR), Pairs Completeness(PC), Pairs Quality(PQ) and F-score. The integrating results denote that each data source can get much Complementary Completeness(CC) and Increased Completeness(IC).

Understanding distributed data – a semantic web approach for data based analysis of NDT data in civil engineering

2020 ◽  
Author(s):  
Christoph Völker ◽  
Benjamin Moreno-Torres ◽  
Sabine Kruschwitz
Keyword(s):  
Semantic Web ◽  
Intelligent Systems ◽  
Civil Engineering ◽  
Measurement Data ◽  
Data Access ◽  
Information Modeling ◽  
Data Sources ◽  
Data Sets ◽  
Extensive Literature ◽  
Distributed Data

<p>In the field of non-destructive testing (NDT) in civil engineering, a large number of measurement data are collected. Although they serve as a basis for scientific analyses, there is still no uniform representation of the data. An analysis of various distributed data sets across different test objects is therefore only possible with high manual effort.</p><p>We present a system architecture for an integrated data management of distributed data sets based on Semantic Web technologies. The approach is essentially based on a mathematical model - the so-called ontology - which represents the knowledge of our domain NDT. The ontology developed by us is linked to data sources and thus describes the semantic meaning of the data. Furthermore, the ontology acts as a central concept for database access. Non-domain data sources can be easily integrated by linking them to the NDT construction ontology and are directly available for generic use in the sense of digitization. Based on an extensive literature research, we outline the possibilities that this offers for NDT in civil engineering, such as computer-aided sorting, analysis, recognition and explanation of relationships (explainable AI) for several million measurement data.</p><p>The expected benefits of this approach of knowledge representation and data access for the NDT community are an expansion of knowledge through data exchange in research (interoperability), the scientific exploitation of large existing data sources with data-based methods (such as image recognition, measurement uncertainty calculations, factor analysis, material characterization) and finally a simplified exchange of NDT data with engineering models and thus with the construction industry.</p><p>Ontologies are already the core of numerous intelligent systems such as building information modeling or research databases. This contribution gives an overview of the range of tools we are currently creating to communicate with them.</p>

Semantic Web and Geospatial Unique Features Based Geospatial Data Integration

2016 ◽  
Vol 12 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Ying Zhang ◽  
Chaopeng Li ◽  
Na Chen ◽  
Shaowen Liu ◽  
Liming Du ◽  
...  
Keyword(s):  
Semantic Web ◽  
Data Integration ◽  
High Performance ◽  
Data Access ◽  
Heterogeneous Data ◽  
Geospatial Data ◽  
Data Sources ◽  
Modeling Process ◽  
Translation Function ◽  
Data Source

Since large amount of geospatial data are produced by various sources and stored in incompatible formats, geospatial data integration is difficult because of the shortage of semantics. Despite standardised data format and data access protocols, such as Web Feature Service (WFS), can enable end-users with access to heterogeneous data stored in different formats from various sources, it is still time-consuming and ineffective due to the lack of semantics. To solve this problem, a prototype to implement the geospatial data integration is proposed by addressing the following four problems, i.e., geospatial data retrieving, modeling, linking and integrating. First, we provide a uniform integration paradigm for users to retrieve geospatial data. Then, we align the retrieved geospatial data in the modeling process to eliminate heterogeneity with the help of Karma. Our main contribution focuses on addressing the third problem. Previous work has been done by defining a set of semantic rules for performing the linking process. However, the geospatial data has some specific geospatial relationships, which is significant for linking but cannot be solved by the Semantic Web techniques directly. We take advantage of such unique features about geospatial data to implement the linking process. In addition, the previous work will meet a complicated problem when the geospatial data sources are in different languages. In contrast, our proposed linking algorithms are endowed with translation function, which can save the translating cost among all the geospatial sources with different languages. Finally, the geospatial data is integrated by eliminating data redundancy and combining the complementary properties from the linked records. We mainly adopt four kinds of geospatial data sources, namely, OpenStreetMap(OSM), Wikmapia, USGS and EPA, to evaluate the performance of the proposed approach. The experimental results illustrate that the proposed linking method can get high performance in generating the matched candidate record pairs in terms of Reduction Ratio(RR), Pairs Completeness(PC), Pairs Quality(PQ) and F-score. The integrating results denote that each data source can get much Complementary Completeness(CC) and Increased Completeness(IC).

Awareness of symptomatic differences Covid-19, SARS, Swine Flu, Common cold among dental students

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 1026-1033
Author(s):  
Nivedha Valliammai Mahalingam ◽  
Abilasha R ◽  
Kavitha S
Keyword(s):  
Infectious Diseases ◽  
Undergraduate Students ◽  
Common Cold ◽  
Emerging Infectious Diseases ◽  
Dental Students ◽  
Swine Flu ◽  
Viral Diseases ◽  
Surveillance Systems ◽  
Cross Sectional ◽  
The Impact

Enormous successes have been obtained against the control of major epidemic diseases, such as SARS, MERS, Ebola, Swine Flu in the past. Dynamic interplay of biological, socio-cultural and ecological factors, together with novel aspects of human-animal interphase, pose additional challenges with respect to the emergence of infectious diseases. The important challenges faced in the control and prevention of emerging and re-emerging infectious diseases range from understanding the impact of factors that are necessary for the emergence, to development of strengthened surveillance systems that can mitigate human suffering and death. The aim of the current study is to assess the awareness of symptomatic differences between viral diseases like COVID-19, SARS, Swine flu and common cold among dental students that support the prevention of emergence or re-emergence. Cross-sectional type of study conducted among the undergraduate students comprising 100 Subjects. A questionnaire comprising 15 questions in total were framed, and responses were collected in Google forms in SPSS Software statistical analysis. The study has concluded that dental students have an awareness of the symptomatic differences between infectious viral disease. The study concluded that the awareness of symptomatic differences between viral diseases like COVID-19, SARS, Swine flu, Common cold is good among the dental students who would pave the way for early diagnosis and avoid spreading of such diseases. A further awareness can be created by regular webinars, seminars and brainstorming sessions among these healthcare professionals.

Taking Aim at Attack Advertising

2019 ◽  
Author(s):  
Kim Fridkin ◽  
Patrick Kenney
Keyword(s):  
Content Analysis ◽  
Focus Groups ◽  
Survey Data ◽  
Data Sources ◽  
Empirical Validation ◽  
Negative Campaigning ◽  
Senate Elections ◽  
Campaign Messages ◽  
Negative Messages ◽  
The Impact

This book develops and tests the “tolerance and tactics theory of negativity.” The theory argues that citizens differ in their tolerance of negative campaigning. Also, candidates vary in the tactics used to attack their opponents, with negative messages varying in their relevance to voters and in the civility of their tone. The interplay between citizens’ tolerance of negativity and candidates’ negative messages helps clarify when negative campaigning will influence citizens’ evaluations of candidates and their likelihood of voting. A diverse set of data sources was collected from U.S. Senate elections (e.g., survey data, experiments, content analysis, focus groups) across several years to test the theory. The tolerance and tactics theory of negativity receives strong empirical validation. First, people differ systematically in their tolerance for negativity, and their tolerance changes over the course of the campaign. Second, people’s levels of tolerance consistently and powerfully influence how they assess negative messages. Third, the relevance and civility of negative messages consistently influence citizens’ assessments of candidates competing for office. That is, negative messages focusing on relevant topics and utilizing an uncivil tone produce significant changes in people’s impressions of the candidates. Furthermore, people’s tolerance of negativity influences their susceptibility to negative campaigning. Specifically, relevant and uncivil messages are most influential for people who are least tolerant of negative campaigning. The relevance and civility of campaign messages also alter people’s likelihood of voting, and the impact of negative messages on turnout is more consequential for people with less tolerance of negativity.

Malaria elimination in India requires additional surveillance mechanisms

2021 ◽  
Author(s):  
Manju Rahi ◽  
Payal Das ◽  
Amit Sharma
Keyword(s):  
Real Time ◽  
Malaria Elimination ◽  
Surveillance Data ◽  
Surveillance Systems ◽  
Malaria Surveillance ◽  
Aggregated Data ◽  
Malaria Burden ◽  
The Core ◽  
Reporting Systems ◽  
Geographic Regions

Abstract Malaria surveillance is weak in high malaria burden countries. Surveillance is considered as one of the core interventions for malaria elimination. Impressive reductions in malaria-associated morbidity and mortality have been achieved across the globe, but sustained efforts need to be bolstered up to achieve malaria elimination in endemic countries like India. Poor surveillance data become a hindrance in assessing the progress achieved towards malaria elimination and in channelizing focused interventions to the hotspots. A major obstacle in strengthening India’s reporting systems is that the surveillance data are captured in a fragmented manner by multiple players, in silos, and is distributed across geographic regions. In addition, the data are not reported in near real-time. Furthermore, multiplicity of malaria data resources limits interoperability between them. Here, we deliberate on the acute need of updating India’s surveillance systems from the use of aggregated data to near real-time case-based surveillance. This will help in identifying the drivers of malaria transmission in any locale and therefore will facilitate formulation of appropriate interventional responses rapidly.

scholarly journals Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1432
Author(s):  
Xwégnon Ghislain Agoua ◽  
Robin Girard ◽  
Georges Kariniotakis
Keyword(s):  
Satellite Images ◽  
Forecast Accuracy ◽  
Absolute Error ◽  
Data Sources ◽  
Test Case ◽  
Temporal Data ◽  
Multiple Sources ◽  
Forecasting Error ◽  
Spatio Temporal ◽  
The Impact

The efficient integration of photovoltaic (PV) production in energy systems is conditioned by the capacity to anticipate its variability, that is, the capacity to provide accurate forecasts. From the classical forecasting methods in the state of the art dealing with a single power plant, the focus has moved in recent years to spatio-temporal approaches, where geographically dispersed data are used as input to improve forecasts of a site for the horizons up to 6 h ahead. These spatio-temporal approaches provide different performances according to the data sources available but the question of the impact of each source on the actual forecasting performance is still not evaluated. In this paper, we propose a flexible spatio-temporal model to generate PV production forecasts for horizons up to 6 h ahead and we use this model to evaluate the effect of different spatial and temporal data sources on the accuracy of the forecasts. The sources considered are measurements from neighboring PV plants, local meteorological stations, Numerical Weather Predictions, and satellite images. The evaluation of the performance is carried out using a real-world test case featuring a high number of 136 PV plants. The forecasting error has been evaluated for each data source using the Mean Absolute Error and Root Mean Square Error. The results show that neighboring PV plants help to achieve around 10% reduction in forecasting error for the first three hours, followed by satellite images which help to gain an additional 3% all over the horizons up to 6 h ahead. The NWP data show no improvement for horizons up to 6 h but is essential for greater horizons.

scholarly journals Towards an integrated animal health surveillance system in Tanzania: making better use of existing and potential data sources for early warning surveillance

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Janeth George ◽  
Barbara Häsler ◽  
Erick Komba ◽  
Calvin Sindato ◽  
Mark Rweyemamu ◽  
...  
Keyword(s):  
Early Warning ◽  
Surveillance System ◽  
Animal Health ◽  
Health Surveillance ◽  
Surveillance Data ◽  
Data Sources ◽  
Surveillance Systems ◽  
Cross Sectional Survey ◽  
Traceability System ◽  
Spatial Coverage

Abstract Background Effective animal health surveillance systems require reliable, high-quality, and timely data for decision making. In Tanzania, the animal health surveillance system has been relying on a few data sources, which suffer from delays in reporting, underreporting, and high cost of data collection and transmission. The integration of data from multiple sources can enhance early detection and response to animal diseases and facilitate the early control of outbreaks. This study aimed to identify and assess existing and potential data sources for the animal health surveillance system in Tanzania and how they can be better used for early warning surveillance. The study used a mixed-method design to identify and assess data sources. Data were collected through document reviews, internet search, cross-sectional survey, key informant interviews, site visits, and non-participant observation. The assessment was done using pre-defined criteria. Results A total of 13 data sources were identified and assessed. Most surveillance data came from livestock farmers, slaughter facilities, and livestock markets; while animal dip sites were the least used sources. Commercial farms and veterinary shops, electronic surveillance tools like AfyaData and Event Mobile Application (EMA-i) and information systems such as the Tanzania National Livestock Identification and Traceability System (TANLITS) and Agricultural Routine Data System (ARDS) show potential to generate relevant data for the national animal health surveillance system. The common variables found across most sources were: the name of the place (12/13), animal type/species (12/13), syndromes (10/13) and number of affected animals (8/13). The majority of the sources had good surveillance data contents and were accessible with medium to maximum spatial coverage. However, there was significant variation in terms of data frequency, accuracy and cost. There were limited integration and coordination of data flow from the identified sources with minimum to non-existing automated data entry and transmission. Conclusion The study demonstrated how the available data sources have great potential for early warning surveillance in Tanzania. Both existing and potential data sources had complementary strengths and weaknesses; a multi-source surveillance system would be best placed to harness these different strengths.

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