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Controlled vocabularies & Ontologies - DSS -. Malaria Decision Support System Integration of a number of data sets that will allow for informed choices,

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Presentation on theme: "Controlled vocabularies & Ontologies - DSS -. Malaria Decision Support System Integration of a number of data sets that will allow for informed choices,"— Presentation transcript:

1 Controlled vocabularies & Ontologies - DSS -

2 Malaria Decision Support System Integration of a number of data sets that will allow for informed choices, decisions and policy. MDSS

3 Malaria surveillance in southern Africa 50s Eradication - Paper Early 90s Dbase + EpiInfo - fragmented Mid 90s Ms. Access - fragmented Early 2000 – SQL - integrated NOW – Open Souce -integrated

4 What it is: PostgreSQL, is a highly scalable, SQL compliant, open source object-relational database management system. Why we decided on using it? Free Open source Easily spatially enabled Software What it is: PHP is a widely-used general-purpose scripting language that is especially suited for Web development and can be embedded into HTML. Why we decided on using it? Free Open source What it is: Is an extension to the PostgreSQL object-relational database system. Allows GIS (Geographic Information System) objects to be stored in the database. Why we decided on using it? Free Open source

5 MALARIA DECISION SUPPORT SYSTEM Health Information System malaria patient / case data Entomology Surveillance System mosquito population data Indicator Survey malaria prevalence surveys Spatial Data GIS data Intervention Monitoring System malaria control interventions SYSTEM Passive surveillance Active Surveillance Species identification and infectivity Insecticide Resistance Parasitemia & Anaemia Household Indicators Specific spatial data Backdrop spatial data Insecticide Treated Bed-nets Indoor Residual Spraying: Other interventions MODULE DENGUE DECISION SUPPORT SYSTEM SPATIAL BACKBONE EDUCATION VECTOR CONTROL VECTOR SURVEILLANCE CLINICAL DENGUE VIRUS SURVEILLANCE DISEASE SURVEILLANCE (passive or active) CATEGORYTYPE - Geographical boundaries - Location of hospitals, health clinics, schools, cemeteries etc - Socioeconomic characteristics - Environmental factors (climate, elevation, vegetation) Knowledge, Attitude and Practice among population Immatures: Mechanical source reduction Vector presence & abundance (larval, pupal, adult indices) Insecticide resistance (larvae, adults) Dengue virus in vector populations Dengue cases Fever of Unknown Origin Symptomology, Onset date, Diagnostic tests, Serotype, Outcome etc Dengue virus in human population Immatures: Biological control Immatures: Chemical control Adults: Chemical spray control Adults: Chemical ITM-based control

6 residual indoor spraying thermal fog space spraying outdoor cold fogs romanomermis iyengari rectangular net long lasting nets CDC light trap self supporting net wedge shaped net circular net growth regulator pathogens protista entomopathogenic nematodes romanomermis culicivorax MALARIA DENGUE MIRO GAZETTEER Relationships Definitions Terms Complexity CV ONTOLOGY IDO

7 Vector surveillance ontologyVector management ontology. X Conceptualize Indentify terms Add definitions Add relationships Separate CV terms Formalize ontology Incorporate into database schema Annotate data Analyze data Interpret Decision making Disseminate Process X Development Use Implementation Process

8 MIRO, available for downloading at:http://obo.cvs.sourceforge.net/*checkout*/obo/obo/ontology/phenotype/mosquito_insecticide_resistance.obo Participants will be able to: 1)propose new terms 2) propose modifications to existing terms and/or their definitions 3)comment on structures and undefined relationships. No restrictions will apply to participation and all contributions will be validated. CV web assistant Planning and constructing an ontology is a process that requires participation of and consensus among the expert community from the start!

9 IR Base Insecticide resistant components feeds IR Base. IR base a global database of insecticide resistance. Entomology database components. Operational. Entomology Database Insecticide Resistance Species Density Sporozoite Rate MDSS Malaria Control Programme Linkage of systems Data sharing Insecticide Resistance

10 IR Base MERGWARN Malaria Atlas MARA MDSS ITEGRATION OF DATA Malaria Control Programme Linkage of systems Global databases

11 Challenges & Opportunities Time Financial resources Priorities/commitment Advocacy Community Participation & Contributions Roles & Responsibilities Ownership Sustainability Provide opportunity to contribute Initiate collaborative efforts Provide standardization- annotation Assist software development process Provide better quality data Provide improved comparison of data Support contributions to global warehouses + interoperability The use supports better decisions Bigger picture – indirectly save lives


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