controlled vocabularies & ontologies - dss -
DESCRIPTION
Controlled vocabularies & Ontologies - DSS -. MDSS. Malaria Decision Support System. Integration of a number of data sets that will allow for informed choices, decisions and policy. Malaria surveillance in southern Africa. NOW – Open Souce -integrated. Early 2000 – SQL - integrated. - PowerPoint PPT PresentationTRANSCRIPT
Controlled vocabularies &
Ontologies- DSS -
Malaria Decision Support System
Integration of a number of data sets that will allow for informed choices, decisions and policy.
MDSS
SWAZILANDSWAZILANDSWAZILANDSWAZILANDSWAZILANDSWAZILANDSWAZILANDSWAZILANDSWAZILAND
0
0
kilometers
15 30
MOZAMBIQUEMOZAMBIQUEMOZAMBIQUEMOZAMBIQUEMOZAMBIQUEMOZAMBIQUEMOZAMBIQUEMOZAMBIQUEMOZAMBIQUE
EmpangeniEmpangeniEmpangeniEmpangeniEmpangeniEmpangeniEmpangeniEmpangeniEmpangeni
Richards BayRichards BayRichards BayRichards BayRichards BayRichards BayRichards BayRichards BayRichards Bay
Kosi BayKosi BayKosi BayKosi BayKosi BayKosi BayKosi BayKosi BayKosi Bay
IngwavumaIngwavumaIngwavumaIngwavumaIngwavumaIngwavumaIngwavumaIngwavumaIngwavuma
JoziniJoziniJoziniJoziniJoziniJoziniJoziniJoziniJozini
Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay
MkhuzeMkhuzeMkhuzeMkhuzeMkhuzeMkhuzeMkhuzeMkhuzeMkhuze
HluhluweHluhluweHluhluweHluhluweHluhluweHluhluweHluhluweHluhluweHluhluwe
HlabisaHlabisaHlabisaHlabisaHlabisaHlabisaHlabisaHlabisaHlabisa
St LuciaSt LuciaSt LuciaSt LuciaSt LuciaSt LuciaSt LuciaSt LuciaSt Lucia
Malaria IncidencePer 1000 Population
50 to 15025 to 500.01 to 250 to 0.01
SWAZILAND
0
0 15 30
kilometers
MOZAMBIQUE
Kosi BayKosi BayKosi BayKosi BayKosi BayKosi BayKosi BayKosi BayKosi Bay
IngwavumaIngwavumaIngwavumaIngwavumaIngwavumaIngwavumaIngwavumaIngwavumaIngwavuma
JoziniJoziniJoziniJoziniJoziniJoziniJoziniJoziniJozini
Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay
MkhuzeMkhuzeMkhuzeMkhuzeMkhuzeMkhuzeMkhuzeMkhuzeMkhuze
HluhluweHluhluweHluhluweHluhluweHluhluweHluhluweHluhluweHluhluweHluhluwe
HlabisaHlabisaHlabisaHlabisaHlabisaHlabisaHlabisaHlabisaHlabisa
St LuciaSt LuciaSt LuciaSt LuciaSt LuciaSt LuciaSt LuciaSt LuciaSt Lucia
Richards BayRichards BayRichards BayRichards BayRichards BayRichards BayRichards BayRichards BayRichards Bay
EmpangeniEmpangeniEmpangeniEmpangeniEmpangeniEmpangeniEmpangeniEmpangeniEmpangeni
Malaria Incidenceper 1000 population
350 to 600150 to 35050 to 15025 to 505 to 250.01 to 5
Nature Reserves
Water Bodies
Main Tow ns
Magisterial Districts
Sub-District Areas
Malaria incidence per 1000 people: July 1999 to June 2000 Malaria incidence per 1000 people: July 2003 to June 2004
Nature Reserves
Water Bodies
Main Tow ns
Magisterial Districts
Sub-District Areas
W E E K 2 W E E K 8W E E K 6W E E K 4
W E E K 1 0 W E E K 1 6W E E K 1 4W E E K 1 2
Spraying Progress During Round 301 October 2003 to 16 J anuary 2004
80 to 10060 to 8040 to 6020 to 40
0 to 20Zone 2A (Boane)
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
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
MA
LA
RIA
DE
CIS
ION
SU
PP
OR
T S
YS
TE
M
Health Information System
malariapatient / case
data
Entomology Surveillance System
mosquitopopulation
data
Indicator Survey
malariaprevalence
surveys
Spatial Data
GIS data
Intervention Monitoring System
malariacontrol
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
MODULED
EN
GU
E D
EC
ISIO
N S
UP
PO
RT
SY
ST
EM
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
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 DENGUEMIRO GAZETTEER
Relationships
Definitions
Terms
Complexity
CV ONTOLOGY
IDO
Vector surveillance ontology Vector 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
De
velo
pmen
tU
se
Implementation
Process
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 definitions3) 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!
IR Base
Insecticide resistant components feeds IR Base.
IR base a global database of insecticide resistance.
Entomology database components. Operational.
Entomology Database
InsecticideResistance
SpeciesDensity
SporozoiteRate
MDSS Malaria Control Programme
Linkage of systemsData sharing
InsecticideResistance
IR Base MERG WARN Malaria Atlas
MARA
MDSS
ITEGRATION OF DATA
Malaria Control Programme
Linkage of systemsGlobal databases
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